Search

Information and Communication Technologies, to a long extent, have a major influence on our social life, economy as well as on worldwide security. Holistically, computer networks embrace the Information Technology. Although the world is never free from people having malicious intents i.e. cyber criminals, network intruders etc. To counter this, Intrusion Detection System (IDS) plays a very significant role in identifying the network intrusions by performing various data analysis tasks. In order to develop robust IDS with accuracy in intrusion detection, various papers have been published over the years using different classification techniques of Data Mining (DM) and Machine Learning (ML) based hybrid approach. The present paper is an in-depth analysis of two focal aspects of Network Intrusion Detection System that includes various pre-processing methods in the form of dimensionality reduction and an assortment of classification techniques. This paper also includes comparative algorithmic analysis of DM and ML techniques, which applied to design an intelligent IDS. An experiment al comparative analysis has been carried out in support the verdicts of this work using ‘Python’ language on ‘kddcup99’ dataset as benchmark . Experimental analysis had been done in which we had found more impact on dimensionality reduction and MLP performed well in the true classification to establish secure network. The motive behind this effort is to detect different kinds of malware as early as possible with accuracy, to provide enhanced observant among various existing techniques that may help the fascinated researchers for future potential works.

With the rapid development of information technology, issues such as network security and privacy protection have attracted more and more attention. The traditional way to solve these problems is to design complex cryptographic protocols to strengthen security, but the traditional solutions are based on centralized systems. The stronger the control of the central organization of the system, the more vulnerable it is to attackers. However, the blockchain technology that has emerged in recent years has revolutionized the use of decentralized solutions. The privacy and security issues of smart cities have become particularly important. Safety technology research in cities has also become very important. In order to better study the security technology in smart cities, this paper uses asynchronous transmission mode to study the blockchain security technology in smart cities on the premise of the Internet of Things technology. This paper compares and analyzes the data collection, sorting and calculation of the cryptographic elliptic curve algorithm before and after the improvement in the blockchain technology, the throughput and delay of different consensus algorithms, and the percentage of the computing power of the consensus algorithm. Transaction security. The final result shows that, the improved elliptic curve algorithm has more obvious advantages than the previous elliptic curve when the value of k is larger, and the higher the value of k, the faster its single scalar multiplication speed; The improved elliptic curve algorithm has more obvious advantages than the previous elliptic curve when the value of k is larger, and the higher the value of k, the faster its single scalar multiplication speed; The consensus algorithm has the largest percentage of computing power, BTC.com, accounting for 20.4%, and the smallest percentage of BWPool's computing power is only 1%. Therefore, it can be concluded that the decentralization and consensus mechanism of blockchain security technology can well guarantee the communication security of the Internet of Things.

Today, the use of large networks under the cloud and big data has become very common, as well as learning methods and artificial intelligence have been designed for them. One of the main issues in the banking and Big Data industry is granting bank facilities to people. The main challenge in evaluating the customers’ credit in the banking industry is that, due to the large volume of the data, it is impossible to review them manually, as we have to use computer algorithms for this purpose. Hence, the accuracy of the algorithm used for this purpose is essential and influential. In this paper, several predictive algorithms are employed on the problem, so that the result would be a prediction based on the combination of the results of each of them. To integrate the results of the algorithms, the weight to each of the algorithms is allocated, which presents the amount of importance of the algorithm in the result. The Ordered weighting averaging is used in this research. By applying these techniques, the proposed algorithms are described in this paper to achieve better accuracy than the existing ones.

The rapid development and adoption of Internet-of-Things (IoT) and sensors such as Global Positioning System (GPS) in our daily life allows gathering of wealth of information. This tremendous amount of sensor data can be clustered to infer information about the whereabouts of users. Researchers have proposed various algorithms for analyzing spatio-temporal data and clustering such datasets. However, a big challenge is to discover clusters with large density variation and to solve this problem most of the existing clustering algorithms manually set the input parameters. In the Density-based spatial clustering (DBSCAN) clustering algorithm, the epsilon and minimum point parameters have to be set manually for further computation. In this paper, we propose an improved DBSCAN algorithm to adaptively set minimum point parameter for dynamic length of data, based on merge sort and silhouette analysis. Validation of the proposed algorithm has been done on GeoLife GPS dataset. Experimental results show that the proposed algorithm is competitive with state-of-the-art methods for identifying users’ significant locations and whereabouts with the density variation. The paper also aims to highlight the issues pertaining to the invasion of user privacy by the potential application of the clustering algorithm on the aforementioned data.

The existing approaches of block based recovery of progressive secret sharing paradigm have support for limited $${k}$$ value in (k, n). Sian et al.’s as well Quan et al.’s block based visual secret sharing scheme has limited bound; works for a limited $${k}$$ value of $$\left( {{k},{~n}} \right)$$ threshold scheme. A novel threshold based (any value of $${k~and~~n}$$ ) block recovery in progressive secret sharing has been introduced and analyzed in this paper. Scheme achieves higher value of threshold than the schemes proposed by Sian et al.’s as well Quan et al.’s. The scheme discusses the sharing ability in progressive block based secret sharing up to any general number of thresholds. The contrast of the recovered block wise secret is reasonably good and the scheme allows for perfect security using at least $${k}$$ numbers of participants before revealing any of the blocks of secret image. The work in this paper offering a reference point for developing solutions needed in the area of secure image sharing techniques.

This paper proposes a new way of Quantum signature masked authentication, in which both user and certifying authority is needed for generating final signature. Our scheme uses decoy particle to protect key from all the attacks in the transmission process and does not require encryption of each key. The paper also shows that our scheme is protected against impersonation, forgery and outside attack and is more efficient than the other masked schemes existing in quantum cryptography.

Remote user authentication schemes provide a system to verify the legitimacy of remote users’ authentication request over insecure communication channel. In last years, many authentication schemes using password and smart card have been proposed. However, password might be revealed or forgotten and smart card might be shared, lost or stolen. In contrast, the biometrics, such as face, fingerprint or iris, have no such weakness. With the trend of mobile payment, more and more applications of mobile payment use biometrics to replace password and smart card. In this paper, we propose a biometric-based remote authentication scheme substituting biometric and mobile device bounded by user for password and smart card. This scheme is more convenient, suitable and securer than the schemes using smart cards on mobile payment environment.

