Detection of DDoS Attack Using Optimized Machine Learning Technique

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Network security is any action an organization takes to prevent malicious use or accidental damage to the network's private data, its users or their devices. The goal of network security is to keep the network running and safe for all legitimate users. Security incident response is one key aspect of maintaining organizational security. A critical task during security incident response is detecting that an incident has occurred. Detection may occur through reports from end-users and other stakeholders in the organization, throughdetection analysis performed or it may be accomplished by using anintrusion detection system. Intrusion Detection (ID) is a challenging endeavor, requiring security practitioners to have a high level of security expertise and knowledge of their systems and organization The demand for the ubiquitous personal communications is driving the development of new networking techniques. Information security has now become a very important aspect of data communication as people spend a large amount of time connected to a network. To improve the security of the data being transmitted various techniques are employed. This chapter presents background, problem discussion, research challenges, objectives and thesis organization. A denial-of-service attack overwhelms a system's resources so thatit cannot respond to service requests. A DDoS attack is also an attack on system's resources, but it is launched from a large number of other host machines that are infected by malicious software controlled by the attacker. There are different types of DoS and DDoS attacks, the most common are TCP SYN flood attack, teardrop attack, smurf attack, ping-of-death attack and botnets.