Cyber Crime and Digital Security: AI-Enhanced Mitigation Strategies

Abstract

These papers address the growing issue of cyber threats, and the methods used to combat them, emphasizing the dual role of technology in both perpetrating and defending against cybercrime. The research explores a range of cyber-attacks, their impacts, and various mitigation strategies. A key theme is the increasing importance of Machine Learning (ML) and Artificial Intelligence (AI) in bolstering cybersecurity, alongside the need for strong security protocols and collaborative efforts. The papers cover that cybercrime, defined as illegal activities using computers and the internet, is a growing concern with diverse perpetrators. A wide range of cyber-attacks include data breaches, malware, network attacks, Denial-of-Service (DoS), phishing, ransomware, SQL injections, Cross-Site Scripting (XSS), and other forms of cybercrime. Significant negative impacts of cyber threats include financial losses, reputational damage, operational disruptions, compromised data, and psychological harm. Cybersecurity strategies include ML and AI for threat detection and automated responses, deep learning for early detection, data encryption, multi-factor authentication, Intrusion Detection Systems (IDS), transaction monitoring, and incident response plans. Specific vulnerabilities in the financial sector and portfolio management are susceptible to attacks like phishing and ransomware. Emerging technologies, such as blockchain for secure transactions and real-time threat intelligence sharing, are gaining importance.
The papers highlight the necessity of a comprehensive approach combining technological solutions, robust security measures, and proactive collaboration to tackle the evolving landscape of cyber threats.