Gait is an individual biometric behavior which can be detected based on distance which has different submissions in social security, forensic detection and crime prevention. Hence, in this paper, Advanced Deep Belief Neural Network with Black Widow Optimization (ADBNN-BWO) Algorithm is developed to identify the human emotions by human walking style images. This proposed methodology is working based on four stages like pre-processing, feature extraction, feature selection and classification. For the pre-processing, contrast enhancement median filter is used and Hu Moments, GLCM, Fast Scale-invariant feature transform (F-SIFT), in addition skeleton features are used for the feature extraction. To extract the features efficiently, the feature extraction algorithm can be often very essential calculation. After that, feature selection is performed. Then the classification process is done by utilizing the proposed ADBNN-BWO Algorithm. Based on the proposed method, the human gait recognition is achieved which utilized to identify the emotions from the walking style. The proposed method is validated by using the open source gait databases. The proposed method is implemented in MATLAB platform and their corresponding performances/outputs are evaluated. Moreover, the statistical measures of proposed method are also determined and compared with the existing method as Artificial Neural Network (ANN), Mayfly algorithm with Particle Swarm Optimization (MA-PSO), Recurrent Neural Network -PSO (RNN-PSO) and Adaptive Neuro Fuzzy Inference System (ANFIS) respectively.

The privacy and utility are treated as a major factor in influencing the role of data privacy preservation in cloud environments. There exists a trade-off between these two factors, where one factor should compromise its functionality over the other. It is hence necessary to maintain both utility and privacy for a data offloaded or accessed across cloud computing environment. In this paper, we develop a utility privacy model that established utility using Deep Adaptive Clustering (DAC) and privacy using Elliptic Curve Digital Signature Algorithm (ECDSA). The utility is performed using clustering of the input datasets using DAC and the privacy is maintained using ECDSA. The simulation is conducted on specific datasets to test the efficacy of the model and the results shows improved accuracy on clustering, and efficient privacy metrics than existing methods.

Education-as-a-Service is the new proposition of this paper, which intelligence mapped with extracted knowledge from the educational data with the sole objective of modernizing the analytical process involved in it. A review of existing studies shows that majority of the scheme has focused on using direct educational data without the inclusion of practical issues in it. The proposed system introduces a novel mechanism where the traffic of unstructured educational data is subjected to unique transformation and extraction of know-ledge in presence of all practical constraints. The outcome of the study is delivered in the form of cloud services. The proposed logic is implemented in MATLAB using bigger size of educational data. The outcome accomplished shows that it facilitates better analytical operation in contrast to existing distributed software framework with better accuracy and computational process du-ration.

Software Defined Networks (SDNs) is an emerging concept in network architectures, which divides the network operations into two, control and data, layers. In this concept, control and management operations are moved from the network devices to the controller and inside the control plane. This separation makes it possible to incorporate network devices for different applications, while on the other hand leads to vulnerabilities in the network. In fact, the controller becomes the bottleneck of the network, and it is vulnerable to intrusions. Various approaches have been proposed to detect intrusion in these networks, which among them using deep learning methods has gained the majority of attention in the past decade. In this paper, an intrusion detection system based on the SDN model is presented which is executed as an application module in the controller. The proposed system consists of three phases: in the first phase, for pre-training, sparse stacked auto-encoders are incorporated which learn the features in an unsupervised manner. In the second phase, to train the system, the SoftMax classifier is used and in the third phase, system parameters are optimized. Performance of the proposed system is evaluated according to two datasets, namely NSL-KDD and CICIDS2017, for classification of attacks. To implement the proposed method, the Mininet software and Keras framework, which is based on Tensorflow, are incorporated. The average accuracy in detection and classification of attacks using the proposed method is 98.5%, which is promising in comparison with previous methods.

Recently, SDN has arisen as a new network platform that offers unparalleled programming that enables network operators to dynamically customize and control their networks. The attackers aim to paralyse the logical plane, the brain of the network that offers several advantages, by using the SDN controller. However, the control plane is the desirable target of security attacks on the opponents because of its characteristics. One of the most common threats is the DDOS attacks to drain network capacity by sending them heavy traffic, causing network congestion. SDN is a common area of investigation for SDN defenceand DDoS threat identification and prevention in the SDN context has been introduced to many researchers since the proposed SDN attacks. Nevertheless, security risks must be adequately secured. In this paper we suggest a discrete scalable memory based support vector machine algorithm for DDoS threat and SDN mitigation architecture for attack detection. By starting the process of attack detection the input data can gets pre-processed by using Spark standardization technique in which the missing values are replaced and the unwanted data are removed. Then the feature extractions are done using semantic multilinear component analysis algorithm. The classifier is responsible for predicting target and for this a novel discrete scalable memory based support vector machine (DSM-SVM) algorithm is used which provides high accuracy of attack prediction. Followed by attack detection the mitigation process was done, here the mitigation server can identify the threat by intelligently dropping malicious bot traffic and absorbing the rest of the traffic. Here the suggested mechanism achieves attack traffic mitigation and benign traffic dropping. We have evaluated the whole process on KDD dataset. The proposed network model was trained and then used in an SDN threat detection and mitigation environment as part of the assessment process. The entire experiment is run on a VMware-based Ubuntu virtual machine. Weka will utilize our suggested classifier model for training and evaluation, while Mininet uses a RYU controller to establish an SD Network. The findings demonstrate that the mechanism presented exceeds the other algorithms examined, by expressing 99.7% accuracy especially concerning training and testing time over KDD dataset.

With the rapid advancement in the mobile and computing technologies, individuals have an inclination to depend more on them in varied range of situations. For ensuring the secured communication among the devices, privacy and security are the most prominent concerns. Accordingly, sophisticated preventive measures that support both secure storage and secret transfer has begun as the need of the hour. Several conventional techniques focuses on the data encryption and key generation approaches to attain reliability. However, there were some short-comings in key aspects such as privacy, key management, and flexibility. So as to overcome existing issues the paper implements a compact, constant-time, and fast improvised ChaCha 20 stream cipher mechanism for encryption and decryption. Likewise, the generation of key is employed with the use of Diffie-Hellman key exchange protocol scheme based on the string comparison. Thus, a secured transmission occurs with a reduced rate of execution time, decryption time, and key generation time. The performance evaluation is made in terms of encryption time, decryption time and the key generation time and are compared with the existing techniques to prove the superiority of this proposed scheme.

