A trust-based game theoretical model for cooperative intrusion detection in multi-cloud environments

Abusitta, Adel; Bellaïche, Martine; Dagenais, Michel R.

doi:10.1109/icin.2018.8401625

Cited by 16 publications

(4 citation statements)

References 29 publications

(35 reference statements)

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“…To address this challenge, building trust is still an important and effective method of discovering attacks from both external and internal networks. Followed by this idea, Abusitta et al [5] described a framework to handle trust management in a multicloud environment, which adopted a Bayesian inference-based trust evaluation by considering a node's personal experience. They then used a coalition formation algorithm and a feedback aggregation algorithm (based on the Dempster-Shafer Theory (DST) [198]) to enhance the detection accuracy by aggregating feedback from multiple nodes.…”

Section: E Discussion On Trust-based Cidss Open Challenges and Develo...mentioning

confidence: 99%

Surveying Trust-Based Collaborative Intrusion Detection: State-of-the-Art, Challenges and Future Directions

Meng

Kwok

2022

IEEE Commun. Surv. Tutorials

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Owing to the swift growth in cyber attacks, intrusion detection systems (IDSs) have become a necessity to help safeguard personal and organizational assets. However, with the increasing size of computer networks, it becomes difficult for a stand-alone IDS to identify sophisticated and advanced threats, such as DDoS attack, due to the lack of contextual information and knowledge regarding the deployed environments. To tackle this issue, distributed and collaborative IDSs (DIDSs and CIDSs) are developed, which enable a set of IDS nodes to operate in a collaborative way through exchanging required information. In this survey, we first summarize the state-of-the-art for traditional DIDSs according to the collaboration topology, e.g., centralized, decentralized, and distributed, and discuss major external and internal threats. Because of the distributed nature and various threats, trust is often enforced among various IDS nodes. We then summarize the relevant research on trust-based DIDSs/CIDSs in a chronological order. Also, we highlight challenges and future directions in this field. The main purpose of this survey is to stimulate more research efforts in developing robust and practical trust-based collaborative intrusion detection.

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Section: E Discussion On Trust-based Cidss Open Challenges and Develo...mentioning

confidence: 99%

Surveying Trust-Based Collaborative Intrusion Detection: State-of-the-Art, Challenges and Future Directions

Meng

Kwok

2022

IEEE Commun. Surv. Tutorials

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“…In recent years, with the continuous development of network communication technology, Internet of Things(IoT), cloud computing and other technologies, these network based technologies are deeply rooted in the modern society [1]- [3]. The security situation in cyberspace is becoming increasingly complex.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Intrusion Detection System Based on Feature Selection and Weighted Stacking Classifier

Zhao

Zou

et al. 2022

IEEE Access

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Cyber-attacks occur more frequently with the rapid growth in the Internet. Intrusion detection systems (IDS) have become an important part of protecting system security. There are still some challenges preventing IDS from further improving its classification performance. Firstly, the complexity of high-dimensional features challenges the speed and the performance of the classification for IDS. Secondly, the classification performance of traditional Stacking algorithm can be easily affected by the base classifiers. Tackling both challenges above, we propose a hybrid intrusion detection system based on a CFS-DE feature selection algorithm and a weighted Stacking classification algorithm. To limit the dimension of the features, we deployed the CFS-DE algorithm, which searches for the optimal feature subset. Afterwards, a weighted Stacking algorithm is proposed, which increases the weights of the base classifiers with good training results and drops those base classifiers with bad ones to improve the classification performance. As such, the model enhances the classification efficiency and yielding better accuracy. All experiments in this study were conducted on the NSL-KDD and CSE-CIC-IDS2018 data sets. The results based on KDDTest+ show that our proposed model has accuracy of 87.44%, precision of 89.09%, recall of 87.44% and F1-score of 88.25%. The results based on CSE-CIC-IDS2018 show that our proposed model has accuracy of 99.87%, precision of 99.88%, recall of 99.87% and F1-score of 99.88%. Compared with traditional machine learning models and models mentioned in other papers, out proposed CFS-DE-weighted-Stacking IDS has the best classification performance.

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“…The proposed framework is based on conditional Generative Adversarial Networks (cGANs) [33], special versions of the Generative Adversarial Networks (GANs) [17], which have shown unprecedented success in generating highquality new synthetic data with selective properties. The proposed framework allows the designers of the machine learning systems to estimate the real distribution of the original data pertaining to the targeted population groups (population groups that are victims of biases) through formulating a minimax two-player game [4] [3]. The game is played between two models, which are trained simultaneously, i.e., the Discriminator (Dis) and the Generator (Gen).…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems

Abusitta¹,

Aïmeur²,

Wahab³

2019

Preprint

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In this paper, we propose a new framework for mitigating biases in machine learning systems. The problem of the existing mitigation approaches is that they are model-oriented in the sense that they focus on tuning the training algorithms to produce fair results, while overlooking the fact that the training data can itself be the main reason for biased outcomes. Technically speaking, two essential limitations can be found in such model-based approaches: 1) the mitigation cannot be achieved without degrading the accuracy of the machine learning models, and 2) when the data used for training are largely biased, the training time automatically increases so as to find suitable learning parameters that help produce fair results. To address these shortcomings, we propose in this work a new framework that can largely mitigate the biases and discriminations in machine learning systems while at the same time enhancing the prediction accuracy of these systems. The proposed framework is based on conditional Generative Adversarial Networks (cGANs), which are used to generate new synthetic fair data with selective properties from the original data. We also propose a framework for analyzing data biases, which is important for understanding the amount and type of data that need to be synthetically sampled and labeled for each population group. Experimental results show that the proposed solution can efficiently mitigate different types of biases, while at the same time enhancing the prediction accuracy of the underlying machine learning model.

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A trust-based game theoretical model for cooperative intrusion detection in multi-cloud environments

Cited by 16 publications

References 29 publications

Surveying Trust-Based Collaborative Intrusion Detection: State-of-the-Art, Challenges and Future Directions

Surveying Trust-Based Collaborative Intrusion Detection: State-of-the-Art, Challenges and Future Directions

A Hybrid Intrusion Detection System Based on Feature Selection and Weighted Stacking Classifier

Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems

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