Detecting Constant Low-Frequency Appilication Layer Ddos Attacks Using Collaborative Algorithms

— A DDoS (i.e., Distributed Denial of Service) attack is a large scale distributed attempt by malicious attackers to fill the users’ network with a massive number of packets. This exhausts resources like bandwidth, computing power, etc.; User can’t provide services to its clients and network performance get destroyed. The methods like hop count filtering; rate limiting and statistical filtering are used for recovery. In this paper, we explored two new information metrics which have generalized information about entropy metric and distance metric .They can detect low-rate of Distributed Denial of Service i.e., DDoS attacks by measuring difference between the legitimate traffic and the attack traffic. The generalized entropy metric information can detect the attacks on several hops before than the traditional Shannon metric. The proposed information about the distance metric outperforms the popular Kullback–Leibler divergence approach as it has the ability to perfectly enlarge the adjudication distance and gets the optimal detection sensitivity. Further the IP trace back algorithm can find all attackers as well as their attacks through local area networks (LANs) and will delete the attack traffic.