Leveraging Machine Learning for Network Intrusion Detection in Social Internet Of Things (SIoT) Systems

This research investigates the application of machine learning models for network intrusion detection in the context of Social Internet of Things (SIoT) systems. We evaluate Convolutional Neural Network with Generative Adversarial Network (CNN+GAN), Generative Adversarial Network (GAN), and Logistic Regression models using the CIC IoT Dataset 2023. CNN+GAN emerges as a promising approach, exhibiting superior performance in accurately identifying diverse intrusion types. Our study emphasizes the significance of advanced machine learning techniques in enhancing SIoT security by effectively detecting anomalous behaviours within socially interconnected environments. The findings provide practical insights for selecting suitable intrusion detection methods and highlight the need for ongoing research to address evolving intrusion scenarios and vulnerabilities in SIoT ecosystems

A review of research works on supervised learning algorithms for SCADA intrusion detection and classification

Abstract: Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works

Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence

Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented

Strategies Security Managers Used to Prevent Security Breaches in SCADA Systems\u27 Networks

Supervisory Control and Data Acquisition (SCADA) systems monitor and control physical processes in critical infrastructure. The impact of successful attacks on the SCADA systems includes the system\u27s downtime and delay in production, which may have a debilitating effect on the national economy and create critical human safety hazards. Grounded in the general systems theory, the purpose of this qualitative multiple case study was to explore strategies SCADA security managers in the Southwest region of the United States use to secure SCADA systems\u27 networks. The participants comprised six SCADA security managers from three oil and gas organizations in the midstream sector located within this region. Data were collected using semistructured interviews and a review of organizational documents. Four themes emerged from the thematic analysis: (a) the importance of security awareness and workforce security training, (b) the use of technical control mechanisms, (c) the establishment of standard security policies, and (d) the use of access and identity management techniques. A key recommendation is for IT managers to adopt security awareness and workforce security training to strengthen the security chain\u27s most vulnerable link. The implications for positive social change include the potential to prevent consequences such as loss of lives, damage to the environment, and the economy resulting from malicious activities