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Context-aware network logging is becoming more prevalent for enterprise networks, data centers, and forensics. Monitoring agents are strategically placed to generate log files from the activity of interests from various network points. In a distributed architecture, these agents are scattered across multiple nodes, and they have limited network visibility. Consequently, the resulting logs become fragmented and less perceptible without a unified network context. Besides, aggregating useful information from a diverse management protocol with various languages, syntax styles, and notations requires complex semantic understanding to synthesize these log files. Currently, general-purpose logs like SNMP's logs only provide parametric values at connection levels but lacks incident-specific information. Meanwhile, proprietary services like AWS CloudTrail identify more contexts at the incident-level, but they only work on selected products and infrastructure. This paper proposed a platform-agnostic log decoding and generation algorithm (SAG) for network logging that is semantic aware using context aggregation. Firstly, a protocol-agnostic controller acts as a master to collect logs from agents running in routers, firewall, IDS/IPS, load balancers, managed switches, and servers. From these logs, three traffic models, namely (1) service-activity model (SaM), (2) general-activity model (GaM), and (3) device-activity model (DaM), are trained using artificial neural network (ANN). The log generator then uses the context-filling technique to resolve and construct log entries using a generic sentence template while inferring from these machine-learning models. A sentence smoothing technique is designed to restructure entities in the logs based on traffic directionality for semantic correctness. The experimental result shows that SAG's logs have 1.8 times more contexts resolved for improved log's perceptibility.

The advent of new technologies and the rapid growth of internet users have given birth to the menace of cyber-crime. Unfortunately, it is increasing at an alarming pace. This situation calls for good cyber hygiene behavior to secure digital lives. Cyber hygiene behaviour holds a significant role in terms of cybersecurity across the globe. There is a dire need to understand better the user variations associated with good or bad cyber hygiene behaviour and an improved view of what users do to encourage good cyber hygiene. Cybersecurity attacks are rising due to recent advancements in ICT and the Industrial Revolution 4.0 (IR 4.0). Software development organizations are among the crucial sectors suffering from cybersecurity issues. These organizations are more vulnerable to cyber-attacks because they lack proper cybersecurity culture. Although many initiatives have been taken by academia and industry to address this rising issue, the problem still exists for Software development organizations because good cyber hygiene behaviour is not observed, which is a prerequisite to reduce cyber threats. This study performed a Systematic Literature Review (SLR) of research papers published during 2010 – 2020. The key factors influencing software engineers’ cyber hygiene behaviour intention are extracted from the published literature. The study examined 35 research papers out of 5,270 found from IEEE Xplore, Emerald Insight, SpringerLink, and ScienceDirect databases. The study reviewed number of factors such as the role of personal, social, socio-cognitive, environmental, & technological factors that may individually or collectively influence software engineers’ cyber hygiene behaviour. The positive and negative factors associated with the cyber hygiene behaviour of software engineers are also categorized. This study enriches the understanding of the potential factors related to software engineers’ cyber hygiene behaviours. It provides valuable insights to researchers, software development organizations, governments, and individuals associated with the field of Software Engineering. This research will assist in changing the software engineers’ behaviour towards cyber hygiene, which will ultimately lead to mitigate the issues of Cybersecurity.

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.

Mobile data traffic volume is growing on a large-scale which creates a data traffic overload problem on current cellular networks. To facilitate these growing demands for mobile data traffic volume in next-generation cellular networks such as 5G, the concept of device-to-device (D2D) communication has been introduced. D2D communications have been shown to improve power efficiency, spectrum efficiency, throughput and delay by maximizing the system’s capability in cellular networks through enabling multiple devices to communicate with each other directly. Despite these benefits, D2D communication faces some open research challenges ahead, for instance, security, cooperative multi-hop D2D communications, transmission scheduling and power control and power efficiency. This paper presents a review on the possible threats and the existing security solutions in D2D communications.

