Academic literature on the topic 'Study of wireless network security analysis methods and tools'

In terms of security, proactively identifying and mitigating network assaults continues to be essential to the protection of vital systems. This chapter presents a unique method for real-time network intrusion detection that makes use of the resilient gradient boosting technique known as the CatBoost algorithm. Conventional signature-based techniques are inadequate against advanced threats because they frequently fail to adjust to changing assault patterns. Because ML (machine learning), and specifically CatBoost, can handle high-dimensional data, nonlinear relationships, and categorical variables, it appears to be a promising answer. This study uses CatBoost to create an advanced model that can recognize complex patterns in network traffic data. Using a large dataset that includes both attack and regular cases, the system gains the ability to identify minute changes in network behavior that could be signs of impending danger. Through feature importance analysis, one can gain a deeper understanding of the critical elements that contribute to the detection of attacks and, consequently, improve network vulnerabilities. Accuracy, F1-score, recall, and precision are among the measures applied to evaluate the efficacy of the proposed CatBoost-based IDS (intrusion detection system). The CatBoost algorithm is superior to traditional methods in terms of successfully recognizing known and unknown attack types while avoiding false positives, as demonstrated by comparison analyses. In addition, it shows that the model is resilient to evasion tactics used by bad actors, which guarantees strong network infrastructure security. The results highlight the effectiveness of using CatBoost in network security applications and highlight the technology's potential as a preventative measure against online attacks that are constantly changing. More flexible and effective intrusion detection systems are made possible by this research, which advances machine learning-driven strategies for strengthening network defenses.

Computer hackers, both individually and as a group, have been identified as a primary threat to computer systems, users, and organizations. Although hacker groups are complex socio-technical systems, much extant research on hackers is conducted from a technical perspective and at an individual level of analysis. This research proposes a research model composed of five dimensions and their relations in order to study hacker’s social organization in the whole socio-technical context. Based on this model, the researcher applies network analysis methods to disclose the structure and patterns of a significant and complex hacker group, Shadowcrew. Network analysis tools are applied for data processing and data analysis. Three network measures: degree centrality, cognitive demand, and eigenvector centrality, are utilized to determine the critical leaders. Out-degree centrality is employed to analyze the relations among the five dimensions in the research model.

Social media analytics are increasingly incorporated into security practices due to the rise in online criminal and extremist activities. Social media research, however, has not become established in either intelligence practice or academic-based approach. This chapter aims to fill the gap by discussing collection methods and analytical tools for the study of social media data for intelligence and countering violent extremism: social network analysis, sentiment analysis, multilingual analysis, geo-coding, automated entity extraction, semantic search, and multimedia analysis. While technological capabilities of social media analytics are improving rapidly, it needs to be complemented with nuanced perspectives from the social sciences. Understanding of the epistemology of social media and dynamics between the online-offline interaction as well as data access will put practitioners in a better position to reap the benefits of the social media. Attention should be given to train practitioners in relevant technological skills while also incorporating social science knowledge.

Critical structures are increasingly susceptible to cyber threats, leading to the inadequacy of traditional security measures. This study explores the application of deep learning tools aimed specifically at enhancing the security of vital systems, such as power grids and transportation networks. By employing convolutional neural networks and recurrent neural networks, the research conducts an analysis of network traffic data to identify security anomalies. An inclusive dataset, which includes simulated attack scenarios, with workable models. Preliminary findings indicate that the convolutional neural network model achieved a high level of anomaly detection accuracy, ominously surpassing the performance of conventional methods. Furthermore, the recurrent neural network model demonstrated enhanced capabilities in detecting time-sensitive threats, resulting in an extensive reduction in false positives. This chapter focus on future research should focus on examining the integration of deep learning models with security frameworks to fortify resilience against evolving threats

Progress in wireless systems has enabled the creation of low-cost, ergonomic, multi-functional, miniature sensing devices. These devices come together in large numbers creating wireless sensor networks (WSNs), which serve for sensing, collecting, analyzing, and sending detected data to a base station. Problems arise, however, due to the limitations of sensor nodes (SNs), incorrect aggregation of data, redundant and similar data problems, data security and reliability, and some others related to WSN topology. This chapter proposes a novel method for solving WSNs problems to improve cyber-physical systems (CPS). As WSN is of increasing interest in CPSs, the authors put forward an approach for reconstructing WSNs. For traditional methods are not able to cope with such problems, this study takes up rendering WSNs more functional through artificial intelligence (AI) techniques which are considered to develop smart SNs through “intelligent computing,” “deep learning,” “self-learning,” and “swarm learning” ability on the network to improve functionality, utility, and survivability of WSNs.

