IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection

• URL: http://arxiv.org/abs/2401.01343v2
• Date: Thu, 12 Jun 2025 07:02:15 GMT
• Title: IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
• Authors: Kahraman Kostas, Mike Just, Michael A. Lones,
• Abstract summary: IoTGeM is an approach for modeling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance.<n>We build and test our models using strictly isolated train and test datasets.<n> IoTGeM achieves F1 scores of 99% for ACK, HTTP, SYN, MHD, and PS attacks, as well as a 94% F1 score for UDP attacks.
• Score: 3.3772986620114387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previous research on behavior-based attack detection for networks of IoT devices has resulted in machine learning models whose ability to adapt to unseen data is limited and often not demonstrated. This paper presents IoTGeM, an approach for modeling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance. We first introduce an improved rolling window approach for feature extraction. To reduce overfitting, we then apply a multi-step feature selection process where a Genetic Algorithm (GA) is uniquely guided by exogenous feedback from a separate, independent dataset. To prevent common data leaks that have limited previous models, we build and test our models using strictly isolated train and test datasets. The resulting models are rigorously evaluated using a diverse portfolio of machine learning algorithms and datasets. Our window-based models demonstrate superior generalization compared to traditional flow-based models, particularly when tested on unseen datasets. On these stringent, cross-dataset tests, IoTGeM achieves F1 scores of 99\% for ACK, HTTP, SYN, MHD, and PS attacks, as well as a 94\% F1 score for UDP attacks. Finally, we build confidence in the models by using the SHAP (SHapley Additive exPlanations) explainable AI technique, allowing us to identify the specific features that underlie the accurate detection of attacks.

Related papers

• Zero-Shot Image Anomaly Detection Using Generative Foundation Models [2.241618130319058]
  This research explores the use of score-based generative models as foundational tools for semantic anomaly detection.<n>By analyzing Stein score errors, we introduce a novel method for identifying anomalous samples without requiring re-training on each target dataset.<n>Our approach improves over state-of-the-art and relies on training a single model on one dataset -- CelebA -- which we find to be an effective base distribution.
  arXiv  Detail & Related papers  (2025-07-30T13:56:36Z)
• Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems [1.1265248232450553]
  This paper proposes a self-supervised contrastive learning approach for generalizable intrusion detection on raw packet sequences.<n>Our framework exhibits better performance in comparison to existing NetFlow self-supervised methods.<n>Our model provides a strong baseline for supervised intrusion detection with limited labeled data.
  arXiv  Detail & Related papers  (2025-05-12T13:42:00Z)
• Enhanced Anomaly Detection in IoMT Networks using Ensemble AI Models on the CICIoMT2024 Dataset [0.7753092380426906]
  The rapid proliferation of Internet of Medical Things (IoMT) devices in healthcare has introduced unique cybersecurity challenges.<n>This research aims to develop an advanced, real-time anomaly detection framework tailored for IoMT network traffic.
  arXiv  Detail & Related papers  (2025-02-17T14:46:58Z)
• GeMID: Generalizable Models for IoT Device Identification [4.029017464832905]
  Device identification (DI) distinguishes IoT devices based on their traffic patterns. Existing approaches to DI that build machine learning models often overlook the challenge of model generalizability across diverse network environments. We propose a novel framework to address this limitation and evaluate the generalizability of DI models across datasets collected within different network environments.
  arXiv  Detail & Related papers  (2024-11-05T17:09:43Z)
• Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection [20.67964977754179]
  We investigate the potential of image representations for morphing attack detection (MAD) We develop supervised detectors by training a simple binary linear SVM on the extracted features and one-class detectors by modeling the distribution of bonafide features with a Gaussian Mixture Model (GMM) Our results indicate that attack-agnostic features can effectively detect morphing attacks, outperforming traditional supervised and one-class detectors from the literature in most scenarios.
  arXiv  Detail & Related papers  (2024-10-22T08:27:43Z)
• Learning to Learn Transferable Generative Attack for Person Re-Identification [17.26567195924685]
  Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed. Our MTGA outperforms the SOTA methods by 21.5% and 11.3% on mean mAP drop rate, respectively.
  arXiv  Detail & Related papers  (2024-09-06T11:57:17Z)
• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [58.60915132222421]
  We introduce an approach that is both general and parameter-efficient for face forgery detection. We design a forgery-style mixture formulation that augments the diversity of forgery source domains. We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• GM-DF: Generalized Multi-Scenario Deepfake Detection [49.072106087564144]
  Existing face forgery detection usually follows the paradigm of training models in a single domain. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets.
  arXiv  Detail & Related papers  (2024-06-28T17:42:08Z)
• MisGUIDE : Defense Against Data-Free Deep Learning Model Extraction [0.8437187555622164]
  "MisGUIDE" is a two-step defense framework for Deep Learning models that disrupts the adversarial sample generation process. The aim of the proposed defense method is to reduce the accuracy of the cloned model while maintaining accuracy on authentic queries.
  arXiv  Detail & Related papers  (2024-03-27T13:59:21Z)
• Data-Free Model Extraction Attacks in the Context of Object Detection [0.6719751155411076]
  A significant number of machine learning models are vulnerable to model extraction attacks. We propose an adversary black box attack extending to a regression problem for predicting bounding box coordinates in object detection. We find that the proposed model extraction method achieves significant results by using reasonable queries.
  arXiv  Detail & Related papers  (2023-08-09T06:23:54Z)
• Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability [70.72426887518517]
  Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. We propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data. Our method utilizes a mask to figure out the memorized atypical samples, and then finetune the model or prune it with the introduced mask to forget them.
  arXiv  Detail & Related papers  (2023-06-06T14:23:34Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection [54.76993389109327]
  Unsupervised anomaly detection aims to build models to detect unseen anomalies by only training on the normal data. We propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges. AMSL incorporates a self-supervised learning module to learn general normal patterns and an adaptive memory fusion module to learn rich feature representations.
  arXiv  Detail & Related papers  (2022-01-03T03:40:21Z)
• Learning to Detect: A Data-driven Approach for Network Intrusion Detection [17.288512506016612]
  We perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy. We demonstrate the advantage of the unsupervised representation learning model in binary intrusion detection tasks.
  arXiv  Detail & Related papers  (2021-08-18T21:19:26Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Knowledge-Enriched Distributional Model Inversion Attacks [49.43828150561947]
  Model inversion (MI) attacks are aimed at reconstructing training data from model parameters. We present a novel inversion-specific GAN that can better distill knowledge useful for performing attacks on private models from public data. Our experiments show that the combination of these techniques can significantly boost the success rate of the state-of-the-art MI attacks by 150%.
  arXiv  Detail & Related papers  (2020-10-08T16:20:48Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)

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.