Enhancement in wireless network had given users the ability to use Internet without the need of physical connection to the router in order to access the Internet. Almost every Internet of Things (IoT) devices such as smart phone, drones and cameras use wireless technology (Infrared, Bluetooth, IrDA, IEEE 802.11 etc.) to establish multiple types of connections between each other at once. With the flexibility of wireless network, we are able to set up networks that have hundreds to thousands of users, increasing the productivity and profitability in a large margin. This has greatly increased the advancement in IoT networks whereby devices can be connected anywhere. However, the number of network attacks in wireless networks is very alarming. This has become a major setback in IoT networks as IoT network is very sensitive to attacks. For example, a simple DOS attack to the IoT network can make the entire IoT devices to be turned off and not providing the desired services. This can be very damaging if the entire IoT network serves some critical applications or services. Many work has been carried out in IoT security but the success rate to prevent network attack is still not welcoming. One of the ways to improve network security is by using Intrusion Detection System (IDS)) which uses data mining techniques to detect the network attack.

IoT networks mostly rely on wireless mediums for communication, and due to that, they are very susceptible to intrusions. And due to the tiny nature, processing complexity, and limited storage capacities, IoT networks require very reliable intrusion detection systems (IDS). Although there are many IDS types of research available in the literature, most of these systems are suitable for wired network environments, and the benchmark datasets used for these research works are mostly relying on wired datasets such as KDD Cup’99 and NSL-KDD. IoT and wireless networks are distinct in nature as wireless networks give more emphasis on the data link layer and physical layer. These concerns are not given much attention in traditional wired datasets in the body of knowledge. Therefore, in this research, an IDS system is developed using a newly available IoT wireless dataset (NaBIoT) in the literature with the datasets focusing much on the common IoT related attacks, and related layers are taken into consideration. The IDS system developed is evaluated by comparing with various machine learning algorithms in terms of evaluation metrics such as accuracy, F1 score, false positive, and false negative. Moreover, the IoT wireless dataset is compared against the traditional NSL-KDD datasets to evaluate the need for IoT wireless datasets. The NaBIoT datasets show its effectiveness in detecting wireless intrusions. Besides that, the simulation is performed with different combinations of features to conclude that certain features are primary in detecting attacks, and IDS does not require all the features to perform detection. This can reduce the detection time mainly for machine learning and creating the models. This research results have proposed some of the critically important features to be used and eliminating not such important features.

Green energy can be classified into many domains such as green aware applications, green aware protocols, green energy harvesting, and many more. The main focus of this chapter is to analyze green energy in terms of energy aware routing protocols and mainly focusing on wireless sensor networks. So, in this domain, greening the routing protocols with energy efficient algorithms is investigated. Energy efficient routing algorithms can be designed by having self-healing concepts whereby routing algorithms can heal themselves when a routing hole is found or nodes can heal themselves when energy depletion occurs. Having that concept in mind, this chapter investigates both the green energy protocols and the self-healing protocols in the literature.

A new coronavirus-CoV-2 virus has caused disease outbreaks in many countries, and the number of cases is increasing rapidly through transmission from person to person. Clinical acoustics for SARS-CoV-2 patients are crucial to distinguish them from other respiratory infections. Symptomatic sufferers can also have pulmonary lesions on the photographs. A computerized tomography study in patients with suspected COVID-19 pneumonia consists of using a high-resolution approach (HRCT). Artificial intelligence applications need to be useful in categorizing the illness to an awesome severity and integrating the structured file, organized consistent with subjective issues, with objective and quantitative checks of the amount of the lesions. Data indicate the statistical document of the world in trendy. This method, with the aid of a coloring map, identifies floor glass in submission processing and separates it from consolidation and units it as a percentage in respect to the balanced weight loss program.