This section presents the results of the first stage of research aimed at developing the conceptual foundations and experimental model of a robotic sensor network for monitoring emergencies and demining demilitarized zones. The main attention is paid to the creation of new principles, models and methods of functioning of sensor networks that operate on the basis of secure wireless communication protocols. An approach to the synthesis of cryptographic algorithms and noise-resistant control protocols optimized for operation in electronic warfare conditions is proposed. The section considers the theoretical foundations of the system, develops an experimental model of the network, and analyzes its functioning under the influence of external destabilizing factors. The results of the study allow significantly increasing the efficiency and stability of robotic sensor networks in difficult operating conditions. The section will be useful for scientists, engineers, as well as specialists working in the fields of information technology, robotics, and national security. The purpose of this research is Development of conceptual foundations, theoretical models and an experimental model of a robotic sensor network designed for emergency monitoring and demining of demilitarized zones, with an emphasis on the implementation of secure wireless communication protocols, optimization of cryptographic data protection and ensuring network resilience to destabilizing external factors. The research methodology of the first stage is based on an interdisciplinary approach that combines elements of systems analysis, control theory, cryptography, and artificial intelligence. The main focus is on developing a conceptual model of a robotic sensor network capable of operating in conditions of destabilizing external influences, such as electronic warfare and complex landscape conditions. The first stage was the formulation of key requirements for the system, in particular its security, energy, and communication parameters. For this purpose, an analysis of existing methods and technologies, their limitations, and integration capabilities was conducted. Based on this, a theoretical framework was developed that includes mathematical models of the sensor network, data transmission algorithms, and methods for their cryptographic protection. The next stage was the modeling and optimization of processes in the sensor network using machine learning and fuzzy logic technologies. Particular attention was paid to the creation of a noise-resistant control protocol that ensures effective operation of the network even in difficult conditions. In addition, methods for testing statistical characteristics of data transmission protocols were developed, which allow assessing their efficiency and stability. The last step within this stage was the creation of an experimental sample of the sensor network. This sample was tested for compliance with the criteria for resistance to external influences, including radio-electronic countermeasures, which provided a practical verification of the developed models and algorithms. The results of the study became the basis for further improvement of the system and its adaptation to real operating conditions. The scientific novelty of this section presents fundamentally new approaches to building robotic sensor networks focused on monitoring emergencies and demining in demilitarized zones. For the first time, the concept of using noise-resistant wireless communication protocols has been developed and substantiated, which provide high resistance to destabilizing external influences, such as electronic warfare. A new method of verifying the distributed knowledge base of the network has been proposed, based on the principle of the "wandering core", which allows to increase the reliability of the transmitted information. One of the key aspects of scientific novelty is the synthesis of artificial intelligence models for network control and decision-making in real time. The developed algorithms for adapting the system to changing conditions allow to ensure stable operation of the network even in the event of significant loss of nodes or disruption of communication channels. This significantly expands the possibilities of using such systems in difficult conditions, in particular in areas of active enemy countermeasures. New graph models have also been developed to describe the functioning of the sensor network, which provide parallel formal analysis of fuzzy situations. The use of these models allows obtaining a wide range of system stability characteristics, which makes it effective for monitoring and demining tasks under conditions of uncertainty. The results of the study create a new scientific platform for further improvement of wireless communication technologies, artificial intelligence and automated monitoring systems. This contributes not only to the development of theory, but also creates practical prerequisites for the use of such systems in the field of national security and defense. Results. The conceptual foundations of a robotic sensor network that ensures functional stability in difficult operating conditions were developed within the section. The main requirements for the system were formulated, including its energy efficiency, security parameters, and the ability to reliably transmit data in an environment with a high level of radio-electronic interference. Mathematical models were developed to describe the functioning of the sensor network and a noise-resistant communication control protocol was proposed. Data transmission protocols were simulated and tested, which made it possible to assess their efficiency and stability in conditions of destabilizing external influences. Artificial intelligence algorithms were proposed, aimed at increasing the autonomy of the sensor network. The algorithms allow the system to adapt to changes in the external environment and maintain functionality even in the event of partial loss of nodes. Special attention is paid to methods for training the system to increase accuracy and speed of response. An experimental sample of a robotic sensor network was implemented. Tests were conducted to confirm the compliance of the developed models and protocols with the requirements of stability, functional reliability and security in conditions of electronic countermeasures. The system's resistance to various destabilizing factors, such as radio interference, landscape changes and loss of individual network components, was assessed. The proposed solutions demonstrate high reliability for use in emergency monitoring and demining tasks. The results obtained make a significant contribution to the development of modern sensor network technologies and their practical application in the fields of defense, security and robotics. They also create a basis for further improvement and implementation of the proposed solutions.

