Proceedings ArticleDOI
A system approach to network modeling for DDoS detection using a Naìve Bayesian classifier
R. Vijayasarathy,S. V. Raghavan,Balaraman Ravindran +2 more
- pp 1-10
TLDR
The approach to a carefully engineered, practically realised system to detect DoS attacks using a Naìve Bayesian(NB) classifier is described, which includes network modeling for two protocols - TCP and UDP.Abstract:
Denial of Service(DoS) attacks pose a big threat to any electronic society. DoS and DDoS attacks are catastrophic particularly when applied to highly sensitive targets like Critical Information Infrastructure. While research literature has focussed on using various fundamental classifier models for detecting attacks, the common trend observed in literature is to classify DoS attacks into the broad class of intrusions, which makes proposed solutions to this class of attacks unrealistic in practical terms. In this work, the approach to a carefully engineered, practically realised system to detect DoS attacks using a Naive Bayesian(NB) classifier is described. The work includes network modeling for two protocols - TCP and UDP.read more
Citations
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Journal ArticleDOI
Botnet in DDoS Attacks: Trends and Challenges
TL;DR: This survey presents a comprehensive overview of DDoS attacks, their causes, types with a taxonomy, and technical details of various attack launching tools.
Journal ArticleDOI
A Survey of Distance and Similarity Measures Used Within Network Intrusion Anomaly Detection
TL;DR: An overview of the use of similarity and distance measures within NIAD research is presented and a theoretical background in distance measures is provided and a discussion of various types of distance measures and their uses are discussed.
Journal ArticleDOI
Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems
Eduardo Viegas,Altair Olivo Santin,André Luiz Pereira de França,Ricardo P. Jasinski,Volnei A. Pedroni,Luiz S. Oliveira +5 more
TL;DR: It is demonstrated that a hardware (HW) implementation of network security algorithms can significantly reduce their energy consumption compared to an equivalent software (SW) version.
Journal ArticleDOI
Comprehensive Review of Artificial Intelligence and Statistical Approaches in Distributed Denial of Service Attack and Defense Methods
Bashar Ahmed Khalaf,Salama A. Mostafa,Aida Mustapha,Mazin Abed Mohammed,Wafaa Mustafa Abduallah +4 more
TL;DR: This review paper focuses on the most common defense methods against DDoS attacks that adopt artificial intelligence and statistical approaches and classifies and illustrates the attack types, the testing properties, the evaluation methods and the testing datasets that are utilized in the methodology of the proposed defense methods.
Journal ArticleDOI
Security Data Collection and Data Analytics in the Internet: A Survey
TL;DR: This paper surveys existing studies about security-related data collection and analytics for the purpose of measuring the Internet security and proposes several additional requirements for security- related data analytics in order to make the analytics flexible and scalable.
References
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Proceedings ArticleDOI
A Novel Model for Detecting Application Layer DDoS Attacks
Yi Xie,Shun-Zheng Yu +1 more
TL;DR: This paper considers sophisticated attacks that utilize legitimate application layer HTTP requests from legitimately connected network machines to overwhelm Web server and proposes a counter-mechanism based on Web user browsing behavior to protect the servers from these attacks.
Book ChapterDOI
Defending DDoS attacks using hidden Markov models and cooperative reinforcement learning
TL;DR: A novel DDoS detection approach based on Hidden Markov Models (HMMs) and cooperative reinforcement learning is proposed, where a distributed cooperation detection scheme using source IP address monitoring is employed.
Journal ArticleDOI
From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective
TL;DR: It is concluded that the BNs performed equivalently well in detecting network attacks and the BN built using the proposed feature set has less features but the performance was comparable to BNs built using other feature sets generated by the two algorithms.
Proceedings ArticleDOI
Mark-aided distributed filtering by using neural network for DDoS defense
Yang Xiang,Wanlei Zhou +1 more
TL;DR: The experimental results show that the approach to find the network anomalies by using neural network, deploy the system at distributed routers, identify the attack packets, and then filter them can perform well in filtering DDoS attack traffic precisely and effectively.
Book ChapterDOI
A new DDoS detection model using multiple SVMs and TRA
TL;DR: A new DDoS detection model based on multiple SVMs (Support Vector Machine) in order to reduce the false positive rate and employ TRA (Traffic Rate Analysis) to analyze the characteristics of network traffic for DDoS attacks.