Search

This paper investigates the integration of blockchain technology into core systems within institutions of higher education, utilizing the National Institute of Standards and Technology's (NIST) Cybersecurity Framework as a guiding framework. It supplies definitions of key terminology including blockchain, consensus mechanisms, decentralized identity, and smart contracts, and examines the application of secure blockchain across various educational functions such as enrollment management, degree auditing, and award processing. Each facet of the NIST Framework is utilized to explore the integration of blockchain technology and address persistent security concerns. The paper contributes to the literature by defining blockchain technology applications and opportunities within the education sector.

This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.

Purpose: Inclusive growth in the education sector is still a major challenge in some countries because of limited access to technologies and internet connectivity, among other reasons. However, as a technology, augmented reality (AR) is expected to be widely used in the field of education in the future. The main purpose of this paper is to explore the use of AR for inclusive growth in education as well as identify the challenges, particularly in countries like India and Tanzania. Design/methodology/approach: This paper applies a systematic literature review by analysing and synthesising relevant documents, mainly journal articles, books and conference papers. Descriptive statistics and cross-tabulation were used for the analysis. Content analysis was used to evaluate the contents of the reviewed literature. Findings: For the use of AR, it is important to have adequate digital infrastructure, access to universal internet or broadband facilities and the digital empowerment of citizens. Major challenges to inclusive growth in education include the lack of trained teacher educators and students' preference for practical or project-based curriculum. Practical implications: Practitioners in both countries may consider the use of AR for inclusive growth in education. Originality/value: This paper specifically examines the use of AR in higher education and the related challenges based on a review of two countries, namely India and Tanzania.

Purpose: This paper evaluated the effectiveness of blended learning of business courses in higher learning institutions (HEIs) in developing economies. Design/methodology/approach: A survey, involving 215 learners, was used to collect data. A stratified sampling technique was used in this study. The data were analyzed using the PROCESS macro in SPSS. Findings: In the blended learning approach, student attitudes, social presence, IT infrastructure and flexible learning are all favorable predictors of learner satisfaction. The impact of blended learning on learner satisfaction is further mediated by IT infrastructure, social presence and learner attitude. Practical implications: HEIs need to invest in planning and resource mobilization in order to realize several benefits derived from the use of blended learning. For optimal learning outcomes, this should be combined with training on IT infrastructure usage for both facilitators and learners. In order to assist learners in developing competencies through consistent use, institutions should also invest in tailored blended learning technologies. In addition, emphasis should be placed on training all actors in order to better manage change. Originality/value: This paper presents and ranks several dimensions for blended learning success in low-budget universities. In addition, the study contributes to the understanding of intervening variables necessary for enhancing the potential of pedagogy in maximizing learner satisfaction.

Many studies have recently focused on the importance of the effective integration of Information and Communication Technology (ICT) tools in science education and the need for science teachers to receive adequate training and support to use them effectively. This paper aims to explore the Albanian science teachers' perceptions and use of ICTs in teaching processes. The study provides an interpretative analysis of the opinions of science teachers teaching in the middle and high schools of several Albanian regions expressed in an online survey. Overall, the results suggest that the use of ICT by science teachers in Albania is limited by a lack of proper infrastructure, limited access to technology, and training of teachers on integrating technology. However, there are indications that the adoption of ICT in science education may increase in the future, particularly after the boost from the COVID-19 pandemic emergency.

This paper introduces a Patron Counting and Analysis (PCA) system that leverages Wi-Fi-connection data to monitor space utilization and analyze visitor patterns in academic libraries. The PCA system offers real-time crowding information to the public and a comprehensive visitor analysis dashboard for library administrators. The system's development was driven by the need for occupancy restrictions during the pandemic, ensuring a spacious environment for library visitors as well as balancing between efficient utilization and adhering to social distancing regulations. Traditional methods of patron behavior performance and library spatial analysis, such as manual head counting or card-swiping systems, often incur additional costs for labor, hardware installation, or software subscription. The PCA system, however, utilizes existing Wi-Fi-connection data, providing a cost-effective solution to represent patron demographics and spatial usage. Limitations may arise when patrons do not carry Wi-Fi-enabled devices or during periods of low Wi-Fi service functionality. Implemented in Node.js and integrated with Python Flask framework and related libraries, the PCA system was piloted at the King Library in Miami University, successfully demonstrating a high validity compared to manually collected data. It filters out noise and redundancy, visualizes the occupancy index meter in real time, and generates statistical reports by linking user IDs with demographic information. The PCA system's reliability was validated through manually head counting data collected at the King Library in Miami University, establishing it as a reliable tool for library space management and patron analysis.

Intangible cultural heritage is an important part of Chinese excellent traditional culture, and college dance teaching is paid more attention by researchers of physical education and computer technology. In order to help the inheritance and development of non-legacy culture in college dance teaching, this paper analyzes the influencing factors of wireless communication network in college dance teaching, constructs an interactive platform for non-legacy digital dance teaching based on wireless communication network technology, and applies it in the dance teaching process of a university in Henan province to highlight the role of wireless communication technology in dance teaching. The results show that the interactive platform can effectively reduce teachers' physical energy consumption and has certain reliability and practicality. Through artificial intelligence algorithm, the traditional dance teaching method has been changed, and a new exploration of non-legacy dance teaching has been realized.

Higher education is becoming increasingly competitive and all educational institutions are concentrating on improving quality and changing traditional higher education teaching methods. New-type classroom instruction has embraced a unique advancement opportunity with the arrival of the fifth generation (5G) era. It is critical to develop a teaching assistance system that makes use of high-speed network methodology and new-type display methodology. For the innovation and reform of higher education, this article combines soft computing techniques, artificial intelligence (AI), and 5G networks. This paper outlines the exact processes and measures for incorporating "5G" technology into higher education. Finally, conduct a comparative experiment to see how good the system is at learning AI knowledge when compared to standard learning methods. The outcomes of the experiments are examined to show that employing this approach to gain AI knowledge is successful and improves students' enthusiasm in learning as well as their hands-on abilities.

