Open AccessPerformance of EWMA and ANN-based Schemes in Detection of Denial of Service Attack
Author(s) -
Y Minn,
Adnan Hassan
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1096/1/012009
Subject(s) - ewma chart , denial of service attack , computer science , intrusion detection system , scheme (mathematics) , benchmark (surveying) , data mining , constant false alarm rate , artificial neural network , false alarm , alarm , computer security , artificial intelligence , machine learning , control chart , process (computing) , engineering , mathematical analysis , the internet , mathematics , geodesy , world wide web , geography , aerospace engineering , operating system
To ensure successful implementation of cyber-physical systems, industries require computer networks to be protected from malicious attacks. Despite various intrusion detection techniques being proposed by researchers, computer networks are still vulnerable to attacks. As new attacks becoming more complicated, more research is needed to develop more effective and reliable intrusion detection schemes. This study investigated the exponentially weighted moving average control charting technique for detection of malicious denial of service (DoS) trafic and compared it with artificial neural network (ANN) based scheme. Eight features from the Benchmark KDD Cup99 computer network datasets were extracted and their respective ARL 1 and false alarm rate were evaluated. The results suggest that EWMA technique is effective only for selective features and the ANN-based scheme is relatively consistent in handling variability in traffic data. This study opens new opportunities for further investigation to enhance performance of the proposed schemes.