Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach

• URL: http://arxiv.org/abs/2404.10792v1
• Date: Sat, 13 Apr 2024 17:24:18 GMT
• Title: Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach
• Authors: Wadid Foudhaili, Anouar Nechi, Celine Thermann, Mohammad Al Johmani, Rainer Buchty, Mladen Berekovic, Saleh Mulhem,
• Abstract summary: Intrusion detection systems (IDS) are crucial security measures nowadays to enforce network security. In this paper, we analyze the concept of intelligent IDS (I-IDS) while addressing the specific requirements of Edge devices. We introduce a systematic approach to constructing the I-IDS on reconfigurable Edge hardware.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intrusion detection systems (IDS) are crucial security measures nowadays to enforce network security. Their task is to detect anomalies in network communication and identify, if not thwart, possibly malicious behavior. Recently, machine learning has been deployed to construct intelligent IDS. This approach, however, is quite challenging particularly in distributed, highly dynamic, yet resource-constrained systems like Edge setups. In this paper, we tackle this issue from multiple angles by analyzing the concept of intelligent IDS (I-IDS) while addressing the specific requirements of Edge devices with a special focus on reconfigurability. Then, we introduce a systematic approach to constructing the I-IDS on reconfigurable Edge hardware. For this, we implemented our proposed IDS on state-of-the-art Field Programmable Gate Arrays (FPGAs) technology as (1) a purely FPGA-based dataflow processor (DFP) and (2) a co-designed approach featuring RISC-V soft-core as FPGA-based soft-core processor (SCP). We complete our paper with a comparison of the state of the art (SoA) in this domain. The results show that DFP and SCP are both suitable for Edge applications from hardware resource and energy efficiency perspectives. Our proposed DFP solution clearly outperforms the SoA and demonstrates that required high performance can be achieved without prohibitively high hardware costs. This makes our proposed DFP suitable for Edge-based high-speed applications like modern communication technology.

Related papers

• Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA [20.629635991749808]
  This paper proposes an algorithm and hardware co-design framework that can generate field-programmable gate array (FPGA)-based accelerators for efficient BayesNNs. At the algorithm level, we propose novel multi-exit dropout-based BayesNNs with reduced computational and memory overheads. At the hardware level, this paper introduces a transformation framework that can generate FPGA-based accelerators for the proposed efficient BayesNNs.
  arXiv  Detail & Related papers  (2024-06-20T17:08:42Z)
• RO-SVD: A Reconfigurable Hardware Copyright Protection Framework for AIGC Applications [7.368978400783039]
  We propose a blockchain-based copyright traceability framework for AI content. Our framework can be easily constructed on existing AI-accelerated devices. This is the first practical hardware study discussing and implementing copyright traceability specifically for AI-generated content.
  arXiv  Detail & Related papers  (2024-06-17T13:38:57Z)
• Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security [7.8344795632171325]
  Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. To address this challenge, the Zero Trust (ZT) paradigm emerges as a key method for safeguarding network integrity and data confidentiality. This work introduces EPS-CNN, a novel deep-learning-based wireless device identification framework.
  arXiv  Detail & Related papers  (2024-02-08T00:23:42Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework [13.573645522781712]
  Deep neural networks (DNNs) and spiking neural networks (SNNs) offer state-of-the-art results on resource-constrained edge devices. These systems are required to maintain correct functionality under diverse security and reliability threats. This paper first discusses existing approaches to address energy efficiency, reliability, and security issues at different system layers.
  arXiv  Detail & Related papers  (2021-09-20T20:22:56Z)
• How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures [7.085772863979686]
  Deep neural networks (DNNs) have led to large strides in various Artificial Intelligence (AI) inference tasks, such as object and speech recognition. deploying such AI models across commodity devices faces significant challenges. We present techniques for achieving real-time performance following a cross-stack approach.
  arXiv  Detail & Related papers  (2021-06-21T11:23:12Z)
• Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
  We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
  arXiv  Detail & Related papers  (2021-06-03T16:45:40Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• ASFD: Automatic and Scalable Face Detector [129.82350993748258]
  We propose a novel Automatic and Scalable Face Detector (ASFD) ASFD is based on a combination of neural architecture search techniques as well as a new loss design. Our ASFD-D6 outperforms the prior strong competitors, and our lightweight ASFD-D0 runs at more than 120 FPS with Mobilenet for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-03-25T06:00:47Z)
• RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design [116.88396201197533]
  A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency. A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
  arXiv  Detail & Related papers  (2020-01-28T14:37:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.