Abstract: Digital forensics is a critical field within cybersecurity, dedicated to the investigation and analysis of digital evidence in the context of cybercrimes and security incidents. This research article explores the theoretical foundations, methodologies, and practical applications of digital forensics. Through comprehensive analysis, the study identifies key techniques used in digital investigations, significant case studies, and the challenges faced by forensic experts. The findings highlight the importance of robust forensic methods, the role of emerging technologies, and the legal implications of digital evidence. Practical recommendations for forensic practitioners, law enforcement, and policymakers are provided, along with suggestions for future research to further advance the field. Keywords: Digital Forensics, Cyber Evidence, Cybersecurity, Digital Investigations, Data Recovery, Forensic Analysis, Cybercrime, Computer Forensics, Network Forensics, Mobile Device Forensics, Evidence Collection, Digital Evidence, Incident Response and Cyber Threats. References: Digital Forensics of Cybercrimes and the Use of Cyber Forensics Tools to Obtain Digital Evidence. (2021). Advances in Digital Crime, Forensics, and Cyber Terrorism, 45–68. https://doi.org/10.4018/978-1-7998-4162-3.ch003 Kävrestad, J. (2020). Cybercrime, Cyber Aided Crime, and Digital Evidence. Fundamentals of Digital Forensics, 59–62. https://doi.org/10.1007/978-3-030-38954-3_6 Khan, M. Z., Mishra, A., & Khan, M. H. (2020). Cyber Forensics Evolution and Its Goals. Critical Concepts, Standards, and Techniques in Cyber Forensics, 16–30. https://doi.org/10.4018/978-1-7998-1558-7.ch002 Luthfi, A., & Prayudi, Y. (2015). Process Model of Digital Forensics Readiness Scheme (DFRS) as a Recommendation of Digital Evidence Preservation. 2015 Fourth International Conference on Cyber Security, Cyber Warfare, and Digital Forensic (CyberSec). https://doi.org/10.1109/cybersec.2015.31 Marcella, A. J., & Guillossou, F. (Eds.). (2012). Cyber Forensics. https://doi.org/10.1002/9781119203452 Mobile Forensics: Beyond Traditional Sources of Digital Evidence. (2020). Proceedings of the 19th European Conference on Cyber Warfare. https://doi.org/10.34190/ews.20.042 Naidu (Retd.), Gp. C. P. A. (2021). Cyber Crime Intervention- Attacks Analysis for Digital Forensics. Digital Forensics (4n6) Journal. https://doi.org/10.46293/4n6/2021.03.02.02 Othman, R. (2020). Financial Statement Fraud Detection and Investigation in Digital Environment. Critical Concepts, Standards, and Techniques in Cyber Forensics, 187–214. https://doi.org/10.4018/978-1-7998-1558-7.ch011