Vehicular Adhoc Network (VANET) is based on the principles of Mobile Adhoc NETwork (MANET) where vehicles are considered as nodes and secure communication is established to provide a safe driving experience. Due to its unique characteristics, it has various issues and challenges. These issues can be resolved by ensuring security requirements like authentication, privacy preservation, message integrity, non-repudiation, linkability, availability etc. Authentication plays a vital role since it is the first step to establish secure communication in the vehicular network. It also distinguishes malicious vehicles from legitimate vehicles. Different authentication schemes have been proposed to establish secure vehicular communications. A survey of the existing authentication schemes is given in this paper. At first, the existing authentication schemes are broadly classified based on message signing and verification methods. Then, each category is clearly explained with its sub-categories. At last, the existing schemes in each category are compared based on security requirements, security attacks and performance parameters.

Computer-assisted Collaborative Learning (CCL) is a good teaching technique to enhance students' learning and intellectual performance consistency. Several studies have been undertaken to recognize variables that affect student intellectual ability. Observation obtained from the studies helped students in the right way and improved their habits and personal condition in learning and intellectual performance. In this paper, the proposed method has a multi-level multi-class classification algorithm. The dataset is transformed into various problem syntheses, and adaptive methods such as Binary Significance (BS), Label Powerset (LP), Classifier Series (CS), K-Nearest Neighbor (KNN), and Machine Learning integrated K-Nearest Neighbor (MLiKNN) adaptive classifier algorithm. Finally, the variables that significantly impact the student's success are identified, and recommendations for enhancing those factors are suggested to enhance the student's intellectual ability. The outcomes observed from the proposed classifier framework provide more recommended strategies to enhance students' learning environment and intellectual ability.

Wireless Sensor Network (WSN) is a wireless network that consists of spatially distributed autonomous devices with sophisticated subsystem called sensors to monitor the environmental conditions. WSN’s measure environmental conditions like temperature, sound, pollution levels, humidity, wind, etc. Data fault detection is a challenging problem due to presence of sensors in unforeseeable areas. This detection has to be precise and accurate, so that it can be used for weather prediction, disease prediction, traffic monitoring, etc. In recent years, Machine Learning plays a vital role in accurate fault detection. In this paper, we propose an Enhanced Minimum Redundancy Maximum Relevance algorithm. Our work combines the principle of filter and wrapper methods by using F-Mutual Information Difference (FMID) and FMIQ as the objective functions to determine the relevance and redundancy. The core analysis carried out in various datasets shows that the classification accuracy ranges from 93 to 100%, which is promising compared to the existing systems. The strength of our algorithm can be leveraged in situations where data fault detection accuracy is a real matter of concern.

Towards enabling 5G and beyond radio access technologies to meet the requirements for continuous dynamic and diverse services, flexibility and scalability of the cellular network are of utmost important. In part, the revolution in information technology is craving way to the realization of the desired flexibility and scalability through advancement in softwarization and virtualization technologies. To this end, radio access networks (RAN) can be softwarized by employing appropriate virtualization techniques and open-source platforms to develop prototypes, which can be directed at fostering further mobile network research activities. In this paper, we provide a review of OpenAirInterface (OAI) implementation and present an OAI based cloud RAN (C-RAN) testbed with which mobile fronthaul (MFH) solutions can be tested. Furthermore, transmission of real-time radio signals over a passive optical network (RFoPON) is demonstrated. While successful transmission is achieved, the preliminary results show that the performance of C-RAN is limited by the delay overhead requirement of the passive optical network.

Mobile nodes are deployed at different locations in the smart city to collect meaningful information so captured data can be used as an input in different smart city applications for society benefits. However, data is transferred through a public communication channel and thus, it is important to achieve specific security level to protect from malicious users in the network. There are various data transmission methods for mobile environments, but they are vulnerable against fundamental security attacks and the performance results are not effective for the mobile ad-hoc network. In this paper, we identify some security issues in Islam et al.’s scheme. To address found issues and improve the security and efficiency, we propose an energy-efficient and secure communication scheme for mobile node applications, achieving user identity privacy. We do security evaluations of the proposed protocol to confirm its strengths against various attacks. Further, we discuss performance analysis (for execution cost, energy consumption, communication overhead, and storage cost) for the suggested data transmission method and then, do the comparison with other relevant communication mechanisms.

Network Functions Virtualization (NFV) is a network architecture concept to improve network performance. This concept empowers the network capacities and reduces the cost and accelerates the network services and moving them to virtual severs. Mobility Management Entity (MME) is one of the feasible components for virtualization to control the plane due to its non-handling features for user data traffic. However, the MME does not handle the user data and not using any sort of hardware for data processing towards the cloud. The deployment of MME on the cloud is not an appropriate strategy due to the heavy load and complex processes. The main objective of this paper is to develop a suitable software deployment architecture Software-Defined Network-based Network Functions Virtualization (SDN-NFV) that can provide a cloud-optimized design for the MME. The proposed architecture is based on SDN networks to boost and enhance the traditional cellular network processes and functionalities. The proposed architecture contains three tiers including the data plane, control plane, and application plane. The data plane contains the switches, open flow protocol, and OpenSwitch virtual software and responsible to handle the data traffic and forward it to the correct destination based on flow entries in its flow table. The control plane is responsible for the entire network topology and contains the functionalities of traditional non-SDN routers and policies that come from the application plane. The application plane uses a virtualization environment where all the requirements are defined to the corresponding controller. The proposed architecture is evaluated in a simulation where it achieved better performance in terms of latency and throughput.

