Cost-aware Feature Selection for IoT Device Classification
- URL: http://arxiv.org/abs/2009.01368v3
- Date: Wed, 21 Apr 2021 18:48:57 GMT
- Title: Cost-aware Feature Selection for IoT Device Classification
- Authors: Biswadeep Chakraborty, Dinil Mon Divakaran, Ido Nevat, Gareth W.
Peters, Mohan Gurusamy
- Abstract summary: We argue that feature extraction has a cost, and the costs are different for different features.
We develop a novel algorithm to solve it in a fast and effective way using the Cross-Entropy (CE) based optimization technique.
- Score: 6.193853963672491
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Classification of IoT devices into different types is of paramount
importance, from multiple perspectives, including security and privacy aspects.
Recent works have explored machine learning techniques for fingerprinting (or
classifying) IoT devices, with promising results. However, existing works have
assumed that the features used for building the machine learning models are
readily available or can be easily extracted from the network traffic; in other
words, they do not consider the costs associated with feature extraction. In
this work, we take a more realistic approach, and argue that feature extraction
has a cost, and the costs are different for different features. We also take a
step forward from the current practice of considering the misclassification
loss as a binary value, and make a case for different losses based on the
misclassification performance. Thereby, and more importantly, we introduce the
notion of risk for IoT device classification. We define and formulate the
problem of cost-aware IoT device classification. This being a combinatorial
optimization problem, we develop a novel algorithm to solve it in a fast and
effective way using the Cross-Entropy (CE) based stochastic optimization
technique. Using traffic of real devices, we demonstrate the capability of the
CE based algorithm in selecting features with minimal risk of misclassification
while keeping the cost for feature extraction within a specified limit.
Related papers
- Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate.
Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
arXiv Detail & Related papers (2024-12-21T10:18:55Z) - A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy.
We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods.
By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
arXiv Detail & Related papers (2024-12-11T22:12:21Z) - Optimized IoT Intrusion Detection using Machine Learning Technique [0.0]
Intrusion detection systems (IDSs) are essential for defending against a variety of attacks.
The functional and physical diversity of IoT IDS systems causes significant issues.
For peculiarity-based IDS, this study proposes and implements a novel component selection and extraction strategy.
arXiv Detail & Related papers (2024-12-03T21:23:54Z) - INTELLECT: Adapting Cyber Threat Detection to Heterogeneous Computing Environments [0.055923945039144884]
This paper introduces INTELLECT, a novel solution that integrates feature selection, model pruning, and fine-tuning techniques into a cohesive pipeline for the dynamic adaptation of pre-trained ML models and configurations for IDSs.
We demonstrate the advantages of incorporating knowledge distillation techniques while fine-tuning, enabling the ML model to consistently adapt to local network patterns while preserving historical knowledge.
arXiv Detail & Related papers (2024-07-17T22:34:29Z) - Machine Learning-Based Intrusion Detection: Feature Selection versus
Feature Extraction [3.5889226512319903]
Internet of things (IoT) devices are highly vulnerable to cyber-attacks.
A variety of machine learning-based network intrusion detection methods for IoT networks have been developed.
This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics.
arXiv Detail & Related papers (2023-07-04T08:48:01Z) - Semantic Information Marketing in The Metaverse: A Learning-Based
Contract Theory Framework [68.8725783112254]
We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data.
Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices.
We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
arXiv Detail & Related papers (2023-02-22T15:52:37Z) - Task-Oriented Over-the-Air Computation for Multi-Device Edge AI [57.50247872182593]
6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task.
Task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system.
arXiv Detail & Related papers (2022-11-02T16:35:14Z) - 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) - Cost-Effective Federated Learning in Mobile Edge Networks [37.16466118235272]
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model without sharing their raw data.
We analyze how to design adaptive FL in mobile edge networks that optimally chooses essential control variables to minimize the total cost.
We develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters.
arXiv Detail & Related papers (2021-09-12T03:02:24Z) - Orthogonal Projection Loss [59.61277381836491]
We develop a novel loss function termed Orthogonal Projection Loss' (OPL)
OPL directly enforces inter-class separation alongside intra-class clustering in the feature space.
OPL offers unique advantages as it does not require careful negative mining and is not sensitive to the batch size.
arXiv Detail & Related papers (2021-03-25T17:58:00Z) - Cost-Effective Federated Learning Design [37.16466118235272]
Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data.
Despite its efficiency and effectiveness, the iterative on-device learning process incurs a considerable cost in terms of learning time and energy consumption.
We analyze how to design adaptive FL that optimally chooses essential control variables to minimize the total cost while ensuring convergence.
arXiv Detail & Related papers (2020-12-15T14:45:11Z)
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.