Suggested Cyber-Security Strategy That Maximizes Automated Detection of Internet of Things Attacks Using Machine Learning
Dharmesh Dhabliya, Pratik Pandey, Varsha Agarwal, N Gobi, Anishkumar Dhablia, Jambi Ratna Raja Kumar, Ankur Gupta, Sabyasachi Pramanik · Advances in computational intelligence and robotics book series · 2024
The world is experiencing an unparalleled digital revolution because of the advancement of computer systems and the internet. This change is made even more noticeable by the fact that the internet of things is opening up new business options. However, the rise of cyberattacks has severely harmed system and data security. It is true that computer intrusion detection systems are automatically activated. However, due to its conceptual flaws, the security chain is insufficient to counter such attacks. It prevents the full potential of machine learning from being realized. Therefore, a new framework is required to properly safeguard the IT environment. The goal in this regard is to use machine learning methods to build and execute a new strategy for cyber-security. The goal is to improve and maximize the identification of harmful assaults and intrusions in the internet of things. Following the application of this novel strategy on the Weka platform, the authors get a final model that is reviewed and evaluated for performance.