Things or device under the Internet of Things, when connected to the internet, becomes a product, offers various applications and services for end-users. Existing methodologies offer sensor-based IoT based health-care services, to the end-users populated with lots of sensed values all at a time, making the health-care system with least robustness and inefficient due to unsystematic preview of patient’s health record. In order to address this issue, a novel semantic-based service framework is proposed in this paper which allows the end-user to subscribe for a specific physiological parameter, among all the available sensed data, making the health-care system more efficient. In the SeSem framework, Semantification rules and semantification relationship table are applied to the sensed JavaScript object Notation format (JSON) data, in order to semantically separate the JSON data, into a meaningful format. To list the sensed data according to the significance of health issues, a priority is assigned to the sensors in the semantification relationship table. Hence, semantically separated data can be done along with assigned priority. Sensor-based Sematic (SS) ontology is then applied to the semantically separated data, to transform the sensed data more relevant in terms of particular disease and sensor associated with it. The semantically separated sensed data are then published to the Message Queuing Telemetry Transport (MQTT) interface. Using MQTT subscribe, the end-user along with date and time, requests for a particular service, using a Semantic Similarity mapping algorithm, which compares the entire sensed data to that of requested data and responds with a particular physiological parameter request. To make the health care system deploy services in an intelligent way, deep learning algorithm Feedforward Recurrent Neural Networks are applied, which makes the prediction of sensed data based on the latest update when the end-user subscribes for a certain sensed data without specifying date and time. The proposed methodology is evaluated against IoT performance metrics which had shoed showing better results in terms of service-oriented IoT.

Cloud-edge computing is an emerging computing model based on Service Oriented Architecture that provides reliable and available cloud services as scalable resources by collaborating fog nodes on Internet of Things (IoT) environments. One of the important issues on service discovery is energy efficiency and security for existing cloud providers and fog nodes. An optimal service discovery and selection approach as an NP-Hard problem can effective on decreasing time and cost in cloud providers to achieve through maximum capacity of Quality of Service (QoS) factors. To address of the above challenges, this paper focuses on above-mentioned outcomes and presents a QoS-aware cloud-edge service discovery and selection model in IoT environment. This model is evaluated based on a hybrid multi-objective meta-heuristic algorithm based on a Grey Wolf Optimizer and a Genetic Algorithm (GWO-GA) for evaluating QoS factors as non-functional properties. The proposed model is meant to guarantee QoS factors such as the response time, energy consumption and cost factors for the service discovery and selection problem in the IoT environment. Experimental showed that the proposed method performs 30% better than the other algorithms for decreasing cost factor.

The internet of things (IoT) is the networking of interrelated devices and sensors connected through the internet to transfer and share data. The data gathered from these devices may have anomalies or other errors for various reasons, such as malicious activities or sensor failures. Anomaly detection is found in several domains, such as fault detection and health monitoring systems. In this paper, we review and analyze the relevant literature on existing anomaly detection techniques that apply different machine learning approaches in the IoT. In addition, we examine different anomaly detection datasets used in IoT and highlight the most concerning issues with these datasets for different approaches, and list several future research directions. We believe this survey will serve as a starting point for researchers to gain knowledge from the IoT that employs machine learning approaches to detect anomalies. Moreover, the datasets that associate with anomaly detection in IoT, issues and future directions from a dataset perspective.

The most burning issues worldwide at present are the availability, accessibility, and affordability of the equitable healthcare services for all. It is getting more severe for developing countries due to increasing population and chronic diseases. The emerging technological interventions in the field of Internet of Things (IoT)-based healthcare systems are a promising solution to meet the general public's healthcare needs. Therefore, an IoT-enabled vital sign monitoring system has been presented in this paper. The presented system can monitor various vital signs in real-time and store the recorded trends locally. The system can also send the data into cloud for further analysis. Abnormality detection with alert notification and automatic calculation of early warning score has been implemented. An Android application is developed to store the vital signs records on a personal server to avoid the burden and maintenance cost of the central medical server. The presented system is straightforward, compact, portable and easy to operate through personal service application. Also, the presented system is compared with the most recent work available in the field.

The recent pandemic has demanded a strong and smart healthcare system which can monitor the patients efficiently and handle the situation that arises from the outbreak of the disease. Smart healthcare cyber physical systems are the future systems as they integrate the physical and cyber world for efficient functioning of medical processes and treatment through external monitoring and control of patients, medical devices and equipment for continuous communication and information exchange of physiological data. Technologies like Internet of Things, Machine learning and Artificial Intelligence have given birth to smart cyber physical systems like Smart Healthcare Systems, Smart Homes, Smart Vehicular Systems and Smart Grid. Such systems are interdisciplinary in nature with multitude of technologies contributing to its effective working. This paper presents a case study on healthcare cyber physical systems presenting its characteristics, role of various technologies in its growth and major challenges in successful implementation of cyber physical medication systems.

Apache Spark programming framework provides an effective and vigorous open-source solution for Big data testing and management. It offers a rich set of big data applications through a programming interface. Testing procedures deliver optimum solutions to complex business problems like analyzing large data volumes. A typical test engineer demands manual to automatic testing which assists the testing strategy with minimal effort. Big data testing means, verification, and validation of data while storing and processing it. This research paper demonstrates the challenges with new high-performance analytics for simpler and faster testing processing of relevant data. It enables, timely and accurate insights using Big data testing predict analytics and can manage large quantities of Structured, Semi-structured, and Unstructured data forms with spark analysis. These methods are evaluated using Extract, Transformation, Loading, and Apache spark procedures. The proposed Big data testing method has statistically outperformed conventional procedures.

Internet of Things is one of the most versatile technologies in existence today. It has taken over our day to day activities and thus has many applications that are designed to make life easier and simpler. Partly because IoT is new, it is replete with insecurities and vulnerabilities. Due to the lack of fundamental security controls, and the integration of real-world objects with the Internet, IoT devices are facile targets for cyber-criminals and other aggressors. This means that these vulnerabilities can be exploited for hacking, adding to Botnets, and then used to launch DoS and DDoS against organizations. To provide security from DoS and DDoS attacks, various solutions have been proposed. In this paper, Machine Learning, as well as Deep Learning algorithms, have been employed to analyze the DoS and DDoS attacks. The Bot-IoT dataset of the Centre of UNSW Canberra Cyber was used for training purposes. ARGUS software was used to generate the features from the pcap files of UNSW. A testbed was setup using 20 devices and generated dataset. From the result, the best accuracy of attack classification is 99.5% and 99.9% for Deep Learning and Machine Learning algorithms respectively.

