Kewal Krishan Sharma

In this day and age, cyberspace is rife with exposures generated by people - insider attacks, clever social engineering phishing schemes, and lurking advanced persistent threats, and each event not only tests company defences but also challenges the broader security framework of a nation. This chapter draws on an intersection of cyber forensics and human intelligence threat, with a forensic-first approach to provide a foundation to profile, investigate and pursue the people undertaking attacks. You'll observe how behavioural analytics, raw digital evidence, and advanced artificial intelligence work in concert to depict motivation, strategies, and actions intruders will employ, as well as their electronic footprints. Here are the sequential techniques to identify, pin blame, and mitigate these impacts and threats, which rely on forensic toolkits, network traffic examinations, and digital poison extraction, aka malware reverse engineering.

The rise of sophisticated cyber threats necessitates advanced security mechanisms for real-time threat detection and incident response. This chapter explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity to enhance threat detection, anomaly identification, and automated incident response. It examines ML-based anomaly detection techniques, deep learning models for malware classification, and AI-driven intrusion detection systems. Additionally, it highlights the role of AI in automating Security Operations Centers (SOCs) and improving response efficiency. By leveraging AI-driven playbooks and predictive analytics, organizations can proactively mitigate cyber risks. The chapter also discusses challenges, ethical considerations, and future research directions in AI-powered cybersecurity solutions.

The creation of artificial intelligence (AI) in training presents each possibilities and challenges, reshaping traditional coaching roles and methodologies. This paper explores the evolving position of teachers in an AI-improved instructional panorama, emphasizing the significance of human-centric abilities along with empathy, creativity, and critical wondering. While AI can automate administrative tasks and offer personalized learning studies, instructors remain critical in fostering a supportive and tasty mastering surroundings. The take a look at highlights the symbiotic courting among AI equipment and educators, advocating for expert improvement and coverage reforms to maximize the benefits of AI integration. By analyzing contemporary traits and future possibilities, this paper objectives to offer a roadmap for educators navigating this transformative generation.

Affective computing in social robotics represents a big development in human-robot interplay by means of allowing robots to understand, interpret, and reply to human emotions. This paper examines the combination of affective computing technology in social robots that specialize in their programs in healthcare, schooling, and customer service. We discuss diverse methodologies for emotion detection, which include facial popularity, voice analysis, and physiological monitoring, and how those strategies decorate robots' capacity to interact empathetically with human beings. The examine additionally addresses the demanding situations and ethical concerns associated with affective computing, which includes privateness worries and the ability for emotional manipulation. Our findings indicate that affective computing in social robotics has the potential to improve consumer engagement, satisfaction, and normal experience, paving the way for greater intuitive and human-like interactions.

The banking and financial sectors are transforming through the integration of blockchain and automated machine learning (ML). This chapter explores their combined potential to revolutionize traditional financial processes, enhancing efficiencies, transparency, and risk management. Blockchain's decentralized, secure nature ensures tamper-proof record-keeping and reduces fraud, while automated ML enables real-time data analysis, predictive modeling, personalized customer experiences, and efficient decision-making. This synergy streamlines operations, optimizes risk assessment, and offers tailored financial solutions. The chapter also addresses the impact on regulatory compliance, cross-border transactions, identity verification, and lending, while discussing challenges related to scalability, data privacy, and integration, providing a comprehensive understanding of the evolving financial landscape.

Social engineering in social media and online interactions has emerged as a critical concern in the digital age, driven by the increasing interconnectivity and sharing of personal information online. This study is motivated by the growing prevalence of cyberattacks that exploit human psychology rather than technical vulnerabilities, targeting individuals through platforms where they share personal details. By examining various social engineering techniques such as phishing, pretexting, and baiting. This research highlights the methods attackers use to manipulate individuals and the specific vulnerabilities they exploit in social media environments. The study leverages data from recent cases and surveys to analyze the effectiveness of these techniques and the impact on both individuals and organizations. The findings underscore the significant risks posed by social engineering attacks, revealing the psychological underpinnings that make such tactics so effective.