Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning

• URL: http://arxiv.org/abs/2301.03532v1
• Date: Mon, 9 Jan 2023 17:26:37 GMT
• Title: Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning
• Authors: Arshiya Khan, Chase Cotton
• Abstract summary: This research proposes a way to overcome the barrier by bypassing feature engineering in the deep learning pipeline and using raw packet data as input. We introduce a feature engineering-less machine learning (ML) process to perform malware detection on IoT devices. Our proposed model, "Feature engineering-less-ML (FEL-ML)," is a lighter-weight detection algorithm that expends no extra computations on "engineered" features.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Through the generalization of deep learning, the research community has addressed critical challenges in the network security domain, like malware identification and anomaly detection. However, they have yet to discuss deploying them on Internet of Things (IoT) devices for day-to-day operations. IoT devices are often limited in memory and processing power, rendering the compute-intensive deep learning environment unusable. This research proposes a way to overcome this barrier by bypassing feature engineering in the deep learning pipeline and using raw packet data as input. We introduce a feature engineering-less machine learning (ML) process to perform malware detection on IoT devices. Our proposed model, "Feature engineering-less-ML (FEL-ML)," is a lighter-weight detection algorithm that expends no extra computations on "engineered" features. It effectively accelerates the low-powered IoT edge. It is trained on unprocessed byte-streams of packets. Aside from providing better results, it is quicker than traditional feature-based methods. FEL-ML facilitates resource-sensitive network traffic security with the added benefit of eliminating the significant investment by subject matter experts in feature engineering.

Related papers

• Real-time Threat Detection Strategies for Resource-constrained Devices [1.4815508281465273]
  We present an end-to-end process designed to effectively address DNS-tunneling attacks in a router. We demonstrate that utilizing stateless features for training the ML model, along with features chosen to be independent of the network configuration, leads to highly accurate results. The deployment of this carefully crafted model, optimized for embedded devices across diverse environments, resulted in high DNS-tunneling attack detection with minimal latency.
  arXiv  Detail & Related papers  (2024-03-22T10:02:54Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Prototype of deployment of Federated Learning with IoT devices [0.0]
  A huge amount of the resource desired, data, is stored in mobile devices, sensors and other Internet of Things (IoT) devices. At the same time these devices do not have enough data or computational capacity to train good models. Federated Learning (FL) provides an innovative solution that allows devices to learn in a collaborative way.
  arXiv  Detail & Related papers  (2023-11-24T10:37:30Z)
• Sparse Federated Training of Object Detection in the Internet of Vehicles [13.864554148921826]
  Object detection is one of the key technologies in the Internet of Vehicles (IoV) Current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server. We propose a federated learning-based framework, where well-trained local models are shared in the central server.
  arXiv  Detail & Related papers  (2023-09-07T08:58:41Z)
• Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications [46.97774949613859]
  Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI) However, their superior performance comes at the considerable cost of computational complexity. This paper provides an overview of efficient deep learning methods, systems and applications.
  arXiv  Detail & Related papers  (2022-04-25T16:52:48Z)
• A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks [0.0]
  Intrusion detection is one of the challenging issues in the area of network security. In this paper, a comparative analysis of conventional machine learning classification algorithms has been performed. It can be observed that Multi-Layer Perception (MLP) has dependencies between input and output and relies more on network configuration for intrusion detection.
  arXiv  Detail & Related papers  (2021-11-02T05:58:07Z)
• Detecting Attacks on IoT Devices using Featureless 1D-CNN [0.0]
  Featureless machine learning enables a low cost and low memory time-series analysis of network traffic. It benefits from eliminating the significant investment in subject matter experts and the time required for feature engineering.
  arXiv  Detail & Related papers  (2021-09-09T01:22:36Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• 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)
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks [96.24723959137218]
  We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
  arXiv  Detail & Related papers  (2020-11-25T12:51:59Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.