Cloud computing is a constantly evolving archetype since web 2.0. Though it’s a popular IT buzzword and concepts are derived from decade old grid computing, distributed computing, utility computing and intensive application computing. It offers a broader range of services such as virtual machines (VMs), servers, storage devices, operating systems (OS),network resources over the internet to its users on ‘pay-for-usage’ basis. Dynamic group data sharing were users anonymously share his/her data with other group members over cloud could compromise security hence there is need to design an efficient, secure data sharing in dynamic group. Hence, this review paper presents various problems and challenges in designing an effective dynamic group data sharing. The problems and challenges are classified based on Client based and service provider based were client-based problems includes user authentication, user privacy and security, data confidentiality & integrity and query cost and service provider based problems includes user’s identity & traceability, user revocation, energy efficiency and performance. Based on the challenges, researchers have proposed and developed various protocols, management and control mechanisms under data security, access control, query grouping and energy efficiency headings which is briefly summarised in this review to help future researchers in developing efficient, security data sharing schemes in cloud environment.

Cloud computing is a global technology for data storage and retrieving. Many organizations are switching their companies to cloud technology, so that they can lease cloud services for use on a membership or pay as you go basis rather than creating their own systems. Cloud service provider and the Cloud service accessibility are the two major problems in cloud computing. The Economic Denial of Sustainability (EDoS) attack is an important attack towards the cloud service providers. The attackers may send continuous requests to the cloud in a particular second. Hence the legitimate user cannot access the data due to heavy cloud traffic. Hence the paid user cannot access the data. However, this problem makes an economical issue to the users. So this paper presented a new technique as, ADS-PAYG (Attack Defense Shell- Pay As You Go) approach using Trust Factor method against the EDoS attack is proposed to improve more number of authenticated users by fixing a threshold value. The algorithm produced an effective result based on response time, accuracy and CPU utilization. The ADS-PAYG solution is applied using MATLAB, which outperforms other Trust factor estimation methods and effectively distinguishes attackers from legitimate users. The detection accuracy is 83.43% for the given dataset and it is high when compared to the existing algorithms.

Nowadays sharing secure data turns out to be a challenging task for the data owner due to its privacy and confidentiality. Several IT companies stores their important information in the cloud since computing has developed immense power in sharing the data. On the other hand, privacy is considered a serious issue in cloud computing as there are numerous privacy concerns namely integrity, authentication as well as confidentiality. Among all those concerns, this paper focuses on enhancing the data integrity in the public cloud environment using Qusai modified levy flight distribution for the RSA cryptosystem (QMLFD-RSA). An effective approach named QMLFD for the RSA cryptosystem is proposed for resolving the problem based on data integrity in public cloud environment. A secured key generation and data encryption are done by employing the RSA cryptosystem thus the data is secured from unauthorized users. The key selection is done by using quasi based modified Levy flight distribution algorithm. Thus the proposed approach provides an effective model to enhance the integrity of data in cloud computing thus checking the data integrity uploaded in the public cloud storage system. In addition to this, ten optimization benchmark functions are calculated to determine the performances and the functioning of the newly developed QMLFD algorithm. The simulation results and comparative performances are carried out and the analysis reveals that the proposed QMLFD for the RSA cryptosystem provides better results when compared with other approaches.

One of the prominent applications of Internet of Things (IoT) in this digital era is the development of smart cities. In IoT based smart cities, the smart objects (devices) are connected with each other via internet as a backbone. The sensed data by the smart objects are transmitted to the sink for further processing using multi hop communication. The smart cities use the analyzed data to improve their infrastructure, public utilities and they enhance their services by using the IoT technology for the betterment of livelihood of the common people. For IoT based smart cities, waste collection is a prominent issue for municipalities that aim to achieve a clean environment. With a boom in population in urban areas, an increasing amount of waste is generated. A major issue of waste management system is the poor process used in waste collection and segregation. Public bins begin to overflow for a long period before the process of cleaning starts, which is resulting in an accumulation of bacteria causing bad odors and spreading of diseases. In order to overcome this issue, in this paper an IoT based smart predication and monitoring of waste disposal system is proposed which utilizes off-the-shelf components that can be mounted to a bin of any size and measure fill levels. An Arduino microcontroller is employed in the proposed model to interface the infrared (IR), ultraviolet (UV), weight sensors, and a Global Positioning System (GPS) module is used to monitor the status of bins at predetermined intervals. The proposed system transmits the data using the cluster network to the master module which is connected to the backend via Wi-Fi. As data is collected, an intelligent neural network algorithm namely Long Short-Term Memory (LSTM) is used which will intelligently learn and predict the upcoming wastage from waste generation patterns. Moreover, the proposed system uses Firebase Cloud Messaging to notify the appropriate people when the bins were full and needed to be emptied. The Firebase Cloud Messaging (FCM) JavaScript Application Programming Interface (API) is used to send notification messages in web apps in browsers that provide service work support. Hence, the proposed system is useful to the society by providing facilities to the governments for enforcing stricter regulations for waste disposal. Additional features such as automated calibration of bin height, a dynamic web data dashboard as well as collation of data into a distributed real-time firebase database are also provided in the proposed system.

Today almost every person’s life revolves around internet and Internet of Things (IoT). IoT is a paradigm which interconnects devices, people, or networks with the ability to process and respond to any physical or virtual communication without a glitch. It is contemplated to be the next era of communication and made devices smarter and more efficient. IoT hits every application area from home controllers and healthcare to agriculture. It utilizes internet connectivity, sensors and numerous other technologies and protocols for data collection and analysis and delivers user required services effectively. In this paper, a detailed review on various architectures, technologies and protocols used in an IoT eco-system is presented. We have also discussed possible layer wise attacks and how new technologies, fog, edge, cloud, artificial intelligence, machine learning and blockchain could be integrated to existing IoT architecture to deliver flawless services and better security. A summary of current research challenges and future directions in this area is also discussed.