The traditional signature-based measures of cybersecurity faced growing challenges due to advanced cyber threats. Cyber AI, on the other hand, aided in automating dynamic and adaptive threat mitigation frameworks that can negate both known and unknown risks in real time. This paper explores the application of machine learning (ML), deep learning (DL), and natural language processing (NLP) in the context of AI-powered threat detection in current cybersecurity infrastructures. This paper starts off by identifying gaps around conventional detection tools that relied on static heuristics and rule-based methods, and didn‘t perform well against zero-day attacks, polymorphic malware, or advanced persistent threats (APTs) encounters. Also, integrating AI into these frameworks allows the use of predictive analytics and behavioural modelling to automate counteractive measures that identify, classify, and neutralise exploits. The examined methodologies also include malware classification using supervised and unsupervised learning algorithms, intrusion detection using neural networks, and analysing threat intelligence from phishing emails using NLP. The fast growth of cyber threats in their style, size, and smart tactics has made normal rulebased safety measures less useful. As a result, Artificial Intelligence (AI) is now seen as a game changer in finding dangers; it provides flexible, smart, and quick solutions that can spot and reduce both familiar and unfamiliar risks instantly. This paper reviews in detail AIdriven threat discovery, emphasising the use of machine learning (ML), deep learning (DL), and natural language processing(NLP) methods within current frameworks. The study begins by contextualising where conventional threat detection methods, rule-based systems and static heuristics fall short in combating zero-day exploits. malware and advanced persisten threats (APTs). Contrarily, AI-driven approaches use predictive analytics, behavioural modelling, and automated response mechanisms for anomaly recognition as well as classification of malicious activities to threats neutralisation prior to escalation. Major methodologies covered include: i) the supervised and unsupervised ML algorithms for malware classification; ii) neural networks for intrusion detection; and iii) NLP for threat intelligence analysis from sources like phishing emails or even dark web forums. It also examines recent developments in deep learning, including CNNs for image-based malware analysis and RNNs for identifying structured attack patterns in network traffic. It also addresses the aspect of how it considers generative adversarial networks in the process of simulating attacks on reinforcing defence systems. Also, this piece of work describes the improved outcome achieved from integrating AI with Security Information and Event Management systems, where threat correlation by machines and real-time response to incidents significantly lower detection and remediation time. Significant challenges that AI-based threat detection confronts in spite of its multiple advantages include adversarial attacks meant to mislead the ML models, limited training data leading to scarcity for creating strong systems, and the "black-box" nature of AI decisionmaking, coupled with lack of transparency and accountability. The moral consequences on potential biases in threat categorisation as well as privacy considerations of ubiquitous AI surveillance, are thoroughly examined.

This paper presents a multidisciplinary approach to analyzing data from Telegram for early warning information regarding cyber threats. With the proliferation of hacktivist groups utilizing Telegram to disseminate information regarding future cyberattacks or to boast about successful ones, the need for effective data analysis methods is paramount. The primary challenge lies in the vast number of channels and the overwhelming volume of data, necessitating advanced techniques for discerning pertinent risks amidst the noise. To address this challenge, we employ a combination of neural network architectures and traditional machine learning algorithms. These methods are utilized to classify and identify potential cyber threats within the Telegram data. Additionally, sentiment analysis and entity recognition techniques are incorporated to provide deeper insights into the nature and context of the communicated information. The study evaluates the effectiveness of each method in detecting and categorizing cyber threats, comparing their performance and identifying areas for improvement. By leveraging these diverse analytical tools, we aim to enhance early warning systems for cyber threats, enabling more proactive responses to potential security breaches. This research contributes to the ongoing efforts to bolster cybersecurity measures in an increasingly interconnected digital landscape.

Ubiquitous computing analysis represents an emerging area that implements communication technologies in day-to-day life actions. Ubiquitous computing changes the methods in which people use computers, considering these are involved in everyday aspects. In ubiquitous computing, several types of processes operate automatically in the background and communicate on the user's account. The ubiquitous computing theory is to provide any information for everyone at anytime and anywhere instantly. Mobile ad-hoc Networks are currently a growing technology for the next generation of wireless communication networks. A mobile ad-hoc network can portray as a military or rescue operation network in which a set of mobile nodes are used to send out a mission operation in diplomatic terms. This paper presents common architecture principles of universal systems and analyses significant features in context-aware ubiquitous systems. The main purpose of this work is to define a principle for researchers who are new to ubiquitous computing and want to gain depth on analysis and implementation of a novel method for the ubiquitous computing system in military sectors, in order to contribute towards further research regulations expected into the quality-of-service pledge of ubiquitous computing. The ubiquitous computing is the future of technology related to the internet or smart devices. Moreover, the applicability of technologies found in smartphones, smart TVs and sensors can guide to an increase in strategic capabilities, like sensing and detecting, exchanging and sharing unique real-time data in the military field. This study aims to sketch particular methods by which the learning and teaching methods can be improved. The intelligence of technology advanced in the military sectors along with ubiquitous computing providing a rise in optimization, security, and defence.