Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

Abstract

Current network intrusion detection systems (NIDS) lack the adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online GMMs are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines (SVM). The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process which uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO and SVM-based algorithm effectively combines the local detection models into the global model in each node: the global model in a node can handle the intrusion types which are found in other nodes, without sharing the samples of these intrusion types.

BiometricAccessFilter: A Web Control Access System Based on Human Auditory Perception for Children Protection

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“…The combination is built by using an algorithm based on particle swarm optimization (PSO) and support vector machines (SVMs). A distribution intrusion detection framework is proposed as a local parameterised detection model wherein each node of the online Adaboost algorithm performs separately [8].…”

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“…Adaboost, online GMM (Gaussian Mixture Model), PSO and SVM based algorithms were used. [27] In 2014, Fung and Boutaba published the book "Intrusion Detection Networks -A Key to Collaborative Security". The basis of cyber-attacks, IDS types and important IDSs in the literature are mentioned.…”

Intelligent Intrusion Detection Techniques for Secure Communications in Wireless Networks: A Survey

“…A number of researchers are addressing these issues by proposing new mechanisms for intrusion detection and security. Hu et al (2014) developed two adaboost-based real-time intrusion detection algorithms for detecting attackers in distributed computer networks. Muthurajkumar et al (2015) proposed an intelligent technique for detecting malware in cloud.…”