The Internet of Medical Things (IoMT) is the array of medical instruments and related technologies that link Information Technology (IT) systems in health care through online smart interface networks. Presently, Medical images are considered an essential source of a patient's clinical record and may be utilized to analyze infectious disease along with their diagnostic procedure, operations, and prediction. In cancer diagnostics, at an early stage, it is hard to classify using present diagnostics scanning methods such as Computed tomography (CT) and magnetic resonance imaging (MRI). In this research paper, an Improved Deep Belief Learning Network (IDBLN) is introduced in the software application of ultrasound imaging techniques for the effective feature analysis and classification of various cancer images at the early stages. IDBLN contains an improved automatic feature selection framework for a useful classification and detection of cancer images during scanning. An Enhanced Optimized Crow Search algorithm (EOCS) is suggested for classification to boost cancer detection efficiency during image scanning. Finally, outcomes are analyzed based on accuracy, sensitivity, and minimum error rate, proving the proposed framework's performance.

Use of internet of things (IoT) in different fields including smart cities, health care, manufacturing, and surveillance is growing rapidly, which results in massive amount of data generated by IoT devices. Real-time processing of large-scale data streams is one of the main challenges of IoT systems. Analyzing IoT data can help in providing better services, predicting trends and timely decision making for industries. The systematic structure of IoT data follows the pattern of big data. In this paper, a novel approach is proposed in which big data tools are used to perform real-time stream processing and analysis on IoT data. We have also applied Spark’s built-in support of the machine learning library in order to make real-time predictions. The efficiency of the proposed system is evaluated by conducting experiments and reporting results on the case scenario of IoT based weather station.

Multiple inputs multiple outputs (MIMO) is a reliable technique which can manage the increased wireless data traffic in the future generation of wireless communication network and it is an emerging area of research. Multiple signal paths that exist between the transmitter and receiver are utilized by the MIMO technique to produce spatial multiplexing. During the entire communication, two factors namely energy efficiency and low power consumption need to be considered as the important parameters to design an effective multimedia communication system. Hence, this paper proposes a MIMO-OFDM (Orthogonal Frequency-Division Multiplexing) multimedia model with very low power consumption that provides better energy-efficiency. The MIMO technique is integrated into an OFDM scheme to deliver enhanced QoS for multimedia wireless communication. The research proposed uses the RSTBC system (Rateless Space-Time Block Code) to code video data on mobile networks in order to optimize the power constraint scheme. The Wyner–Ziv coding scheme is proposed here to enhance the flexibility of high-speed video coding models. The Modified Dragonfly Optimization algorithm (MDOA) is designed to decrease the power consumption of mobile communication systems and optimize their energy efficiency. During the optimization process, the MDOA generates the confidence to minimize the mean square error (MSE) value. The MIDO algorithm is used for broadcasting and for effective promotion of hierarchical visual communication to increase energy efficiency and decrease energy consumption. The experiment is conducted using the MDOA algorithm demonstrate that the proposed algorithm can guarantee maximum efficiency for the MIMO-OFDM approach in terms of bit error rate, signal to noise ratio, mean square error (MSE), power consumption, processing time, and energy efficiency.

Internet of Things (IoT) has received attention recently, featuring tremendous applications. Due to the lack of authentication, popular IoT applications are prone to various attacks. However, because of resource limitation and heterogeneous characteristics of IoT applications, the cryptographic algorithms may fail to be universal adopted and thus, it is challenging to defend against these attacks. In this paper, we proposed NOMOP-ECDSA, a lightweight Elliptic Curve Digital Signature Algorithm for IoT applications, which can address the aforementioned challenge. Our idea is that the resource-consuming operations can be replaced with other basic operations, and therefore, achieve better performance. As a demonstration, we also proposed a mutual authentication protocol for smart cards. In terms of security and efficiency, systematic analysis is performed to validate our NOMOP-ECDSA and protocol.

Recent development of cognitive computing driven evolutionary techniques improve the overall quality of service and user experience in wireless communication network. This Paper consists of a feature selection method based on improvement of Crow Search Algorithm which has been used in Intrusion Detection System to limit the size of the dataset with which the system is working with and getting better results. Since IDS deals with a large data, the crucial task of IDS is to keep efficient features which represents the whole data and there is no duplicity and irrelevancy. The previous model that was proposed used the crow search algorithm in the intrusion detection system (CSA-IDS) as a model to find the optimal feature’s subset and random forest as a judgement on features that are produced by the CSA-IDS. The KDD and UNSW datasets are used to evaluate the earlier proposed model. The proposed model achieved an accuracy of 99.84% for attack detection using UNSW datasets. Similarly, R2L and U2R attacks have detected accuracy of 99.97% for NSL-KDD dataset. The development of proposed model improve the overall communication services and feature selection in wireless communication network. The outcome proves that the subset of features that are obtained by using CSA-IDS fetches higher accuracy rate using a smaller number of features.

Network intrusion detection systems analyze traffic in a medical IoT system to detect abnormal behaviors. Machine learning and artificial intelligence (AI) algorithms are widely used in designing intrusion detection systems to prevent attacks on a medical IoT system. In this paper, an artificial neural network is employed to detect abnormal behavior in a medical IoT system. The accuracy of the detection depends heavily on the features that are fed into the artificial neural network. Selecting the important and discriminative features of network traffic is a crucial and challenging issue because it has a significant impact on the learning process. In the proposed method, the butterfly optimization algorithm which is a meta-heuristic optimization algorithm is employed to select the optimal features for the learning process in an artificial neural network. The results achieved, 93.27% accuracy, indicate the capability of the butterfly optimization algorithm to determine discriminative features of network traffic data. The proposed algorithm outperformed the decision tree, support vector machine, and ant colony optimization, which was proposed in previous researches for the same goal.

Smart healthcare systems are important components in an IoT environment that attracted the researchers’ attentions, recently. For such applications the biomedical sensors are placed on patients’ body to gather the condition of the patient through a wireless body region network (WBAN). In a WBAN, energy consumption is one of the crucial issues, in particular in emergency situations. In this paper, a solution is presented using sensor node clustering methods. In the proposed method, body region is divided into three parts, upper, lower, and middle region. An improved LEACH clustering algorithm is performed in each region. After that, an evolutionary algorithm, whale optimization algorithm (WOA), is used to select cluster heads. To evaluate the proposed method, the algorithms are implemented in MATLAB environment and compared with SEP and LEACH-H solutions that are proposed before for the similar solutions. The proposed solution outperformed the other solutions and provide longer lifespan for sensors in a wireless body region network.