The COVID-19 pandemic sparked an unprecedented expansion of educational technologies and digitisation of the university sector, and also amplified existing inequalities and crises. In this paper, we introduce the 'socio-technically just pedagogies framework' to systemically explore curriculum-making, student-staff partnerships, knowledge production, and networked capabilities in higher education. This conceptual innovation seeks to (re)articulate pedagogy across four aspects: (i) a commitment to curriculum-making as a form of everyday activism; (ii) a nurturing of student-staff coalitions to expand student-staff partnerships; (iii) development of generative spaces for transdisciplinary co-creation; and (iv) the deliberation of networked capabilities. This framework emerged from a partnership with students at an Australian university that sought to experiment with pedagogical practices and possibilities. Our coalition then responded to the framework to illicit collective insights about the curriculum-making phenomenon. The framework seeks to articulate curriculum-making initiatives that collectively enact socio-technically just pedagogies.

Qualitative research has been strongly affected by the COVID-19 pandemic, highlighting the possibilities that Voice over Internet Protocol (VoIP) technologies such as Skype, WhatsApp, and Zoom offer to qualitative scholars. Based on the experience of using such technologies to collect qualitative data for our PhD studies, we present how we dealt with the challenges of this interview mode. Precisely, we discuss problems related to rapport, technology, digital exclusion, and ethics frequently pointed out in the methodological literature on online interviews. Thereby we put forward strategies and techniques that helped us to 1) build a rapport, 2) manage technical difficulties, 3) reflect on risks of digital exclusion, and 4) comply with the ethical standards of our institution. In doing so, we draw on our qualitative data to support the arguments. The aim of this paper is, thus, to deepen the methodological debate on online interviews in social sciences.

Technology is advancing and metaverse is gaining popularity. The magic of metaverse is beyond our imagination. In simple terms, the metaverse refers to a virtual shared space that exists online, where people can interact, socialise, work, and play using digital avatars, just like they do in the real world. It is a combination of virtual reality, augmented reality, and the internet, all rolled into one immersive environment. Virtual reality (VR) immerses users in entirely computer-generated environments through headsets, while augmented reality (AR) overlays digital information onto the real world, enhancing the user's perception. This paper introduces new strategies to bring healthcare into the metaverse by providing solutions to the hurdles that have kept us from exploring this idea. These include reducing data transfer delays in the metaverse, making VR headsets more affordable, accurately predicting diseases by studying symptoms, and creating a platform for medical professionals to practice procedures on avatars before performing them on real patients. Metaverse has immense scope of revolutionising the healthcare and we are yet to unfold its complete usefulness.

With the rapid development of computer technology and network communication technology, embedded micro control system has been widely used. It is crucial in the fields of large-scale chemical equipment, mechanized production base, transportation and future robots. Combined with the current Internet of Things technology, the equipment needs to be kept on the network during operation, real-time data comparison, data feedback, data reception and other network operations to provide strong help for human society. Focusing on the network communication level, due to the improvement of hardware and software technology, the basic link work has become the foundation part of realizing network communication, and the real problem is often the information transmission. For example, for wireless channel estimation and optimization based on embedded system, it is necessary to eliminate the interference of irresistible factors as far as possible, overview the defects in algorithm level and logic analysis level since the development of network communication technology, discuss the impact of network communication module of embedded monitoring system in practical application as far as possible, and make the error infinite to zero under the premise of unchanged hardware conditions. Based on the above conditions, the lightweight channel estimation method and the optimization under the condition of sparse matrix are proposed. This paper studies the network communication of embedded devices, especially the channel link in network communication, and focuses on the channel estimation algorithm. This paper proposes the advantages of LS and MMSE algorithms. According to the experimental results, although in terms of speed indicators, it still maintains better than the traditional algorithm in terms of average throughput, transmission delay, response time, transmission rate and so on in a static environment. [ABSTRACT FROM AUTHOR]

How do communication costs affect the creation of scientific output? This study examines changes in scientific output and citation patterns following an institution's connection to the National Science Foundation Network (NSFNET), an early version of the Internet. Established in 1985 to connect five NSF-sponsored supercomputers, the NSFNET national internet backbone quickly expanded to universities across the United States by linking existing and newly formed, wide-area regional computer networks. I estimate the effect of connection to the national internet backbone on citations per paper by exploiting plausibly exogenous variation in the connection times of the regional NSFNET networks. Following connection to the national NSFNET, average citations per paper increase by over 10% relative to the pre-connection mean. Subgroup analyses reveal that the net effect was driven largely by middle- and top-tier institutions. Finally, I show that NSFNET connection led to a decline in interdisciplinary citations and an increase in within-field citations. [ABSTRACT FROM AUTHOR]

