Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

Kang, Min Joo; Kang, Je Won

doi:10.1371/journal.pone.0155781

Cited by 511 publications

(247 citation statements)

References 40 publications

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“…Another possible direction, would be to understand how danger theory ideas [24, 77] could be compatible with the CFFs. This work also suggests several ideas for research in machine learning, such as extending the current results to unsupervised learning [78] and even deep learning strategies in anomaly detection [79]. …”

Section: Discussionmentioning

confidence: 98%

Can the Immune System Perform a t-Test?

2017

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The self-nonself discrimination hypothesis remains a landmark concept in immunology. It proposes that tolerance breaks down in the presence of nonself antigens. In strike contrast, in statistics, occurrence of nonself elements in a sample (i.e., outliers) is not obligatory to violate the null hypothesis. Very often, what is crucial is the combination of (self) elements in a sample. The two views on how to detect a change seem challengingly different and it could seem difficult to conceive how immunological cellular interactions could trigger responses with a precision comparable to some statistical tests. Here it is shown that frustrated cellular interactions reconcile the two views within a plausible immunological setting. It is proposed that the adaptive immune system can be promptly activated either when nonself ligands are detected or self-ligands occur in abnormal combinations. In particular we show that cellular populations behaving in this way could perform location statistical tests, with performances comparable to t or KS tests, or even more general data mining tests such as support vector machines or random forests. In more general terms, this work claims that plausible immunological models should provide accurate detection mechanisms for host protection and, furthermore, that investigation on mechanisms leading to improved detection in “in silico” models can help unveil how the real immune system works.

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Section: Discussionmentioning

confidence: 98%

Can the Immune System Perform a t-Test?

2017

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“…Kang and Kang [24] proposed the use of an unsupervised DBN to train parameters to initialise the DNN, which yielded improved classification results (exact details of the approach are not clear). Their evaluation shows improved performance in terms of classification errors.…”

Section: Existing Workmentioning

confidence: 99%

A Deep Learning Approach to Network Intrusion Detection

Shone

Ngoc

Phai

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

1,154

481

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Abstract-Network Intrusion Detection Systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed non-symmetric deep auto-encoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in GPU-enabled TensorFlow and evaluated using the benchmark KDD Cup '99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

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“…Vehicle detection system consists of transmitter module, receiving module and anti-collision alarm module [7]. The signal processing circuit relies on the electric coupling to perform the conversion of light spots and uses the optical modulation processing method to modulate and demodulate optical signals so as to solve the problem of external light interference in the operation of the system [8].…”

Section: System Hardware Designmentioning

confidence: 99%

Application of Laser Sensor in Urban Vehicle Type Detection

Liu

2018

Teh. vjesn.

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With the development of urban transportation, more and more traffic accidents happened, most of which were caused by ultra-high vehicles, which not only damaged the road facilities, but also endangered the safety of the masses. In this study, a vehicle height detection system was designed and applied to urban transportation to prevent such accidents. The system utilized the laser light curtain principle of the laser sensor to accurately detect the height of vehicles, equipped with an optical signal anti -interference system and an anti -collision alarm system. The experimental results showed that the system could effectively detect the unqualified vehicles and give alarms, which provided a reference for the application of laser sensor in urban road traffic detection.

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Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

Cited by 511 publications

References 40 publications

Can the Immune System Perform a t-Test?

Can the Immune System Perform a t-Test?

A Deep Learning Approach to Network Intrusion Detection

Application of Laser Sensor in Urban Vehicle Type Detection

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