In this paper, the authors propose a secure and trusted energy trading (ET) scheme for electric vehicles (EVs) for vehicle-to-anything (V2X) ecosystems. The scheme, named as EVBlocks, facilitates ET among entities (i.e., EVs, charging stations (CS), and smart grids (SG)) in a secured and trusted manner through a consortium blockchain (CBC) network. The scheme operates in three phases. In the first phase, to allow real-time and resilient network orchestration of V2X nodes, we consider the ET service designed over a fifth-generation (5G) enabled software-defined networking (SDN) environment. Integration of SDN in 5G-V2X ecosystems allows V2X nodes to eliminate intermediaries and handle many requests with a minimum response time. Then, in the second phase, a non-cooperative game is presented that optimizes a cost function and converges to reach at least one Nash equilibrium point. Finally, a consensus algorithm Proof-of-Greed (PoG) is proposed that handles fluctuations in charging/discharging EVs through an event-driven scheduling mechanism. The obtained results are compared against parameters, such as ET time, State-of-Charge (SoC) levels, EV utility, block-convergence time, profits, computation, and communication costs. For example, EVBlocks achieve an average SOC charge of 22.8MW, with a peak at 377.5MW, the average power dissipation of 4.1125 kWH that is lower than $$25\%$$ against existing conventional and fixed tariff schemes. The scheme converges at stable profit values for 5 EVs through a non-cooperative game. For proposed PoG consensus, the block convergence time for 1000 nodes is 138.96 seconds, at a computation cost of 46.92 milliseconds (ms) and communication cost of 149 bytes. The comparative analysis suggests the proposed scheme is efficient as compared to existing state-of-the-art approaches against compared parameters.

Privacy is a significant problem in communications networks. As a factor, trustworthy knowledge sharing in computer networks is essential. Intrusion Detection Systems consist of security tools frequently used in communication networks to monitor, detect, and effectively respond to abnormal network activity. We integrate current technologies in this paper to develop an anomaly-based Intrusion Detection System. Machine Learning methods have progressively featured to enhance intelligent Anomaly Detection Systems capable of identifying new attacks. Thus, this evidence demonstrates a novel approach for intrusion detection introduced by training an artificial neural network with an optimized Bat algorithm. An essential task of an Intrusion Detection System is to maintain the highest quality and eliminate irrelevant characteristics from the attack. The recommended BAT algorithm is used to select the 41 best features to address this problem. Machine Learning based SVM classifier is used for identifying the False Detection Rate. The design is being verified using the KDD99 dataset benchmark. Our solution optimizes the standard SVM classifier. We attain optimal measures for abnormal behavior, including 97.2 %, the attack detection rate is 97.40 %, and a false-positive rate of 0.029 %.

As the number of smartphone users is increasing exponentially, there is an increase in the availability of continuous sensor data, which has attracted enormous interest in sensor-based human-activity recognition (HAR). Recognizing human activities is particularly important in detecting abnormal activities and tracking a person's physical activity, especially in healthcare applications, among many others. In this paper, HAR analysis is conducted with three different machine learning algorithms (Support Vector Machines (SVM), Decision tree, and random forest methods) based on smartphone sensors. Machine learning algorithms are capable of identifying and differentiating between different human activities using mobile phone sensor data. The smartphone sensors (gyroscope and accelerometer) data are recorded at the Koneru Lakshmaiah Education Foundation University campus, Guntur, India, with different human activities. In this research work, the data from smartphone mobile sensors were initially analysed with SVM, decision tree, and random forest algorithms. To evaluate the machine learning algorithm's accuracy, F1 score for different smartphone sensors for both individually and combined is estimated. The results indicate that the proposed machine learning methods can derive a relation between type of activity, algorithm, smartphone sensors data, and their corresponding accuracy. The outcome of this work would be beneficial for detecting abnormal features of older people with a smartphone device.

This paper proposes an area, speed and power-optimized band-pass digital signal processing filter targeted for Kintex-7 Field Programmable Gate Array device. The filter was designed using MATLAB and Simulink and code generated using HDL Coder from MathWorks. The implementation was created using a novel high-level synthesis design method, which reduces pessimism associated with bit-width constraints in synthesis for inputs, outputs, and intermediate data nodes. MATLAB HDL coder generated Register Transfer Level (RTL) code was implemented on Xilinx Kintex 7 using Vivado software. The obtained results are superior to those of previous implementations for exact filter specifications. We also performed an RTL simulation for the filter and compared the functional verification results with a golden double-precision implementation in MATLAB. The results suggest that constraining the bit width and pessimism reduction has less than 1% impact on the filter accuracy within limits specified by architecture specifications.

A vehicular ad-hoc network (VANET) is an autonomous system of mobile vehicles created on-demand where vehicles can send or receive data packets over wireless links. In VANET, vehicles have limited communication range R and can communicate directly with neighbor vehicles. Vehicles are equipped with the short communication range dedicated communicative device, global positioning system and other computing devices. Links among vehicles break frequently due to the highly movable nature and limited communication range which turns into data delivery failure. To overcome data delivery failure in multi-hop routing, a novel routing model namely fuzzy logic-based multi-hop directional location routing (FLMDLR) in the vehicular ad-hoc networks has been developed. Using the concept of fuzzy logic, FLMDLR selects remarkable next-hops which help to establish a stable route from the source S to destination node D. Research work done in this paper has shown fuzzy logic applications are more useful to deliver data packets from node S to D. FLMDLR has simulated through network simulator-2 to evaluate performance in term of the link duration, next- hop distance, average number of next-hops, and one hop delay. Through simulated result has shown the proposed model FLMDLR perform better as compared to existing directional-location aided routing (D-LAR) and location aided routing (LAR) protocols.