Green energy infrastructure with the internet technologies relies on five important domains: green machine to machine (M2M), green cloud computing (CC), green data center (DC), green ICT, and green cellular. The ever-increasing demand for cloud computing and heavy dependence on cloud for storage, processing, and applications results in the need for more data centers with high capacity. Power management using wireless sensor networks (WSN) can be a potential solution as there has been a lot of works suing WSN for power management for green buildings, green home, and green farming. The same design can be applied to data centers with modifications to cater for data centers. Since WSN is part of IoT, various IoT-related solutions can be proposed for green data center solutions. A hybrid model that consists of virtualization, cooling systems, and IoT shows energy efficient data center designs. There have been various efforts as such, and this research will present green energy designs and mainly IoT-related initiatives for green-aware data centers.

Providing affordable and quality healthcare is the most burning demand for humanity. It is a fact that more or less half of the world's population resides in rural areas, and a majority of these people are left without the most basic amenities, such as healthcare and education. It is considered difficult to open and manage healthcare facilities in any community using traditional healthcare models for the states with limited resources particularly for developing countries. One of the most popular substitute tools is telemedicine to improve healthcare for underprivileged groups. In telemedicine, information and communication technologies (ICTs) are employed to ensure healthcare at a distance. On the other hand, one of the main problems in developing countries is the quality and cost of healthcare. In health research, telemedicine has become a new hope for eliminating bottlenecks. In this study, the authors have examined what challenges and issues developing countries are facing in implementation of telemedicine; particularly, they examine Pakistan as a case.

Keeping the recent history of Anti Narcotic Force with accused records has been a growing concern for the agency. Nature of Addiction (NA) has witnessed a number of cases which resulted in death just because of mismanagement of records files. The author concludes the result of the software with a greater potential for new features. The author, therefore, considered also the possibility to add a secure way to authenticate the user. This chapter applies the idea of emancipation to apprehend the usage of management system amongst crime investigation in city and rural regions related to ICT and era; the author examines the Karachi Provincial Office for the Nature of Addiction (NA) and centers the product on keeping records of the accused and their employees' information through Excel sheets and paper files, which are managed by the mill to handle and keep each of the records by ordering.

Information and communication technology (ICT) integration in the education sector plays an important role in the socio-economic growth of any country. The developing countries are facing hindrances in effective implementation of ICT in the education sector. Pakistan is one of the developing countries of Asia and facing challenges in ICT integration in the education sector. This study aims to identify the challenges being faced in the implementation of ICT in the education sector of Pakistan at all levels of education. The second objective of this study is to explore the best practices to overcome the identified challenges. The PRISMA statement has been used to achieve the objectives of the study. The qualitative synthesis and descriptive analysis are used to demonstrate the findings of this systematic literature review. The findings of this study highlight the need to improve the ICT infrastructure to facilitate students with digital online resources, economical solutions, technical facilities, and self-efficacy.

This chapter applies the idea of emancipation to apprehend the usage of cellular telephones amongst lady entrepreneurs in city and rural regions related to ICT and era. Data were accumulated via questionnaire and focused on institution discussions and were analysed by SPSS. The findings found that the majority of women from different countries were micro-entrepreneurs. In traditional operations such as marketing and distribution, data inflow, customer service delivery, compounded industry strategies, and revenues, woman marketers earned directly, which promoted improved profitability and organizational change. The look came to conclude that successful girls are beginning something new with a small investment instead of being inactive and searching out a process. It could be best for graduate owners to start their own commercial enterprises in partnership with NGOs and humanitarian resource corporations.

Device-to-device (D2D) is a kind of communication technique where it allows direct communication between user equipment without the involvement of network infrastructure. 5G network is being the cellular network trends nowadays where it enables D2D communication with a huge device connectivity and it is expected to be able to deliver a more reliable and faster network throughput such as high definition video streaming, online gaming and multimedia downloading. This paper presents a formation of multi-hop in D2D communications for more reliable network link and brings more benefits compare to single hop communication systems. It can be enhanced furthermore with network coding technique. Network performance in terms of packet loss rate is presented. The simulation results show that network coding can improve the network performance with low packet loss rate.

