Analysing Cyber Threats: A Comprehensive Literature Review on Data-Driven Approaches

This literature review paper aims to examine and analyze the existing research on prediction models for different types of cyber-attacks. Four key papers have been selected as base this review: "A Prediction Model DoS Attack's Distribution Discrete Probability," "Apriori Viterbi Prior Detection Socio-Technical Attacks," "Cyber Attacks Based Bayesian Network," "Applying Data Science Cybersecurity Network & Events.” An overview value in cybersecurity their function reducing potential threats come first review. The methodology section outlines search strategy used identify relevant selection criteria papers. subsequent sections provide an field, highlighting historical development theories or frameworks related cyber-attack prediction. themes subtopics identified are discussed, focusing discrete probability distribution model attacks, Apriori detecting socio-technical network-based model, application data science analyzing network attacks events. critically evaluates papers, methodologies, findings, limitations. It identifies gaps, controversies, conflicting findings literature, paving way further field. synthesis interpretation integrates from studies, compares various perspectives, discusses implications significance In conclusion, provides a comprehensive analysis cyber-attacks, based highlights strengths weaknesses approaches, offers recommendations future studies. contributes advancement knowledge field aids more effective combat evolving cyber threats.