ON CYBER SECURITY MODELS USING LAD

Abstract

The Internet plays a major role in every field whether it is education, finance, business, entertainment, industry, etc. The widespread use of the Internet has also increased security violations and cyber threats. Attackers try to access sensitive information of users by using fake emails, websites, etc. The use of unsecured networks for performing transactions and other communication makes it easier for hackers to access the sensitive information that is being transmitted over these networks. The attackers launch Denial of Service attacks to disrupt the services affecting users across the world. They not only launch false requests but also send malware files containing viruses, worms, Trojan horses, etc. to corrupt the systems and to exploit the vulnerabilities in the system for extracting confidential information. Thus, the Internet has brought both advantages and disadvantages with it. With the use of technology in our day-to-day life, there also comes a challenge to keep our data and information safe and detect network attacks in real-time to avoid any damage to the system. Intrusion detection is a crucial aspect of modern computer security that aims to identify and respond to unauthorized access attempts to a network or system. In recent years, various machine learning models have been proposed for intrusion detection, with the goal of improving their accuracy and efficiency. Research in this field is focused on developing algorithms that can accurately distinguish between normal and malicious behavior in a network. These models include supervised learning techniques such as decision trees, random forests, and neural networks, as well as unsupervised techniques such as clustering and anomaly detection. Researchers are also exploring the use of hybrid approaches that combine multiple techniques to improve the overall performance of intrusion detection systems. The goal of this research is to create robust and scalable intrusion detection models that can be applied in real-world environments and effectively protect against cyber attacks.