Game Theoretical Method for Anomaly-Based Intrusion Detection

Wang, Zhiyong; Xu, Shengwei; Xu, Guizhi; Yin, Yong; Zhang, Miao; Sun, Dawei

doi:10.1155/2020/8824163

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Artificial intelligence can be trained to automatically learn complex features from data using deep learning algorithms that construct a deep hierarchical network. By adopting the proposed method, new features can be generated rapidly [16].…”

Section: Figure 1 Process Of Dimension Reductionmentioning

confidence: 99%

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Stabbing of Intrusion with Learning Framework Using Auto Encoder Based Intellectual Enhanced Linear Support Vector Machine for Feature Dimensionality Reduction

Kumar¹,

Babu²

2022

RIA

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Using an Intelligent Intrusion Detection System (IIDS) instead of less effective firewalls and other intrusion detection systems can increase network security. The system's overall effectiveness is determined by the accuracy and speed of IIDS' categorization and training algorithms. According to research, Stabbing Intrusion Using Learning Framework (SILF) is an innovative and intelligent method of learning attack features and lowering dimensionality. To improve Enhanced Long Short-Term Memory (ELSTM) prediction accuracy while minimising testing and training time, an auto-encoder approach, which is an efficient learning methodology for feature generation in an unsupervised way is applied. Initial training samples are fed into the classifier to increase the predictability of incursion and classification accuracy. Thus, model efficacy may be achieved linearly while alternative classifier approaches such as conventional SVM, Random Forest (RF), and Naive Bayesian (NB) are investigated and compared. In this research, an efficient Intelligent Intrusion Detection System using Auto Encoder with Enhanced LSTM (IIDS-AE-ELSTM) is proposed for feature dimensionality reduction. Testing and training have shown that the proposed model is more efficient than existing systems in terms of performance measures such as accuracy, precision, recall, and F-measure. A new method to intrusion detection is presented, which increases detection of network intrusions with dimensionality reduction. The Python environment is used in the proposed model to create an efficient dimensionality reduction model for intrusion detection.

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Section: Figure 1 Process Of Dimension Reductionmentioning

confidence: 99%

“…According to the analysis by Wang et al [16], the optimal strategy for anomaly-base intrusion detection systems can be determined using a game-theoretic analysis method. A twostage game model represents the attackers and defenders.…”

Section: Literature Surveymentioning

confidence: 99%

Stabbing of Intrusion with Learning Framework Using Auto Encoder Based Intellectual Enhanced Linear Support Vector Machine for Feature Dimensionality Reduction

Kumar¹,

Babu²

2022

RIA

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“…When the intrusion detector finds misbehavior, it can take appropriate actions immediately so that any harm to the system will be minimized [5]. At present, related research can be divided into misuse-based detection [6] and anomaly-based detection [7] according to detection technology. e misuse-based intrusion detection scheme matches the extracted network traffic with the data traffic, which has the existing type tags.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

Tian

2021

Security and Communication Networks

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Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of the Smart Grid. However, methods based on traditional machine learning are not appropriate for learning high-dimensional features and dealing with the data imbalance of communication traffic in AMI. To solve the above problems, we propose an intrusion detection scheme by combining feature dimensionality reduction and improved Long Short-Term Memory (LSTM). The Stacked Autoencoder (SAE) has shown excellent performance in feature dimensionality reduction. We compress high-dimensional feature input into low-dimensional feature output through SAE, narrowing the complexity of the model. Methods based on LSTM have a superior ability to detect abnormal traffic but cannot extract bidirectional structural features. We designed a Bi-directional Long Short-Term Memory (BiLSTM) model that added an Attention Mechanism. It can determine the criticality of the dimensionality and improve the accuracy of the classification model. Finally, we conduct experiments on the UNSW-NB15 dataset and the NSL-KDD dataset. The proposed scheme has obvious advantages in performance metrics such as accuracy and False Alarm Rate (FAR). The experimental results demonstrate that it can effectively identify the intrusion attack of communication in AMI.

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Robust Enhancement of Intrusion Detection Systems Using Deep Reinforcement Learning and Stochastic Game

Benaddi

Ibrahimi

Benslimane

et al. 2022

IEEE Trans. Veh. Technol.

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Game Theoretical Method for Anomaly-Based Intrusion Detection

Cited by 3 publications

References 27 publications

Stabbing of Intrusion with Learning Framework Using Auto Encoder Based Intellectual Enhanced Linear Support Vector Machine for Feature Dimensionality Reduction

Stabbing of Intrusion with Learning Framework Using Auto Encoder Based Intellectual Enhanced Linear Support Vector Machine for Feature Dimensionality Reduction

An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

Robust Enhancement of Intrusion Detection Systems Using Deep Reinforcement Learning and Stochastic Game

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