A Deep Learning Approach to Network Intrusion Detection

Shone, Nathan; Ngoc, Tran Nguyen; Phai, Vu Dinh; Shi, Qi

doi:10.1109/tetci.2017.2772792

Cited by 1,157 publications

(545 citation statements)

References 31 publications

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“…A dynamic threshold was set to capture the brightest part of the face, even where part of the face was hidden. For detecting the retinal point and other bright facial features, a modified Gabor filter was used [30][31][32].…”

Section: Methodologies and Results Analysismentioning

confidence: 99%

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol

Pandya¹,

Ghayvat²,

Kotecha³

et al. 2018

Preprint

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Abstract:The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an on-going theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT), Cognitive Internet of Things, Internet of Medical Things, and Cloud Computing are expanding smart home concepts and solutions, and their applications. The primary objective of the present research work was to design and develop IoT and cloud computing based smart home solutions. In addition, we also propose a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision facility. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time.

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Section: Methodologies and Results Analysismentioning

confidence: 99%

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol

Pandya¹,

Ghayvat²,

Kotecha³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…There are recent works that use unsupervised deep learning models to transform the data into lower rank features before applying supervised machine learning. Several prior approaches first employ autoencoders [22] and their variants to extract the compressed latent representation as features, and subsequently use these features for anomaly detection by training standard classifiers such as Random Forests [23].…”

Section: Related Workmentioning

confidence: 99%

GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

Nguyen

Lim

Divakaran³

et al. 2019

2019 IEEE Conference on Communications and Network Security (CNS)

110

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This paper looks into the problem of detecting network anomalies by analyzing NetFlow records. While many previous works have used statistical models and machine learning techniques in a supervised way, such solutions have the limitations that they require large amount of labeled data for training and are unlikely to detect zero-day attacks. Existing anomaly detection solutions also do not provide an easy way to explain or identify attacks in the anomalous traffic. To address these limitations, we develop and present GEE, a framework for detecting and explaining anomalies in network traffic. GEE comprises of two components: (i) Variational Autoencoder (VAE) -an unsupervised deep-learning technique for detecting anomalies, and (ii) a gradient-based fingerprinting technique for explaining anomalies. Evaluation of GEE on the recent UGR dataset demonstrates that our approach is effective in detecting different anomalies as well as identifying fingerprints that are good representations of these various attacks.

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“…Later, Shone et al [26] proposed a novel deep learning-based intrusion detection method called nonsymmetric deep autoencoder (NDAE). The authors used TensorFlow and evaluated their method by using KDD Cup '99 and NSL-KDD datasets.…”

Section: Intrusion Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.

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A Deep Learning Approach to Network Intrusion Detection

Cited by 1,157 publications

References 31 publications

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol

GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

Artificial Intelligence in the Cyber Domain: Offense and Defense

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