A botnet attack is a major cybersecurity threat that involves coordinated control of a network of infected computers, enabling large-scale distributed denial of service (DDoS) attacks, malware spreading, and other cybercrime activities. Proactive security measures and advanced threat intelligence systems are essential to detect and mitigate these assaults. This paper proposes the Harris Hawks Optimization (HHO) algorithm, which employs exploration and exploitation techniques to find optimal solutions for analyzing botnet attack pathways. The proposed approach involves HHO as a feature selector for extracting features from anomalous network traffic. The algorithm’s impact on botnet IP positioning performance is analyzed, considering different optimization modes and control center accuracy. The paper is organized into sections covering attack path establishment and analysis, system testing and verification, and a central leadership entity controls it [1]. Botnets are created based on the use of malicious software packages to infect important and sensitive devices in the network, thus making servers, computers, and Internet of Things devices vulnerable [2]. To detect these attacks and limit their impact requires many proactive security measures such as strong network security settings, regular software upgrades, etc. [3]. HHO is a powerful method that has the potential to solve many functional optimization problems and provides a suitable environment for engineering applications, as it mimics the exploration and exploitation phases during the foraging process of Harris Hawks [4]. A model based on HHO algorithm is proposed in this paper that has the ability to track and analyze bot attack paths by extracting a set of features during abnormal network traffic. The results were analyzed and their impact on the performance of robot networks was discussed, based on the use of different experimental results. After configuring the network topology and determining the attack path based on HHO, the performance of the algorithm and its effectiveness in preventing IP addresses from being spoofed are verified. The results showed convergence in being able to correct attack paths and effective performance in repelling the interference of fake IP addresses. [ABSTRACT FROM AUTHOR]

In recent years, a surge in malicious network incidents and instances of network information theft has taken place, with malware identified as the primary culprit. The primary objective of malware is to disrupt the normal functioning of computers and networks, all the while surreptitiously gathering users' private and sensitive information. The formidable concealment and latency capabilities of malware pose significant challenges to its detection. In light of the operational characteristics of malware, this paper conducts an initial analysis of prevailing malware detection schemes. Subsequently, it extracts fuzzy features based on the distinct characteristics of malware traffic. The approach then integrates traffic detection techniques with Type II fuzzy recognition theory to effectively monitor malware-related traffic. Finally, the paper classifies the identified malware instances according to fuzzy association rules. Experimental results showcase that the proposed method achieves a detection accuracy exceeding 90%, with a remarkably low false alarm rate of approximately 5%. This method adeptly addresses the challenges associated with malware detection, thereby making a meaningful contribution to enhancing our country's cybersecurity. [ABSTRACT FROM AUTHOR]

The significance of detecting network traffic anomalies in cybersecurity cannot be overstated, especially given the increasing frequency and complexity of computer network attacks. As new Internet-related technologies emerge, so do more intricate attacks. One particularly daunting challenge is represented by dictionary-based bruteforce attacks, which require effective real- time detection and mitigation methods. In this paper, we investigate Secure Shell or Secure Socket Shell, is a network protocol that gives users, particularly system administrators, a secure way to access a computer over an unsecured network(SSH)and File Transfer Protocol is a standard network protocol used for the transfer of files from one host to another over a TCP-based network, such as the Internet (FTP) brute-force attack detection by using Our research focuses on using the machine learning approach to detect SSH and FTP brute-force attacks. A reasonably thorough investigation of machine learners' efficacy in identifying brute force assaults on SSH and FTP is made possible by employing several classifiers. Bruteforce assaults are a popular and risky method of obtaining usernames and passwords. Applying ethical hacking is an excellent technique to examine the effects of a brute-force assault. This article discusses many defense strategies and approaches to using bruteforce assaults. The pros and cons of several defense strategies are enumerated, along with information on which kind of assault is easiest to identify. we made use of machine learning classifiers: Naive Bayes, Random Forest, Logistic Regression, we determined that the Random Forest algorithm achieved the highest level with an accuracy the contribution lies in demonstrating the feasibility of training and evaluating basic Random Forest models with two independent variables to classify CSE-CIC-IDS2018 dataset. [ABSTRACT FROM AUTHOR]

Copyright of Military Technical Courier / Vojnotehnicki Glasnik is the property of Military Technical Courier / Vojnotehnicki Glasnik and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Retrial queues are used extensively to model many practical problems in service systems, call centers, data centers, and computer network systems. The average waiting time is the main observable characteristic of the retrial queues. Long queues may cause negative impacts such as waste of manpower and unnecessary crowding leading to suffocation, and can even cause trouble for customers and institutions. Applying control chart technology can help managers analyze customers' waiting times to improve the effective performance of service and attention. This paper pioneers the developing and detailed study of a waiting time control chart for a retrial queue with general service times. Two waiting time control charts, the Shewhart control chart, and a control chart using the weighted variance method are constructed in this paper. We present three cases for the Shewhart control chart in which the service time obeys special distributions, such as exponential, Erlang, and hyper-exponential distributions. The case of an exponentially distributed service time is also presented for the control chart using the weighted variance method. Based on the numerical simulations conducted herein, managers can better monitor and analyze the customers' waiting times for their service systems and take preventive measures. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDS) are an important tool for securing computer networks from various types of cyberattacks. The increasing complexity of network attacks demands more sophisticated approaches to intrusion detection. This paper presents an innovative method for IDS that involves combining Z-Score outlier detection, KMeans clustering, and Random Forest classification techniques. We tested our methodology using the CICIDS2017 dataset, which is a standardization dataset for intrusion detection that is frequently utilized. Our proposed approach first uses Z-Score outlier detection to identify abnormal traffic flows in the network. Next, KMeans clustering is used to group the traffic flows into different clusters based on their similarity. Finally, Random Forest classification is used to classify each traffic flow into normal or abnormal categories. Based on our experimental results, our approach for intrusion detection shows superior performance compared to several other state-of-the-art methods in terms of accuracy and precision. Our proposed method achieved an accuracy rate of 95.75% and a precision of 95.76%, surpassing the performance of KNN, SVM, and decision trees approaches. In conclusion, the proposed Z-K-R approach offers a promising solution for IDS by leveraging the strengths of Z-Score outlier detection, KMeans clustering, and Random Forest classification techniques. This strategy has the potential to increase the efficiency of IDS and boost network security in applications that take place in the real world. [ABSTRACT FROM AUTHOR]

