Hybrid Machine Learning and Meta-Heuristic Algorithms for DDOS Attack
Theory and Case Study
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The Distributed Denial of Service (DDoS) attack is a kind of intrusion in a cloud computing environment that severely affects the end user by injecting illegitimate packets of data into internet traffic without the knowledge of the clients. It is a serious problem in cloud computing because the detection and mitigation of intrusion is a challenging task that will affect the functionality of the entire architecture. Numerous cyber-security measures have been carried out to protect the server from attackers or hackers. The traditional cyber-security methods failed to protect the server against s...
The Distributed Denial of Service (DDoS) attack is a kind of intrusion in a cloud computing environment that severely affects the end user by injecting illegitimate packets of data into internet traffic without the knowledge of the clients. It is a serious problem in cloud computing because the detection and mitigation of intrusion is a challenging task that will affect the functionality of the entire architecture. Numerous cyber-security measures have been carried out to protect the server from attackers or hackers. The traditional cyber-security methods failed to protect the server against several external unauthorized traffic. It is important to develop an Intrusion Detection System (IDS) in loT architecture. This book aims to provide detailed literature reviews carried out to investigate various machine learning techniques, neural network models, and optimization algorithms aimed to identify the gap problems and then develop machine learning algorithms to detect the intrusionaccurately and effectively.
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