DDOS is a type of DOS attack where multiple compromised systems -- which are usually infected with a Trojan -- are used to target a single system causing a Denial of Service (DoS) attack. Victims of a DDoS attack consist of both the end targeted system and all systems maliciously used and controlled by the hacker in the distributed attack. This work proposes a Back Propagation Neural Network (BPNN) prevention engine to flag known and unknown attacks from genuine traffic. We have intensively trained the algorithm with real life cases and attacking scenarios (patterns) based on the existing DDoS tools. The more we train the algorithm with up-to-date patterns (latest known attacks), the further we increase the chances of detecting unknown attacks, considering that over training is avoided. This is because BPNN algorithm learns from scenarios and detects zero-day patterns that are similar to what it was trained with. This design is implemented in the MATLAB environment.
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DDOS Attack, Neural Network, Genetic Algorithm.
