Detection of DDoS Attack Using Optimized Machine Learning Technique

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.