This paper introduces a novel Energy Efficient Mobility-Based Watchman Algorithm (E2-MBWA) to intensification packet delivery ratio of mitigating the Hotspot issue in Wireless Sensor and Actor Networks (WSAN). Hotspot issue mostly causes of network breakdown and decrease of data packet delivery. Therefore, it is required to design a new technique for data packet forwarding that can resolve these issues in the network. In this study, E2-MBWA has introduced, that cope with the layer-by-layer mechanism for data packet forwarding. The proposed algorithm works with the help of the Data Packet Forwarding Algorithm (DFPA) and Watchman Layer Update Mechanism (WLUM). Furthermore, it also rescues the data storage issues, for this, used secondary nods as substitutes. Moreover, proposed technique is compared with some latest baseline’s approaches, for example, Efficient Traffic Load Reduction Algorithm (ETLRA). The analytical energy model is also described for the best health of the network to measure the accuracy level of the Hotspot issue.

With the growing nature of data over the internet, item-based collaborative filtering has become a promising method in the recommendation. The two-step process of item-based collaborative filtering, i.e., computation of similarity among items, and rating prediction using similar items are utilized in recommendation. However, the quality of recommendations after following these steps degrade in sparse datasets. Traditionally, in item-based collaborative filtering, several similarity measures have used to find top-k similar items, and prediction approaches are utilized for rating prediction, and then a top-n list of recommended items is generated. Plenty of work has been done to increase the performance of collaborative filtering using the combination of new/modified similarity measures and the traditional prediction approach. But, traditional prediction approaches also give future scope for improvement in the recommendation system. Therefore, the objective of the paper is to serve a comparative study on conventional prediction approaches for existing best similarity measures. The performance of different prediction approaches is tested with MovieLens datasets using various accuracy metrics.

The concept of wireless body area network (WBAN) is conquered the medical field while considering hospitals, patients, doctors to exchange crucial health data for efficient medical services. Health data includes personal medical information of patients, quality of life, reproductive outcomes, and behavioral information. While transferring health data for medical treatment/review, it is essential to provide proper security during device-to-device communication among WBAN players. Researchers have suggested different health data transmission mechanisms to achieve security, but these schemes are weak against various security attacks, such as modification, session key disclosure, impersonation, replay, man-in-the-middle, and stolen smart card, making crucial information in a susceptible situation specifically for patients. Further, they require high computational resources during WBAN communications. In this paper, we propose a secure and lightweight health authentication and key agreement protocol using low-cost operations. To confirm the robustness of the proposed mechanism, we do security analysis against various security attacks. Based on the computational analysis, the proposed protocol comparatively takes less execution cost, computation time, and power consumption. Moreover, LAKA requires around 57% less communication overhead and 15% less storage cost.

Sports stadiums have a substantial influence on the environmental, urban, and social context. Information and communication technology applications in the international sports stadium increasingly use modern venues and facilities, containing the command and control system, intelligent application of sports facilities, television systems, ticket access control systems, communication systems for event management, contest information systems, etc. There is a high demand for advanced stadium security systems because of the large number of sporting events organized. Hence, in this study, an Artificial intelligence assisted Cyber-Physical System (AI-CPS) has been proposed for security in the network to predict cyber attacks and adversaries. The data has been collected and analyzed, and the proposed AI-CPS model predicts anomaly behaviour in the network. This study deals with the subject of how surveillance and security practices at sports events are organized. Advances in Artificial Intelligence (AI) techniques show potential in enabling cybersecurity authorities to counter the ever-evolving attack posed by an adversary. Here, this paper explores AI’s potential in enhancing cybersecurity resolutions by determining both its strengths and weaknesses. The numerical results show that the suggested AI-CPS model improves an accuracy ratio of 95.6%, a prediction ratio of 97.6%, packet loss of 12.3%, delay ratio of 15.1%, and latency ratio of 11.2% to other existing methods.

Object detection is the key process in any video surveillance application. In case of remote surveillance, it is a necessity to accurately detect the target and transmit the detected data rapidly to main station so that further actions can be taken. This paper concentrates on a framework which uses deep neural network and Internet of Things for target detection and transferring detected information to the cloud at low transmission rates. The detection framework is based on combination of YOLO-Lite which is a simpler version of you only look once (YOLO) detector and spatial pyramid pooling (SPP). When trained on COCO dataset, YOLO-Lite + SPP model runs at a speed of 40 fps with mAP of 35.7% on non-GPU platform. Performance of the same has been analyzed on PASCAL VOC, COCO, TB-50 and TB-100 dataset. On GPU based platform, precision and recall values of 89.79% and 91.67% has been achieved with processing speed of 218 fps. ThingSpeak platform has been used for data reception on cloud. Results in real-time are also demonstrated which proves the efficiency of the anticipated framework and also confirms its suitability for remote video surveillance.

With an advent of social networks, spamming has posted the most important serious issues among the users. These are termed as influential users who spread the spam messages in the community which has created the social and psychological impact on the users. Hence the identification of such influential nodes has become the most important research challenge. The paper proposes with a method to (1) detect a community using community algorithms with the Laplacian Transition Matrix that is the popular hashtag (2) to find the Influential nodes or users in the Community using Intelligent centrality measure's (3) The implementation of machine learning algorithm to classify the intensity of users.The extensive experimentations has been carried out using the COVID-19 datasets with the different machine learning algorithms. The methodologies SVM and PCA provide the accuracy of 98.6 than the linear regression for using the new centrality measures and the other scores like NMI, RMS, are found for the methods. As a result finding out the Influential nodes will help us find the Spammy and genuine accounts easily.

The paper proposes the coupling method based on Bayesian formula and fuzzy recognition by improving the Bayesian classification algorithm. The combination weighting method and relative membership degree are introduced to clarify the weight of evaluation index, and the maximum likelihood classification criterion is used to determine the grade of individual primary school students' physical health evaluation index. This method is applied to physical-health evaluation of six students in Shuangfeng primary school in Jiujiang City. The results show that some student VI is worse in physical health evaluation, the other students are medium or above, which is consistent with the actual sampling results. The method achieves 67% comprehensive accuracy and has certain credibility, which realizes the correct judgment on physical condition of primary school students in probability, and take the initiative to warn the primary school students who have a higher probability of physical hidden-danger. In this case, group physical judgment and personalized effective intervention are carried out on students to promote their healthy development and the overall physical level of primary school students.