Communication networks are crucial components of the underlying digital infrastructure in any smart city setup. The increasing usage of computer networks brings additional cyber security concerns, and every organization has to implement preventive measures to protect valuable data and business processes. Due to the inherent distributed nature of the city infrastructures as well as the critical nature of its resources and data, any solution to the attack detection calls for distributed, efficient and privacy preserving solutions. In this paper, we extend the evaluation of our federated learning framework for network attacks detection and classification based on random forest. Previously the framework was evaluated only for attack detection using four well-known intrusion detection datasets (KDD, NSL-KDD, UNSW-NB15, and CIC-IDS-2017). In this paper, we extend the evaluation for attack classification. We also evaluate how adding differential privacy into random forest, as an additional protective mechanism, affects the framework performances. The results show that the framework outperforms the average performance of independent random forests on clients for both attack detection and classification. Adding differential privacy penalizes the performance of random forest, as expected, but the use of the proposed framework still brings benefits in comparison to the use of independent local models. The code used in this paper is publicly available, to enable transparency and facilitate reproducibility within the research community. [ABSTRACT FROM AUTHOR]

As cyberattacks become increasingly sophisticated and frequent, it is crucial to develop robust cybersecurity measures that can withstand adversarial attacks. Adversarial simulation is an effective technique for evaluating the security of systems against various types of cyber threats. However, traditional adversarial simulation methods may not capture the complexity and unpredictability of real-world cyberattacks. In this paper, we propose the improved deep reinforcement learning (DRL) algorithm to enhance adversarial attack simulation for cybersecurity with real-world scenarios from MITRE-ATT&CK. We first describe the challenges of traditional adversarial simulation and the potential benefits of using DRL. We then present an improved DRL-based simulation framework that can realistically simulate complex and dynamic cyberattacks. We evaluate the proposed DRL framework using a cyberattack scenario and demonstrate its effectiveness by comparing it with existing DRL algorithms. Overall, our results suggest that DRL has significant potential for enhancing adversarial simulation for cybersecurity in real-world environments. This paper contributes to developing more robust and effective cybersecurity measures that can adapt to the evolving threat landscape of the digital world. [ABSTRACT FROM AUTHOR]

This paper investigates an infinite buffer fluid queueing model driven by a state-dependent birth-death process prone to catastrophes. We use the Laplace-Stieltjes transform and continued fraction approaches to establish precise expression for the joint probability of the content of the buffer and the number of customers in an M/M/1 queueing model. The importance of the proposed system is that, in numerous practical situation, the service facility has defence mechanisms in place to deal with long waits. Under the strain of a significant backlog of work, the servers may improve their service rate. Therefore, considering the state-dependent character of queueing systems is of relevance. For example, congestion control technologies prevent long queues forming in computer and communication systems by adjusting packet transmission speeds based on the length of the queue (of packets) at the source or destination. Theoretical results are supported by numerical illustrations. [ABSTRACT FROM AUTHOR]

This paper presents an innovative use case of virtual reality (VR) for career development and exploration, within the context of the CareProfSys recommendation system for professions. The recommender users receive recommendations not only in textual format but as WebVR gamified scenarios as well, having thus the possibility to try activities specific to the suggested professions and decide whether they are suitable for them or not. This paper describes, from a functional and technical point of view, scenarios for six different jobs: computer network specialists, civil engineers, web and multimedia developers, chemical engineers, project managers, and university professors. Extended experiments were performed, using an internal protocol, with 47 students enrolled in engineering studies. The results of the experiments were measured with the aid of four instruments: two questionnaires, one unstructured interview, and the VR simulation performance recording module. Positive results were obtained: the users admitted that such a tool was useful when choosing one's career and that it was entertaining. Most of the students considered the VR scenarios as learning or testing experiences, too. Thus, we claim that a VR form of providing job recommendations is more appealing to young people and brings value to career development initiatives. [ABSTRACT FROM AUTHOR]

The object of this study is the process of recognizing intrusions in computer networks. Network intrusion detection systems (NIDS) have become an important area of research as they are used to protect computer systems from hacker attacks. Deep learning is becoming increasingly popular for detecting and classifying malicious network traffic, including for building NIDS. In this paper, we propose a network intrusion detection model CNN-BiGRU-Attention based on a time-based approach to deep learning using the attention mechanism. The main goal of the study is to build an effective combined deep learning model that can detect various network cyber threats. A 1D convolutional neural network is implemented to extract high-level representations of intrusion information features. A bidirectional gated recurrent unit (BiGRU) with an attention mechanism for traffic data classification has been designed. The attention mechanism plays a key role in the constructed model as it allows the system to focus only on important aspects of network traffic and allows the model to adapt to new types of threats. The results of the study show that using a combination of CNN and BiGRU with the attention mechanism speeds up and improves the process of classifying network attacks. On the NSL-KDD and UNSW-NB15 training datasets, the model shows an accuracy of 99.81 % and 97.80 %. On the NSL-KDD and UNSW-NB15 test datasets, the model demonstrates 82.16 % and 97.72 % accuracy. The proposed NIDS model will be considered for implementation in a real-time corporate network security system. In general, the results of the study provide a new perspective on improving the performance of NIDS and are quite relevant in terms of using attention mechanisms to classify network traffic. [ABSTRACT FROM AUTHOR]

A novel approach is presented in this paper to address the limitations of virtual machine technology, active kernel technology, heuristic killing technology, and behaviour killing technology in computer network virus defence. The proposed method provides data mining technology, specifically Object-Oriented Analysis (OOA) mining, to detect deformed and unknown viruses by analyzing the sequence of Win API calls in PE files. Experimental results showcase the Data Mining-based Antivirus (DMAV) system's superiority over existing virus scanning software in multiple aspects: higher accuracy in deformed virus detection, enhanced active defence capabilities against unknown viruses (with a recognition rate of 92%), improved efficiency, and a reduced false alarm rate for non-virus file detection. Furthermore, the paper introduces an OOA rule generator to optimize feature extraction, enhancing the system's intelligence and robustness. This research provides a promising solution to support virus detection accuracy, active defence mechanisms, and overall efficiency while minimizing false positives in virus scanning, thus contributing significantly to the advancement of computer network security. [ABSTRACT FROM AUTHOR]

