The Application of Genetic Algorithm to Intrusion Detection in MP2P Network

Lu, Liwei; Zhang, Guoyin; Nie, Jinyuan; Niu, Yingjiao; Yao, Aihong

doi:10.1007/978-3-642-30976-2_47

Cited by 10 publications

(7 citation statements)

References 2 publications

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“…A probabilistic classifier detects anomalies and a hidden Markov model narrows down attacker addresses [13]. Recently, genetic algorithms have been widely used in anomaly detection and misuse detection [14], [15]. Another recent development is using artificial immune systems (AIS) in intrusion detection [16].…”

Section: Related Researchmentioning

confidence: 99%

An Efficient Network Log Anomaly Detection System Using Random Projection Dimensionality Reduction

Juvonen

Hämäläinen

2014

2014 6th International Conference on New Technologies, Mobility and Security (NTMS)

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Abstract-Network traffic is increasing all the time and network services are becoming more complex and vulnerable. To protect these networks, intrusion detection systems are used. Signature-based intrusion detection cannot find previously unknown attacks, which is why anomaly detection is needed. However, many new systems are slow and complicated. We propose a log anomaly detection framework which aims to facilitate quick anomaly detection and also provide visualizations of the network traffic structure. The system preprocesses network logs into a numerical data matrix, reduces the dimensionality of this matrix using random projection and uses Mahalanobis distance to find outliers and calculate an anomaly score for each data point. Log lines that are too different are flagged as anomalies. The system is tested with real-world network data, and actual intrusion attempts are found. In addition, visualizations are created to represent the structure of the network data. We also perform computational time evaluation to ensure the performance is feasible. The system is fast, finds real intrusion attempts and does not need clean training data.

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Section: Related Researchmentioning

confidence: 99%

An Efficient Network Log Anomaly Detection System Using Random Projection Dimensionality Reduction

Juvonen

Hämäläinen

2014

2014 6th International Conference on New Technologies, Mobility and Security (NTMS)

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“…A probabilistic classifier detects anomalies and a hidden Markov model narrows down attacker addresses [8]. Recently genetic algorithms have been widely used in anomaly detection and misuse detection [9], [10]. Artificial immune systems have raised the interest of intrusion detection researchers [11].…”

Section: Anomaly-based Intrusion Detection Systemmentioning

confidence: 99%

Combining conjunctive rule extraction with diffusion maps for network intrusion detection

Juvonen

Sipola

2013

2013 IEEE Symposium on Computers and Communications (ISCC)

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“…For example, in literature [1], a hybrid intrusion detection model combining misuse detection and anomaly detection is proposed. In [2][3][4][5][6][7], other learning algorithms are applied to intrusion detection, such as support vector machine, genetic algorithm, and artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Classification Intrusion Detection Method Based on the Fusion of PCA and LDA

Shen

Zhang

Chen

2019

Security and Communication Networks

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The rapid development of network technology is facing severe security threats while bringing convenience to people. How to build a secure network environment has become an important guarantee for social development. Intrusion detection plays an important role in the field of network security. With the complexity and diversification of networks, intrusion detection systems also need to be constantly improved and developed to match external environmental changes. The innovative work of this paper is as follows: principal component analysis and linear discriminant analysis are used to reduce the dimensionality of the data set, which avoids unnecessary detection content and improves detection efficiency and accuracy. The principal component analysis method, linear discriminant analysis algorithm, and Bayesian classification are combined to construct the PCA-LDA-BC classification algorithm, and the intrusion detection model is established based on this algorithm. The simulation experiment was carried out on the algorithm CICIDS2017 data set proposed in this paper. From the experimental results, it can be analysed that in the intrusion detection of missing data, the improved algorithm is compared with the traditional naive Bayesian classification algorithm, the detection rate is improved, and the false detection rate and the missed alarm rate are reduced. In terms of intrusion detection for various types of attacks, the detection rate, false detection rate, and missed alarm rate have been improved accordingly. It is proved that the algorithm has certain validity and feasibility.

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The Application of Genetic Algorithm to Intrusion Detection in MP2P Network

Cited by 10 publications

References 2 publications

An Efficient Network Log Anomaly Detection System Using Random Projection Dimensionality Reduction

An Efficient Network Log Anomaly Detection System Using Random Projection Dimensionality Reduction

Combining conjunctive rule extraction with diffusion maps for network intrusion detection

A Bayesian Classification Intrusion Detection Method Based on the Fusion of PCA and LDA

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