Modeling, Detecting, and Mitigating Threats Against Industrial Healthcare Systems: A Combined Software Defined Networking and Reinforcement Learning Approach

Radoglou-Grammatikis, Panagiotis; Robolos, Konstantinos; Sarigiannidis, Panagiotis; Argyriou, Vasileios; Λάγκας, Θωμάς; Sarigiannidis, Antonios; Goudos, Sotirios K.; Wan, Shaohua

doi:10.1109/tii.2021.3093905

Cited by 51 publications

(28 citation statements)

References 20 publications

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“…Wang et al [ 3 ] proposed the framework for cross-silo Federated Learning with Local Differential Privacy (LDP) mechanism, which can provide strong data privacy protection while still retaining user data statistics to preserve its high utility. Radoglou-Grammatikis et al [ 4 ] applied machine learning and reinforcement learning to the modeling of intrusion detection and prevention system, which effectively improved the detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

Personality Privacy Protection Method of Social Users Based on Generative Adversarial Networks

Sui

Wang

Zheng

et al. 2022

Computational Intelligence and Neuroscience

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Obscuring or otherwise minimizing the release of personality information from potential victims of social engineering attacks effectively interferes with an attacker’s personality analysis and reduces the success rate of social engineering attacks. We propose a text transformation method named PerTransGAN using generative adversarial networks (GANs) to protect the personality privacy hidden in text data. Making use of reinforcement learning, we use the output of the discriminator as a reward signal to guide the training of the generator. Moreover, the model extracts text features from the discriminator network as additional semantic guidance signals. And the loss function of the generator adds a penalty item to reduce the weight of words that contribute more to personality information in the real text so as to hide the user’s personality privacy. In addition, the semantic and personality modules are designed to calculate the semantic similarity and personality distribution distance between the real text and the generated text as a part of the objective function. Experiments show that the self-attention module and semantic module in the generator improved the content retention of the text by 0.11 compared with the baseline model and obtained the highest BLEU score. In addition, with the addition of penalty item and personality module, compared with the classification accuracy of the original data, the accuracy of the generated text in the personality classifier decreased by 20%. PerTransGAN model preserves users’ personality privacy as found in user data by transforming the text and preserving semantic similarity while blocking privacy theft by attackers.

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Section: Related Workmentioning

confidence: 99%

Personality Privacy Protection Method of Social Users Based on Generative Adversarial Networks

Sui

Wang

Zheng

et al. 2022

Computational Intelligence and Neuroscience

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“…The Internet of Medical Things (IoMT) is on the rise, and threats against certain systems and protocols is closely following [199]. Researchers in [199] have introduced an intrusion detection and prevention system to automatically reduce and mitigate the threats using ML techniques. They explain that this system created reduces the attack surface and helps detect multi layer cyber attack [199].…”

Section: Ai-assisted Threat Managementmentioning

confidence: 99%

“…Researchers in [199] have introduced an intrusion detection and prevention system to automatically reduce and mitigate the threats using ML techniques. They explain that this system created reduces the attack surface and helps detect multi layer cyber attack [199].…”

Section: Ai-assisted Threat Managementmentioning

confidence: 99%

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Hillier¹,

Karroubi²

2022

Preprint

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The threat hunting lifecycle is a complex atmosphere that requires special attention from professionals to maintain security. This paper is a collection of recent work that gives a holistic view of the threat hunting ecosystem, identifies challenges, and discusses the future with the integration of artificial intelligence (AI). We specifically establish a life cycle and ecosystem for privacy-threat hunting in addition to identifying the related challenges. We also discovered how critical the use of AI is in threat hunting. This work paves the way for future work in this area as it provides the foundational knowledge to make meaningful advancements for threat hunting.

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“…Some researchers employ machine learning approaches to construct models, while proposing new algorithms to train the models to enhance the efficiency of IDS in identifying attacks on huge data 5 . Despite the availability of several improved approaches, the performance of IDS is still constrained by some unavoidable constraints 6 .…”

Section: Introductionmentioning

confidence: 99%

ID-RDRL: A Deep Reinforcement Learning-based Feature Selection Intrusion Detection Model

Ren

Zeng

Cao

et al. 2022

Preprint

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Network assaults pose significant security concerns to network services; hence, new technical solutions must be used to enhance the efficacy of intrusion detection systems. Existing approaches pay insufficient attention to data preparation and inadequately identify unknown network threats. This paper presents a network intrusion detection model (ID-RDRL) based on RFE feature extraction and reinforcement learning. ID-RDRL filters the optimum subset of features using the RFE feature selection technique, feeds them into a neural network to extract feature information and then trains a classifier using DRL to recognize network intrusions. We utilized CSE-CIC-IDS2018 as a dataset and conducted tests to evaluate the model's performance, which is comprised of a comprehensive collection of actual network traffic. The experimental results demonstrate that the proposed ID-RDRL model can select the optimal subset of features, remove approximately 80 percent of redundant features, and learn the selected features through DRL to enhance the IDS performance for network attack identification. In a complicated network environment, it has promising application potential in IDS.

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Modeling, Detecting, and Mitigating Threats Against Industrial Healthcare Systems: A Combined Software Defined Networking and Reinforcement Learning Approach

Cited by 51 publications

References 20 publications

Personality Privacy Protection Method of Social Users Based on Generative Adversarial Networks

Personality Privacy Protection Method of Social Users Based on Generative Adversarial Networks

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

ID-RDRL: A Deep Reinforcement Learning-based Feature Selection Intrusion Detection Model

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