With the advancement of Industry 4.0, Time-Sensitive Networking (TSN) has become essential for ensuring prompt and reliable data transmission. As an augmentation of Ethernet, TSN aims to supply services capable of low latency, minimal jitter, and low packet loss for urgent data in decentralized, user-oriented networks. Efficient detection techniques are integral to TSN for swiftly determining the practicability of network configurations, as existing schedulability analysis proves insufficient. This paper delves into the potential of backpropagation neural networks (BPNN) in schedulability analysis efficiency. We optimize BPNN using spearman correlation feature selection combined with a voting ensemble method and Particle Swarm Optimization (PSO), forming two models: Spearman-Vote-BPNN and Spearman-PSO-BPNN. Testing on 5,000 network configurations in computer simulations, both models demonstrated high generalization accuracy, around 97.4%. Spearman-Vote-BPNN achieved the fastest training speed at 0.63 s and an accuracy of 98.2%. Meanwhile, Spearman-PSO-BPNN showed the highest accuracy (98.5%) with the quickest detection speed (5.6 ms). The outcomes of this research significantly advance the efficacy and precision of TSN network configuration detection and establish a formidable groundwork for future scholarly pursuits in this area. [ABSTRACT FROM AUTHOR]

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. [ABSTRACT FROM AUTHOR]

The chordal ring (CR) graphs are a well-known family of graphs used to model some interconnection networks for computer systems in which all nodes are in a cycle. Generalizing the CR graphs, in this paper, we introduce the families of chordal multi-ring (CMR), chordal ring mixed (CRM), and chordal multi-ring mixed (CMRM) graphs. In the case of mixed graphs, we can have edges (without direction) and arcs (with direction). The chordal ring and chordal ring mixed graphs are bipartite and 3-regular. They consist of a number r (for r ≥ 1 ) of (undirected or directed) cycles with some edges (the chords) joining them. In particular, for CMR, when r = 1 , that is, with only one undirected cycle, we obtain the known families of chordal ring graphs. Here, we used plane tessellations to represent our chordal multi-ring graphs. This allowed us to obtain their maximum number of vertices for every given diameter. Additionally, we computationally obtained their minimum diameter for any value of the number of vertices. Moreover, when seen as a lift graph (also called voltage graph) of a base graph on Abelian groups, we obtained closed formulas for the spectrum, that is, the eigenvalue multi-set of its adjacency matrix. [ABSTRACT FROM AUTHOR]

Traditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real‐time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this article proposes a vehicular edge computing optimization scheme based on the Lyapunov function and Deep Reinforcement Learning. In this solution, this article uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real‐time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi‐objective dynamic optimization problem. It solves the problem by applying the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing. [ABSTRACT FROM AUTHOR]

Nowadays, different types of computer networks such as Wireless sensor networks (WSNs), the Internet of things (IoT), and wireless body area networks (WBANs) transfer information, share resources, and process information. The IoT is a novel network which interconnects various smart devices and can consist of heterogeneous components such as WSNs for monitoring and collecting information. Characterized by specific advantages, the IoT contains different types of nodes, each with few sensors to collect environmental information on agriculture, ecosystem, search and rescue, conflagrations, etc. Despite extensive applications and high flexibility in the modern world, the IoT faces specific challenges, the most important of which include routing, energy consumption and localization. Localization leads to other network challenges and thus can be considered the most important challenge in the IoT. Localization refers to a process aiming at determining the positions and locations of objects lacking global positioning system (GPS) and needing to use the information of network sensors and topology to estimate their own positions and locations. The distance vector hop (DV-Hop) algorithm is a range-free localization technique, in which the major challenge is that the number of hops between two nodes is multiplied by a number that is the same for all nodes leading to a significant reduction in the localization accuracy. In the method proposed in this paper, a network node with no GPS determines the hops from three anchor nodes with GPS. The location of smart objects can be then estimated according to distances from those anchor nodes. Thereafter, a few positions can be created nearby to mitigate the error. Then each position can be regarded as a member of the grasshopper optimization algorithm (GOA) to minimize the localization error. According to the results obtained from implementation of the proposed algorithm, it is characterized by a lower localization error than grasshopper optimization, butterfly optimization, firefly and swarm optimization algorithms. [ABSTRACT FROM AUTHOR]

Educational technology is important because it helps today's teachers to integrate new technologies and tools into their classrooms. This allows a student to flip the notion of a classroom on his/her head by choosing when and where to learn. Video lectures, recordings, and digital resources make it possible for students to learn at their own pace. The walls of the classrooms are no longer a barrier as technology enables new ways of learning, communicating, and working collaboratively. Technology has also begun to change the roles of teachers and learners. This paper discusses the systematic five-step teaching-learning process for effective engagement in the class. These five steps - The Instructional Process, Modular Design, and Course Delivery, Creating a Lively classroom, Employing technology and Assessment are proven processes, and, an effective implementation of the same yielded very good results in terms of enhancement of the skills and overall development of the students' learning perspectives. Giving varied assignments to different groups based on their interest, had a spike in exhibiting their potential. Assessment through rubrics increased the clarity for students to perform and present their work effectively. This was implemented for the computer networks course for CSE branch students and Biomedical Instrumentation and Digital Health course for Biotechnology branch. The impact and results were amazing. There was an overall development of their knowledge and skills. [ABSTRACT FROM AUTHOR]

