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Wang Z, Ghaleb FA, Zainal A, Siraj MM, Lu X. An efficient intrusion detection model based on convolutional spiking neural network. Sci Rep 2024; 14:7054. [PMID: 38528084 DOI: 10.1038/s41598-024-57691-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/20/2024] [Indexed: 03/27/2024] Open
Abstract
Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.
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Affiliation(s)
- Zhen Wang
- Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, 81310, Johor, Malaysia
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325035, Zhejiang, China
| | - Fuad A Ghaleb
- College of Computing and Digital Technology, Birmingham City University, Birmingham, B47XG, United Kingdom
| | - Anazida Zainal
- Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, 81310, Johor, Malaysia
| | - Maheyzah Md Siraj
- Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, 81310, Johor, Malaysia
| | - Xing Lu
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325035, Zhejiang, China.
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Kaiser MAA, Datta G, Wang Z, Jacob AP, Beerel PA, Jaiswal AR. Neuromorphic-P 2M: processing-in-pixel-in-memory paradigm for neuromorphic image sensors. Front Neuroinform 2023; 17:1144301. [PMID: 37214316 PMCID: PMC10192623 DOI: 10.3389/fninf.2023.1144301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 04/13/2023] [Indexed: 05/24/2023] Open
Abstract
Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor, in-sensor, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers; however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, for the first time, we propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating in-situ multi-bit multi-channel convolution inside the pixel array performing analog multiply and accumulate (MAC) operations that consume significantly less energy than their digital MAC alternative. To make this approach viable, we incorporate the circuit's non-ideality, leakage, and process variations into a novel hardware-algorithm co-design framework that leverages extensive HSpice simulations of our proposed circuit using the GF22nm FD-SOI technology node. We verified our framework on state-of-the-art neuromorphic vision sensor datasets and show that our solution consumes ~2× lower backend-processor energy while maintaining almost similar front-end (sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art while maintaining a high test accuracy of 88.36%.
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Affiliation(s)
- Md Abdullah-Al Kaiser
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Gourav Datta
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States
| | - Zixu Wang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States
| | - Ajey P. Jacob
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Peter A. Beerel
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Akhilesh R. Jaiswal
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
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