Generally, a two-factor authentication system verifies the user using three credentials in two steps. These three credentials are a password, a private key assigned to the user in his mobile app and a fingerprint. All these three credentials fell into different categories which are “Something you know,” “Something you have,” and “Something you are.” Credentials from these three categories have been used for people to prove who they claimed to be even before the days of the Internet. This application system combines credentials from all these three categories in order to provide a secure way of authentication that is hardly able to be compromised by hackers. This system uses a complex algorithm in order to protect the private key which is assigned to the user during registration. A unique private key is assigned to the user in the mobile app. This key plays an extremely significant role in this system because of its variable for every time-based one-time password, TOTP to be unique. This system requires Android 6.0 APIs for fingerprint authentication in order to secure the TOTP generation. The TOTP algorithm implemented in this system has the capability to generate a unique string of code every 30 s.

Nowadays, mobile devices are used in communication to send and receive sensitive data. To protect user’s personal and sensitive data, PIN, pattern, and passwords are the most commonly used techniques, but these techniques have several drawbacks such as password or PIN can be stolen or forgotten due to memory loss. In this regard, biometric approaches provide more security than these traditional authentication approaches (PIN/password). Biometric techniques are divided into physiological and behavioral techniques. Certain physiological techniques are being used nowadays like fingerprint and iris scanner, but these techniques need special hardware that is not present in every device, also these techniques can also be compromised. This paper presents a short survey of different behavioral authentication methods based on the basic sensors that are present in all existing smartphones. Moreover, in this paper, different challenges and open issues for future directions are also discussed to obtain higher accuracy, reliability, and universality.

The internet usage for commercial and public services has significantly increased over these decades to where security of information is becoming a more important issue to society. At the same time, the number of network attacks in IoT is increasing. These include distributed denial of service (DDOS) attack, phishing, trojan, and others that will cause the network information to not be secure. With the revolution in Industry 4.0 and IoT being the main asset in the Fourth Industrial Revolution, many companies spend thousands or millions to protect their networks and servers. Unfortunately, the success rate to prevent network attack is still not welcoming. The attacks on physical layers, such as jamming, node tampering; link layer, such as collision, unfairness, battery exhaustion; network layer, such as spoofing, hello flood, Sybil attack, wormhole, DOS, DDOS; transport layer, such as flooding, de-synchronization; application layer, such as flooding, are alarming. This chapter reviews attacks and countermeasures.

Weather forecasting is a significant meteorological task and has arisen in the last century from a rational and revolutionary point of view among the most difficult problems. The authors are researching the use of information mining techniques in this survey to measure maximum temperature, precipitation, dissipation, and wind speed. This was done using vector help profiles, decision tree, and weather data obtained in Pakistan in 2015 and 2019. For the planning of workbook accounts, an information system for meteorological information was used. The presentations of these calculations considered using standard implementing steps as well as the estimate that gave the best results for generating disposal rules for intermediate environment variables. Likewise, a prophetic network model for the climate outlook program, contradictory results, and true climate information for the projected periods have been created. The results show that with sufficient information on cases, data mining strategies can be used to estimate the climate and environmental change that it focuses on.

An intrusion detection system (IDS) works as an alarm mechanism for computer systems. It detects any malicious activity that happened to the computer system and it alerts an alarm message to notify the user there is malicious activity. There are IDS that are able to take action when malicious or anomalous networks are detected, which include suspending the traffic sent from suspicious IP addresses. The problem statement for this project is to find out the most accurate machine learning algorithm and the types of IDS with different placement strategies. When it comes to the deployment of a wireless network, IDS is not as easy a task as deploying a traditional network IDS. There are many unexpected complexities of the problem of reliable intrusion detection in a wireless network. The motivation of this research is to find the most suitable classification techniques that are able to increase the accuracy of an IDS. Machine learning is useful for the upcoming trend; it provides better accuracy in the detection of malicious traffic.

