Network programming and mining classifier for intrusion detection using probability classification

Prasenna, P.; Kumar, Rajeev; Ramana, Attili Venkata; Devanbu, A.

doi:10.1109/icprime.2012.6208344

Cited by 17 publications

(15 citation statements)

References 9 publications

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“…This dataset is still the most trustful and credible public benchmark dataset [53,54,55,56,57,58,59] for evaluating network intrusion detection algorithms. In the dataset, 41 features including 9 categorical features and 32 continuous features are extracted for each network connection.…”

Section: Methodsmentioning

confidence: 99%

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

Gao

Wang

et al. 2014

IEEE Trans. Cybern.

146

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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.

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Section: Methodsmentioning

confidence: 99%

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

Gao

Wang

et al. 2014

IEEE Trans. Cybern.

146

View full text Add to dashboard Cite

show abstract

“…In 2012, P. Prasenna et al [19] suggested that in conventional network security simply relies on mathematical algorithms and low counter measures to taken to prevent intrusion detection system, although most of this approaches in terms of theoretically challenged to implement. Authors suggest that instead of generating large number of rules the evolution optimization techniques like Genetic Network Programming (GNP) can be used .The GNP is based on directed graph.…”

Section: Literature Surveymentioning

confidence: 99%

An approach for efficient intrusion detection for KDD dataset: A survey

Sharma¹,

Gaur²

2016

IJATEE

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“…Emp irical results shows that selected reduced attributes give better performance to design IDS that is efficient and effective for network intrusion detection. Prasenna P et al [76] has proposed network programming and mining classifier for intrusion detection using probability classification. This method can sufficient to evaluate misuse and anomaly detection.…”

Section: Anomaly and Misuse Detectionmentioning

confidence: 99%

Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets

Azad

Jha²

2013

IJITCS

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Abstract-In the era of info rmation and communicat ion technology, Security is an important issue. A lot of effort and finance are being invested in this sector. Intrusion detection is one of the mos t prominent fields in this area. Data min ing in network intrusion detection can automate the network intrusion detection field with a greater efficiency. This paper presents a literature survey on intrusion detection system. The research papers taken in this literature survey are published fro m 2000 to 2012. We can see that almost 67 % of the research papers are focused on anomaly detection, 23 % on both anomaly and misuse detection and 10 % on misuse detection. In this literature survey statistics shows that 42 % KDD cup dataset, 20 % DA RPA dataset and 38 % other datasets are used by the different researchers for testing the effectiveness of their proposed method for misuse detection, anomaly detection or both.

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Network programming and mining classifier for intrusion detection using probability classification

Cited by 17 publications

References 9 publications

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

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

An approach for efficient intrusion detection for KDD dataset: A survey

Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets

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