Predicting Future Cyber Threats: Analysing Trends and Predicting Future Cybersecurity Challenges
Richard Aggrey -, Emmanuel Adjirachor -, Bright Ansah Adjei -, Nana Adwoa Konadu Dsane -, Karl Osei Afoduo - · International Journal For Multidisciplinary Research · 2024
As the sophistication of cyber threats grows, we move from traditional reactive cybersecurity to the implementation of predictive cybersecurity. This paper explores how more advanced technology, integrated systems, and state-sponsored attacks have created a complicated threat landscape requiring predictive threat management. An organisation can foresee future vulnerabilities by analysing trends such as phishing, ransomware, and advanced persistent threats (APTs) through logical correlation. This paper discusses the adoption of predictive models - both quantitative and qualitative methods - and using Artificial Intelligence (AI) and Machine Learning (ML) in real-time threat detection and situational awareness. For instance, the SolarWinds supply chain and the Ukraine power grid attacks show why predictive analysis is needed to deal with the risks. Due to ethical considerations, such as data bias or fairness, developing reliable predictive models is critical. To effectively predict threats, industries, governments, and researchers must collaborate and establish a resilient cybersecurity framework anticipating what is coming next.