Mobile phones or smartphones are rapidly becoming the primary and essential communication device in people’s lives that cannot be replaced by other communication devices, because of the portability, the size, and the multifunctionality provided in it. Nowadays, mobile phones are being used in almost every aspect of life and work as your personal assistant e.g. meeting reminders. It monitors your daily activities and gives suggestions accordingly e.g. health applications. With the help of smartphone online transactions can be performed, meetings can be conducted via video conferencing. It contains your personal files, emails, bank information, and your social network accounts record. It also contains information related to the credentials, which are stored in its memory. Despite all the benefits, there is a great threat to private information, in the case when mobile is snatched, misplaced, or in the use of an unauthorized user. An attacker can steal the user’s private data and can misuse it without the owner’s consent. Although the traditional authentication methods are in use, they have several limitations. In this paper, an authentication system is proposed that uses a combination of user behavior and touchscreen which can seamlessly capture the user’s tapping behavior. The information obtained from the touch screen sensors reflects the unique tapping behavior of each user. Moreover, machine learning is utilized to perform the classification for the user’s authentication.

In any democratic nation, electoral processes pave the way to democratic governance. In regards to this, a fully digitized online voting system should be introduced, and governments should invest efforts in studying the possibilities in adopting online voting system to curb the issues mentioned above. In that sense, any user should be allowed to cast his/her vote from any remote location, and the processes should be held in full confidentiality without any intervention or possible vulnerabilities. Therefore, this chapter investigates how governments can learn various initiatives and provide funding to further conduct research and implement such methodologies. The chapter begins by exploring some traditional and current practice of electoral system and further embarks into a review of secured online voting systems that has been proposed by the body of knowledge. The chapter then presents how some countries have adopted this secured online voting systems and giving an avenue to secured digital electoral governance.

This chapter intends to provide a review of cooperation between public and private sectors towards cybersecurity governance. With the partnership, government can have confidence towards the safety and protection of their national critical digital infrastructure. The goal of this chapter is achieved by analyzing some of the cybersecurity frameworks adopted by the developed and developing nations. The analysis is further carried out by investigating the public-private policy initiatives in their national cybersecurity framework. The chapter also investigates the effectiveness of the National Institute of Standards and Technology Cybersecurity Framework (NIST) adopted by the US government.

Human beings have a knack for errors. Counter-effective actions rendered to specify and rectify such errors in a minimum period of time are required when effectiveness and swift advancement depends on the capability of acknowledging the faults and errors and repair quickly. The software as audit module application in IT complaint is in review in this commentary as is another significant instrument created in the field of data analysis that digs deep into quickly and successfully assessing the imprecisions or grievances identified by the users in a certain company. The target of this study is to evaluate the statistical significance in relationship between client reporting attitude and client reliability and to evaluate the impact of strong responsiveness on client reliability, to measure the statistically noteworthy effect of client grievance conduct on service quality, and to test the impact of service quality on client dedication.

Raising awareness on social engineering is becoming more essential. When we are connected to the internet, we are at the risk of becoming victims of numerous attacks. It is very difficult to protect ourselves from all of these threats, but the least we could do is not jeopardize our own safety. In order to achieve this, the need to have substantial knowledge on social engineering techniques and how to safeguard one from being victimized are mandatory. This research presents the overview of social engineering attacks. This plays a crucial role in supporting victims of cyber assaults.

Transportation is an essential part of human activity that allows people to move from one place to other places. Modern transportation systems refer to intelligent transportation systems (ITS). It has been proven that information and communication technology (ICT) has a significant role to improve the quality and security transportation services. ITS can be described as an integration of computer, electronics, and communication technologies and management strategies that can increase the efficiency and safety of transportation. The application is a platform that manages the vehicles, drivers, passengers, road operators, and managers in an environment that is suitable for everyone to communicate and interact. This chapter presents the history of transportation systems and the role of ICT to bring ITS in different transportation modes. The main motivation of the chapter is to survey the various technology-enabled ITS systems that have been in place, analyzing their strengths and providing recommendations for such ITS systems in developing countries to improve the governance.