A study on IDS for preventing Denial of Service attack using outliers techniques

Salim, Marlina; Razak, T. Abdul

doi:10.1109/icetech.2016.7569352

Cited by 8 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DDOS attack has the same purpose but with the difference of using intermediate of multiple networks between the attacker and its target ( [7], [8]). This technique allows the attacker to amplify its attack with orchestrating a simultaneous sending of an excessive number of unwanted computing requests to its victim to overload its computing capacity.…”

Section: Introductionmentioning

confidence: 99%

Predicting DOS-DDOS Attacks: Review and Evaluation Study of Feature Selection Methods based on Wrapper Process

Bouzoubaa¹,

Taher²,

Nsiri³

2021

IJACSA

View full text Add to dashboard Cite

Now-a-days, Cybersecurity attacks are becoming increasingly sophisticated and presenting a growing threat to individuals, private and public sectors, especially the Denial Of Service attack (DOS) and its variant Distributed Denial Of Service (DDOS). Dealing with these dangerous threats by using traditional mitigation solutions suffers from several limits and performance issues. To overcome these limitations, Machine Learning (ML) has become one of the key techniques to enrich, complement and enhance the traditional security experiences. In this context, we focus on one of the key processes that improve and optimize Machine Learning DOS-DDOS predicting models: DOS-DDOS feature selection process, particularly the wrapper process. By studying different DOS-DDOS datasets, algorithms and results of several research projects, we have reviewed and evaluated the impact on used wrapper strategies, number of DOS-DDOS features, and many commonly used metrics to evaluate DOS-DDOS prediction models based on the optimized DOS-DDOS features. In this paper, we present three important dashboards that are essential to understand the performance of three wrapper strategies commonly used in DOS-DDOS ML systems: heuristic search algorithms, meta-heuristic search and random search methods. Based on this review and evaluation study, we can observe some of wrapper strategies, algorithms, DOS-DDOS features with a relevant impact can be selected to improve the DOS-DDOS ML existing solutions.

show abstract

Section: Introductionmentioning

confidence: 99%