Towards Robust IoT Defense: Comparative Statistics of Attack Detection in Resource-Constrained Scenarios

• URL: http://arxiv.org/abs/2410.07810v1
• Date: Thu, 10 Oct 2024 10:58:03 GMT
• Title: Towards Robust IoT Defense: Comparative Statistics of Attack Detection in Resource-Constrained Scenarios
• Authors: Zainab Alwaisi, Simone Soderi,
• Abstract summary: Resource constraints pose a significant cybersecurity threat to IoT smart devices. We conduct an extensive statistical analysis of cyberattack detection algorithms under resource constraints to identify the most efficient one.
• Score: 1.3812010983144802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Resource constraints pose a significant cybersecurity threat to IoT smart devices, making them vulnerable to various attacks, including those targeting energy and memory. This study underscores the need for innovative security measures due to resource-related incidents in smart devices. In this paper, we conduct an extensive statistical analysis of cyberattack detection algorithms under resource constraints to identify the most efficient one. Our research involves a comparative analysis of various algorithms, including those from our previous work. We specifically compare a lightweight algorithm for detecting resource-constrained cyberattacks with another designed for the same purpose. The latter employs TinyML for detection. In addition to the comprehensive evaluation of the proposed algorithms, we introduced a novel detection method for resource-constrained attacks. This method involves analyzing protocol data and categorizing the final data packet as normal or attacked. The attacked data is further analyzed in terms of the memory and energy consumption of the devices to determine whether it is an energy or memory attack or another form of malicious activity. We compare the suggested algorithm performance using four evaluation metrics: accuracy, PoD, PoFA, and PoM. The proposed dynamic techniques dynamically select the classifier with the best results for detecting attacks, ensuring optimal performance even within resource-constrained IoT environments. The results indicate that the proposed algorithms outperform the existing works with accuracy for algorithms with TinyML and without TinyML of 99.3\%, 98.2\%, a probability of detection of 99.4\%, 97.3\%, a probability of false alarm of 1.23\%, 1.64\%, a probability of misdetection of 1.64\%, 1.46 respectively. In contrast, the accuracy of the novel detection mechanism exceeds 99.5\% for RF and 97\% for SVM.

Related papers

• Challenging Machine Learning Algorithms in Predicting Vulnerable JavaScript Functions [2.243674903279612]
  State-of-the-art machine learning techniques can predict functions with possible security vulnerabilities in JavaScript programs. Best performing algorithm was KNN, which created a model for the prediction of vulnerable functions with an F-measure of 0.76. Deep learning, tree and forest based classifiers, and SVM were competitive with F-measures over 0.70.
  arXiv  Detail & Related papers  (2024-05-12T08:23:42Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• A Large-scale Multiple-objective Method for Black-box Attack against Object Detection [70.00150794625053]
  We propose to minimize the true positive rate and maximize the false positive rate, which can encourage more false positive objects to block the generation of new true positive bounding boxes. We extend the standard Genetic Algorithm with Random Subset selection and Divide-and-Conquer, called GARSDC, which significantly improves the efficiency. Compared with the state-of-art attack methods, GARSDC decreases by an average 12.0 in the mAP and queries by about 1000 times in extensive experiments.
  arXiv  Detail & Related papers  (2022-09-16T08:36:42Z)
• UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples [1.2691047660244335]
  Botnet detection requires extremely low false-positive rates (FPR), which are not commonly attainable in contemporary deep learning. In this paper, two LSTM-based classification algorithms for botnet classification with an accuracy higher than 98% are presented.
  arXiv  Detail & Related papers  (2022-04-18T21:49:14Z)
• Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm [93.80082636284922]
  Sparse adversarial attacks can fool deep networks (DNNs) by only perturbing a few pixels. Recent efforts combine it with another l_infty perturbation on magnitudes. We propose a homotopy algorithm to tackle the sparsity and neural perturbation framework.
  arXiv  Detail & Related papers  (2021-06-10T20:11:36Z)
• Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits [55.740716446995805]
  We study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Our goal is to misclassify a specific sample into a target class without any sample modification. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem.
  arXiv  Detail & Related papers  (2021-02-21T03:13:27Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Phishing URL Detection Through Top-level Domain Analysis: A Descriptive Approach [3.494620587853103]
  This study aims to develop a machine-learning model to detect fraudulent URLs which can be used within the Splunk platform. Inspired from similar approaches in the literature, we trained the SVM and Random Forests algorithms using malicious and benign datasets. We evaluated the algorithms' performance with precision and recall, reaching up to 85% precision and 87% recall in the case of Random Forests.
  arXiv  Detail & Related papers  (2020-05-13T21:41:29Z)
• A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid [0.6445605125467573]
  Intrusion detection system (IDS) plays an important role in securing smart grid networks and detecting malicious activity. This paper presents an overview of four data mining algorithms used by IDS in Smart Grid. Results show that Random Forest outperforms the other three algorithms in detecting attacks with higher probability of detection, lower probability of false alarm, lower probability of miss detection, and higher accuracy.
  arXiv  Detail & Related papers  (2019-12-31T20:48:13Z)

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.