Towards Real-Time Deep Learning-Based Network Intrusion Detection on FPGA

Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack of defense mechanisms and hardware security support, therefore making them vulnerable to cyber attacks. In addition, IoT gateways provide very limited security features to detect such threats, especially the absence of intrusion detection methods powered by deep learning. Indeed, deep learning models require high computational power that exceeds the capacity of these gateways. In this paper, we introduce Realguard, an DNN-based network intrusion detection system (NIDS) directly operated on local gateways to protect IoT devices within the network. The superiority of our proposal is that it can accurately detect multiple cyber attacks in real time with a small computational footprint. This is achieved by a lightweight feature extraction mechanism and an efficient attack detection model powered by deep neural networks. Our evaluations on practical datasets indicate that Realguard could detect ten types of attacks (e.g., port scan, Botnet, and FTP-Patator) in real time with an average accuracy of 99.57%, whereas the best of our competitors is 98.85%. Furthermore, our proposal effectively operates on resource-constraint gateways (Raspberry PI) at a high packet processing rate reported about 10.600 packets per second.

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Cloud-based Real-time Network Intrusion Detection Using Deep Learning

2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security) ◽

10.1109/cybersecpods.2018.8560674 ◽

2018 ◽

Cited By ~ 7

Author(s):

Santhosh Parampottupadam ◽

Arghir-Nicolae Moldovann

Keyword(s):

Deep Learning ◽

Intrusion Detection ◽

Real Time ◽

Network Intrusion Detection ◽

Network Intrusion

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Network intrusion detection method based on deep learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/1966/1/012051 ◽

2021 ◽

Vol 1966 (1) ◽

pp. 012051

Author(s):

Shuai Zou ◽

Fangwei Zhong ◽

Bing Han ◽

Hao Sun ◽

Tao Qian ◽

...

Keyword(s):

Deep Learning ◽

Intrusion Detection ◽

Detection Method ◽

Network Intrusion Detection ◽

Network Intrusion

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Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽

10.3390/s21144736 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4736

Author(s):

Sk. Tanzir Mehedi ◽

Adnan Anwar ◽

Ziaur Rahman ◽

Kawsar Ahmed

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Intrusion Detection ◽

Real Time ◽

Transfer Learning ◽

Security Requirements ◽

Detection Accuracy ◽

Area Network ◽

Complex Data ◽

Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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Power information network intrusion detection based on deep learning and cloud computing

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219104 ◽

2021 ◽

pp. 1-9

Author(s):

Xiangbing Zhao ◽

Jianhui Zhou

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Intrusion Detection ◽

High Speed ◽

Information Networks ◽

Network Intrusion Detection ◽

Information Network ◽

Advantages And Disadvantages ◽

Network Intrusion ◽

Capture Mechanism

With the advent of the computer network era, people like to think in deeper ways and methods. In addition, the power information network is facing the problem of information leakage. The research of power information network intrusion detection is helpful to prevent the intrusion and attack of bad factors, ensure the safety of information, and protect state secrets and personal privacy. In this paper, through the NRIDS model and network data analysis method, based on deep learning and cloud computing, the demand analysis of the real-time intrusion detection system for the power information network is carried out. The advantages and disadvantages of this kind of message capture mechanism are compared, and then a high-speed article capture mechanism is designed based on the DPDK research. Since cloud computing and power information networks are the most commonly used tools and ways for us to obtain information in our daily lives, our lives will be difficult to carry out without cloud computing and power information networks, so we must do a good job to ensure the security of network information network intrusion detection and defense measures.

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