Securing Networks: Machine Learning Models for Cyber Attack Identification

Abstract

The modern world increasingly depends on digital platforms for everyday life. As online activities grow, the digital space is being utilized more extensively. This expansion has led to a rise in cyber threats and crimes. "cyber hazard" refers to illegal activities carried out over the Internet. Over time, cybercriminals have devised more sophisticated techniques to breach security systems, and traditional methods often fail to detect complex or zero-day attacks. Machine learning has become a crucial tool for identifying and mitigating cyber risks. This paper examines several machine learning techniques to address some of the most significant cyber threats. Approaches such as deep belief networks, decision trees, and support vector machines are assessed for effectiveness. The study focuses on their performance in detecting spam, identifying intrusions, and recognizing malware using well-established benchmark datasets. While advancements in computing and communication technologies offer substantial benefits to individuals and organizations, they also introduce new challenges, particularly in securing sensitive data and ensuring its availability. Tackling these challenges is vital to minimizing digital threats. Cybersecurity concerns have prompted collaborative efforts among governments, organizations, and cybersecurity experts to address the rising digital threat. These threats can potentially undermine national security, leading to the development of Intrusion Detection Systems (IDS) to strategically combat and neutralize such attacks.