Detecting and Preventing Cyber Insider Threats: A Survey

Liu, Liu; Vel, Olivier De; Han, Qing‐Long; Zhang, Jun; Xiang, Yang

doi:10.1109/comst.2018.2800740

Cited by 309 publications

(155 citation statements)

References 95 publications

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“…Robust network in the medical data traffic classification is also another fundamental problem needed to be fixed in the machine learning–based data processing; the Robust statistical traffic classification (RTC) is the most popular classification method proposed in 2015; after that, the cross‐project transfer representation learning has become popular, which is also based on the machine learning theory, especially based on the long short‐term memory (LSTM) . Apart from that, the nearest neighbor (NN)–based method also has a performance of high classification in the medical data applications, such as the traffic classification correlation (TCC) and the artificial neural network (ANN)–based schemes …”

Section: Related Workmentioning

confidence: 99%

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Machine learning–based haptic‐enabled surgical navigation with security awareness

Tai

Wei

Zhou

et al. 2018

Concurrency and Computation

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Summary A novel security awareness surgical navigation system has been proposed for the accurate minimally invasive surgery with machine learning algorithms, haptic‐enabled devices, and customized surgical tools to guide the surgery with real‐time force and visual navigation. To provide a direct and simplified user interface during the operation, we combined traditional surgical guide images with AR‐based view and implemented a 3D reconstructed patient‐specific surgical environment includes with all surgical requisite details. In particular, we trained the surgical collected biomechanics haptic data by employed LSTM‐based RNN algorithm, and residual network for the intraoperative force manipulation prediction and classification, respectively. Experiments evaluation results on percutaneous therapy surgery demonstrated a higher performance and distinguished accuracy by the visual and haptic combined than the traditional navigation system. These preliminary study findings may suggested a new framework in the minimally invasive surgical navigation application and hint at the possibility integration of haptic, AR, and machine learning algorithms implementation in medical simulation. In addition, we take security into account when implementation this new framework.

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Section: Related Workmentioning

confidence: 99%

“…22 Apart from that, the nearest neighbor (NN)-based method also has a performance of high classification in the medical data applications, such as the traffic classification correlation (TCC) 23 and the artificial neural network (ANN)-based schemes. 24…”

Section: Related Workmentioning

confidence: 99%

Machine learning–based haptic‐enabled surgical navigation with security awareness

Tai

Wei

Zhou

et al. 2018

Concurrency and Computation

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“…Cybersecurity has become a very hot topic in recent years. Many communities, groups, and governments start to realize the importance and urgency to deal with the ever‐changing cyberattacks . Experts in the industry and scholars in the academia strive to innovate the next‐generation solutions.…”

Section: Introductionmentioning

confidence: 99%

Editorial: Recent advances in machine learning for cybersecurity

Pan

Zhang

Oliver³

2019

Concurrency and Computation

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“…This requires a lot of manual work and time. However, machine learning technique is proven to be useful in automatically identifying potential vulnerabilities, and it even plays an important role in cyber security . It uses ground‐truth for knowledge learning, then applies the learnt knowledge for new knowledge prediction.…”

Section: Introductionmentioning

confidence: 99%

A performance evaluation of deep‐learnt features for software vulnerability detection

Ban

Liu

Chen

et al. 2018

Concurrency and Computation

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Summary Software vulnerability is a critical issue in the realm of cyber security. In terms of techniques, machine learning (ML) has been successfully used in many real‐world problems such as software vulnerability detection, malware detection and function recognition, for high‐quality feature representation learning. In this paper, we propose a performance evaluation study on ML based solutions for software vulnerability detection, conducting three experiments: machine learning‐based techniques for software vulnerability detection based on the scenario of single type of vulnerability and multiple types of vulnerabilities per dataset; machine learning‐based techniques for cross‐project software vulnerability detection; and software vulnerability detection when facing the class imbalance problem with varying imbalance ratios. Experimental results show that it is possible to employ software vulnerability detection based on ML techniques. However, ML‐based techniques suffer poor performance on both cross‐project and class imbalance problem in software vulnerability detection.

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Detecting and Preventing Cyber Insider Threats: A Survey

Cited by 309 publications

References 95 publications

Machine learning–based haptic‐enabled surgical navigation with security awareness

Machine learning–based haptic‐enabled surgical navigation with security awareness

Editorial: Recent advances in machine learning for cybersecurity

A performance evaluation of deep‐learnt features for software vulnerability detection

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