In classical broadcast models, information is disseminated in synchronous rounds under the constant communication time model, wherein a node may only inform one of its neighbors in each time-unit—also known as the processor-bound model. These models assume either a coordinating leader or that each node has a set of coordinated actions optimized for each originator, which may require nodes to have sufficient storage, processing power, and the ability to determine the originator. This assumption is not always ideal, and a broadcast model based on the node's local knowledge can sometimes be more effective. Messy models address these issues by eliminating the need for a leader, knowledge of the starting time, and the identity of the originator, relying solely on local knowledge available to each node. This paper investigates the messy broadcast time and optimal scheme in a grid graph, a structure widely used in computer networking systems, particularly in parallel computing, due to its robustness, scalability, fault tolerance, and simplicity. The focus is on scenarios where the originator is located at one of the corner vertices, aiming to understand the efficiency and performance of messy models in such grid structures. [ABSTRACT FROM AUTHOR]

The Precision Time Protocol (PTP) plays a pivotal role in achieving precise frequency and time synchronization in computer networks. However, network delays and jitter in real systems introduce uncertainties that can compromise synchronization accuracy. Three clock skew estimators designed for the PTP scenario were obtained in our earlier work, complemented by closed-form approximations for the Mean Squared Error (MSE) under the generalized fractional Gaussian noise (gfGn) model, incorporating the Hurst exponent parameter (H) and the a parameter. These expressions offer crucial insights for network designers, aiding in the strategic selection and implementation of clock skew estimators. However, substantial computational resources are required to fit each expression to the gfGn model parameters (H and a) from the MSE perspective requirement. This paper introduces new closed-form estimates that approximate the MSE tailored to match gfGn scenarios that have a lower computational burden compared to the literature-known expressions and that are easily adaptable from the computational burden point of view to different pairs of H and a parameters. Thus, the system requires less substantial computational resources and might be more cost-effective. [ABSTRACT FROM AUTHOR]

This study describes improving network security by implementing and assessing an intrusion detection system (IDS) based on deep neural networks (DNNs). The paper investigates contemporary technical ways for enhancing intrusion detection performance, given the vital relevance of safeguarding computer networks against harmful activity. The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset, a popular benchmark for IDS research. The model performs well in both the training and validation stages, with 91.30% training accuracy and 94.38% validation accuracy. Thus, the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation. Furthermore, for both macro and micro averages across class 0 (normal) and class 1 (anomalous) data, the study evaluates the model using a variety of assessment measures, such as accuracy scores, precision, recall, and F1 scores. The macro-average recall is 0.9422, the macro-average precision is 0.9482, and the accuracy scores are 0.942. Furthermore, macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model's ability to precisely identify anomalies precisely. The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved by DNN-based intrusion detection systems, which can significantly improve network security. The study underscores the critical function of DNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field. Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge. [ABSTRACT FROM AUTHOR]

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats. [ABSTRACT FROM AUTHOR]

A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often camouflage their activity by utilizing common internet protocols, such as HTTP and IRC, making their detection challenging. This paper addresses this threat by proposing a method to identify botnets based on distinctive communication patterns between command and control servers and bots. Recognizable traits in botnet behavior, such as coordinated attacks, heartbeat signals, and periodic command distribution, are analyzed. Probabilistic models, specifically Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), are employed to learn and identify these activity patterns in network traffic data. This work utilizes publicly available datasets containing a combination of botnet, normal, and background traffic to train and test these models. The comparative analysis reveals that both HMMs and PHMMs are effective in detecting botnets, with PHMMs exhibiting superior accuracy in botnet detection compared to HMMs. [ABSTRACT FROM AUTHOR]

Graphs are used to solve many problems in mathematics and computer sciences. Many structures can be displayed with the help of graphs. For example, a directed graph can be used to show how websites are related to each other. Vague graph (VG) is one of the most important graphs in the fuzzy graph (FG)-theory, which can play a significant role in finding the most suitable places in construction and also finding the shortest path in computer networks. Connectivity indices are one of the most widely used topics in graph theory, which are used in other sciences, including computer science and chemistry. One of the most famous indices in the graph is the Wiener index, which belongs to the description of the molecular structure, which is used to design molecules with desirable properties. Therefore, in this paper, we introduce important topological indices such as Wiener index, Wiener absolute index, Randic index, Zegreb index, Harmonic index, and Average Wiener index on VGs and investigate their properties with several examples. Finally, an application of the Wiener index is given to find the most suitable place to build an university. [ABSTRACT FROM AUTHOR]

Graph coloring is a fundamental topic in graph theory that requires an assignment of labels (or colors) to vertices or edges subject to various constraints. We focus on the harmonious coloring of a graph, which is a proper vertex coloring such that for every two distinct colors i, j at most one pair of adjacent vertices are colored with i and j. This type of coloring is edge-distinguishing and has potential applications in transportation networks, computer networks, airway network systems. The results presented in this paper fall into two categories: in the first part of the paper we are concerned with the computational aspects of finding a minimum harmonious coloring and in the second part we determine the exact value of the harmonious chromatic number for some particular graphs and classes of graphs. More precisely, in the first part we show that finding a minimum harmonious coloring for arbitrary graphs is APX-hard and that the natural greedy algorithm is a p n)-approximation. In the second part, we determine the exact value of the harmonious chromatic number for all 3-regular planar graphs of diameter 3 and some cycle-related graphs. [ABSTRACT FROM AUTHOR]

