Securing the Future: AI-Driven Cyber Defenses in a Hyperconnected World
Sriharsha Daram · International Journal of Computer Trends and Technology · 2024
In the current world, where most activities entail the use of technology, the increasing challenge of fighting cyber threats is complex. The number of devices, along with the usage of the cloud and IoT, has skyrocketed within the past years, and this has given a long list of opportunities for hackers. At the same time, conventional security measures fail to adapt to the speed of the process. This paper discusses the change Artificial Intelligence (AI) brings to present-day cybersecurity measures. AI has the ability to prevent cyber threats by using big data analytics, ML, NLP, and deep learning techniques to identify patterns and trends, making it capable of a proactive defense from developing threats in consideration of the ever-evolving threat environment. The first part exposes the evolution of cyber threats and threats, describing how current security measures are enough to combat complex attacks such as APTs, ransomware, and zero-day exploits. Subsequently, the paper reflects upon the development of AI integration in cybersecurity, which started with using AI in malware detection and signature-based cybersecurity systems. It propelled itself into AI-driven threat intelligence and behavioral analytics and AI-driven automated incident response. Under the methodology area, the authors explain how they adapted various AI-based cybersecurity measures and how they address data gathering, preparation, model identification, model building, and model deployment. Examples of how AI has helped to reduce breaches and the time required to respond to incidents in areas like finance, healthcare, and defense will be used. The study’s findings and analysis shall focus on parameters like the overall detection rate, observed false alarm rates, and time taken to react. In the final section, the prospects for applying AI in relation to cybersecurity and its further development will be reviewed, focusing on such aspects as ethically relevant ones and the use of explainability of AI systems (XAI) to develop more transparent and trustworthy systems.