Detection of known and unknown DDoS attacks using Artificial Neural Networks

TY - JOUR

T1 - Detection of known and unknown DDoS attacks using Artificial Neural Networks

AU - Saied, Alan

AU - Overill, Richard E.

AU - Radzik, Tomasz

PY - 2016/1/8

Y1 - 2016/1/8

N2 - The key objective of a Distributed Denial of Service (DDoS) attack is to compile multiple systems across the Internet with infected zombies/agents and form botnets of networks. Such zombies are designed to attack a particular target or network with different types of packets. The infected systems are remotely controlled either by an attacker or by self-installed Trojans (e.g. roj/Flood-IM) that are programmed to launch packet floods. Within this context, the purpose of this paper is to detect and mitigate known and unknown DDoS attacks in real time environments. We have chosen an Artificial Neural Network (ANN) algorithm to detect DDoS attacks based on specific characteristic features (patterns) that separate DDoS attack traffic from genuine traffic.

AB - The key objective of a Distributed Denial of Service (DDoS) attack is to compile multiple systems across the Internet with infected zombies/agents and form botnets of networks. Such zombies are designed to attack a particular target or network with different types of packets. The infected systems are remotely controlled either by an attacker or by self-installed Trojans (e.g. roj/Flood-IM) that are programmed to launch packet floods. Within this context, the purpose of this paper is to detect and mitigate known and unknown DDoS attacks in real time environments. We have chosen an Artificial Neural Network (ANN) algorithm to detect DDoS attacks based on specific characteristic features (patterns) that separate DDoS attack traffic from genuine traffic.

KW - DDoS attacks

KW - DDoS detectors

KW - Genuine and DDoS patterns

UR - http://www.scopus.com/inward/record.url?scp=84946499243&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2015.04.101

DO - 10.1016/j.neucom.2015.04.101

M3 - Article

AN - SCOPUS:84946499243

SN - 0925-2312

VL - 172

SP - 385

EP - 393

JO - Neurocomputing

JF - Neurocomputing

ER -