The field of cybersecurity constantly evolves as attackers develop new methods and technologies. Defending against cyberattacks involves a combination of robust security measures, regular updates, user education, and the use of advanced technologies, such as intrusion detection systems and artificial intelligence, to find out the threats in realtime. IDS are designed to identify and address any unauthorized actions or potential security threats within a computer network or system. A hybrid intrusion detection system (IDS) combines many detection techniques and strategies from different IDS types into a single, coherent solution. Combining the benefits of each approach should result in more comprehensive and effective intrusion detection. This paper outlines a proposed anomaly intrusion detection system (AIDS) framework that leverages a hybrid of deep learning strategies. It incorporates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, which were developed using XGBoost, and their efficacy was assessed with the NSL-KDD dataset. The evaluation of the suggested model focused on its accuracy, detection capabilities, and the rate of false positives. The outcomes of this research are noteworthy within the cybersecurity field. In this paper, a framework of an Anomaly IDS is proposed. The purpose of an anomaly IDS, or AIDS, is to spot odd behavior on a network or system that might point to a security breach or malevolent attempt to hack it. Anomalybased IDSs concentrate on finding departures from accepted typical behavior, in contrast to signature-based detection systems, which depend on a predefined database of known attack patterns. [ABSTRACT FROM AUTHOR]

In modern telecommunications and computer networks, efficient and reliable information collection is essential for effective decision-making and control task resolution. Current methods, such as periodic data transmission, event-driven data collection, and on-demand requests, have distinct advantages and limitations. The object of the paper: The study focuses on developing a comprehensive model to optimize information collection processes in network environments. Subject of the paper: This paper investigates various information collection methods, including periodic data transmission, event-driven data collection, and on-demand requests, and evaluates their efficiency under different network conditions. This study proposes a flexible and accurate model that can optimize information support technologies for network control tasks. The key tasksinclude 1. Developing a probabilistictime graph model to evaluate the efficiency of different information collection methods. 2. Analyzing model performance through mathematical relationships and simulations. 3. Comparing the proposed model with existing methodologies. Results. The proposed model demonstrated significant variations in the efficiency of the information collection methods. Periodic data transmission increased network load, while event-driven data collection was more responsive but could miss infrequent changes. On-demand requests balanced timely data needs with resource constraints but faced delays due to packet loss. The probabilistic time graph effectivel y captured these dynamics, providing a detailed understanding of the trade-offs. Conclusions. This study developed a flexible and accurate model for optimizing information support technologies during network control tasks. The model's adaptability to varying network conditions has significant practical implications for improving network efficiency and performance. Future research should explore the integration of machine learning techniques and extend the model to more complex network environments. [ABSTRACT FROM AUTHOR]

Recent advances in the area of the Internet of Things shows that devices are usually resource-constrained. To enable advanced applications on these devices, it is necessary to enhance their performance by leveraging external computing resources available in the network. This work presents a study of computational platforms to increase the performance of these devices based on the Mobile Cloud Computing (MCC) paradigm. The main contribution of this paper is to research the advantages and possibilities of architectures with multiple offloading options. To this end, a review of architectures that use a combination of the computing layers in the available infrastructure to perform this paradigm and outsource processing load is presented. In addition, a proof-of-concept application is introduced to demonstrate its realization along all the network layers. The results of the simulations confirm the high flexibility to offload numerous tasks using different layers and the ability to overcome unfavorable scenarios. [ABSTRACT FROM AUTHOR]

A significant quantity of malware is created on purpose every day. Users of smartphones and computer networks now mostly worry about malware. These days, malware detection is a major concern in the cybersecurity area. Several factors can impact malware detection performance, such as inappropriate features and classifiers, extensive domain knowledge, imbalanced data environments, computational complexity, and resource usage. A significant number of existing malware detection methods have been impacted by these factors. Therefore, in this paper, we will first identify and determine the best features and classifiers and then use them in order to propose the malware detection method. The comparative strategy and proposed malware detection procedure consist of four basic steps: malware transformation (converting images of malware from RGB to grayscale), feature extraction (using the ResNet-50, DenseNet-201, GoogLeNet, AlexNet, and SqueezeNet models), feature selection (using PCA method), classification (including GDA, KNN, logistic, SVM, RF, and ensemble learning), and evaluation (using accuracy and error evaluation metrics). Unbalanced Malimg datasets are used in experiments to validate the efficacy of the results that were obtained. According to the comparison findings, KNN is the best machine learning classifier. It outperformed the other classifiers in the Malimg datasets in terms of both accuracy and error. In addition, DenseNet201 is the best pretrained model in the Malimg dataset. Therefore, the proposed DenseNet201-KNN methods had an accuracy rate of 96% and a minimal error rate of 3.07%. The proposed methods surpass existing state-of-the-art approaches. The proposed feature extraction is computationally quicker than most other methods since it uses a lightweight design and fewer feature vector dimensions. [ABSTRACT FROM AUTHOR]

The computer network environment is very complex, and there are many factors that need to be considered in the process of network security evaluation. At the same time, various factors have complex nonlinear relationships. Neural networks are mathematical models that simulate the behavioral characteristics of animal neural networks. They process information by adjusting the connection relationships of internal nodes, and have a wide range of applications in solving complex nonlinear relationship problems. The computer network security evaluation is multiple attribute group decision making (MAGDM) problems. In this paper, based on projection measure and bidirectional projection measure, we shall introduce four forms projection models with q-rung orthopair fuzzy sets (q-ROFSs). Furthermore, combine projection measure and bidirectional projection measure with q-ROFSs, we develop four forms of projection models with q-ROFSs. Based on developed weighted projection measure models, the multiple attribute group decision making (MAGDM) model is established and all computing steps are simply depicted. Finally, a numerical example for computer network security evaluation is given to illustrate this new model and some comparisons are also conducted to verify advantages of the new built methods. [ABSTRACT FROM AUTHOR]

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity, and because of its complexity and evasiveness, it is challenging to identify using traditional signature-based detection approaches. The study article discusses the growing danger to cyber security that malware hidden in PDF files poses, highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies. The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree. Using a dataset from the Canadian Institute for Cybersecurity, a comparative analysis is carried out with well-known machine learning models, such as Credal Decision Tree, Naïve Bayes, Average One Dependency Estimator, Locally Weighted Learning, and Stochastic Gradient Descent. Beyond traditional structural and Java Scriptcentric PDF analysis, the research makes a substantial contribution to the area by boosting precision and resilience in malware detection. The use of Logistic Model Tree, a thorough feature selection approach, and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection. The paper emphasizes Logistic Model Tree's critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector. The results reveal that the Logistic Model Tree is superior, with improved accuracy of 97.46% when compared to benchmark models, demonstrating its usefulness in addressing the ever-changing threat landscape. [ABSTRACT FROM AUTHOR]

With the deepening of industrial technology, the monitoring of the thermodynamic state of complex equipment systems has become a key to ensuring production continuity and safety. The thermodynamic state not only reflects the immediate working performance of the equipment but is also decisive in preventing overheating failures. The application of real-time remote monitoring technology provides a new solution for equipment health management, significantly reducing unexpected downtime, preventing failures, thus improving production efficiency and equipment lifespan. However, existing research still shows limitations in real-time data analysis, diversity of equipment adaptability, and accurate fault prediction. This paper proposes three innovative algorithms for the real-time remote monitoring of the thermodynamic state of complex equipment systems, and a method for mining overheating early warning information. The first algorithm, based on reconstruction error, is suitable for equipment with a large amount of normal operation data, using machine learning technology to accurately simulate the normal state to identify anomalies. The second single-class monitoring algorithm, suitable for situations with only normal operating data, can effectively detect deviations from the normal thermodynamic parameters. The third algorithm, based on statistical quantities, uses the statistical characteristics of equipment operation data for fault warning. In addition, the paper explores the application of the Bucket Sorting Fpgrowth algorithm in the mining of overheating early warning information of the thermodynamic state, analyzing potential fault modes and association rules through efficient data mining technology. These methods not only enhance the applicability and predictive accuracy of monitoring algorithms but also provide valuable decision support for equipment managers. [ABSTRACT FROM AUTHOR]

Official documents have a long history along with social governance. The dissemination of official documents began to mature with the evolution of the dynasty from the pre-Qin Dynasty until it developed into the electronic documents in the current electronic information age. With the development of computer network, document circulation system has been widely used in government system and enterprise. The academic research of official document communication in China started relatively late. Due to the low academic status of official document and the limited development of the subject, it is difficult for the research of official document writing and communication in China to keep up with the pace of the times. This paper will make an in-depth analysis of the academic development of official document communication from three aspects: the history of official document communication, the "academic" approach of official document communication and the "academic" structure of official document communication, combined with my own research and discovery in this field, aiming to lay a profound foundation for the academic approach of official document communication. [ABSTRACT FROM AUTHOR]

Modern computer networks' increasing complexity and scale need serious attention towards network anomaly detection. Software‐defined networking (SDN) and graph neural networks (GNN) have emerged as promising approaches for anomaly detection due to their ability to capture dynamic network behavior and learn complex patterns from large‐scale network data. The amalgamation of SDN and GNN for network anomaly detection presents promising opportunities for improving the accuracy and efficiency of network anomaly detection. This paper focuses on various trends, issues, and challenges by integrating GNN on the top of SDN for network anomaly detection. The article highlights the advantages of using SDN for providing fine‐grained control and programmability in network monitoring. At the same time, GNN can model network behavior as a graph and learn representations from graph‐structured data. The authors also discuss the limitations of traditional anomaly detection methods in SDN, such as rule‐based approaches, and the potential of GNN to overcome these limitations by leveraging their ability to capture non‐linear and dynamic patterns in network data. This paper also presents a case study of DoS attack detection using SDN. The result shows that SDN based approach helps to detect attacks with an accuracy of 97% with future research directions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Architectural Sciences & Applications (JASA) is the property of Journal of Architectural Sciences & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The strategies of multi-autonomous cooperative robots in a football game can be solved in multiple ways. Still, the most common is the "Skills, Tactics and Plays (STP)" architecture, developed so that robots could easily cooperate based on a group of predefined plays, called the playbook. The development of the new strategy algorithm presented in this paper, used by the RoboCup Middle Size League LAR@MSL team, had a completely different approach from most other teams for multiple reasons. Contrary to the typical STP architecture, this strategy, called the Probability-Based Strategy (PBS), uses only skills and decides the outcome of the tactics and plays in real-time based on the probability of arbitrary values given to the possible actions in each situation. The action probability values also affect the robot's positioning in a way that optimizes the overall probability of scoring a goal. It uses a centralized decision-making strategy rather than the robot's self-control. The robot is still fully autonomous in the skills assigned to it and uses a communication system with the main computer to synchronize all robots. Also, calibration or any strategy improvements are independent of the robots themselves. The robots' performance affects the results but does not interfere with the strategy outcome. Moreover, the strategy outcome depends primarily on the opponent team and the probability calibration for each action. The strategy presented has been fully implemented on the team and tested in multiple scenarios, such as simulators, a controlled environment, against humans in a simulator, and in the RoboCup competition. [ABSTRACT FROM AUTHOR]

The fast development in the use of computer networks raises concerns about network availability, integrity, and confidentiality. This requires network managers to use various types of intrusion detection systems (IDS) to monitor network traffic for unauthorized and malicious activity. An intrusion is a malicious breach of security policy. As a result, an intrusion detection system monitors network traffic flowing through computer systems to look for malicious actions and recognized dangers, providing alarms when it detects them. Mining approaches can be highly useful in the development of an intrusion detection system. In order to detect intrusions, network traffic might be categorized as normal or anomalous. After examining over twenty-five studies, we chose the top three classification algorithms in our paper: Logistic Regression, Naive Bayes, and SVM. This study compares the top three classification algorithms based on their performance criteria to determine the best suited method available. [ABSTRACT FROM AUTHOR]