SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment

• URL: http://arxiv.org/abs/2204.07772v1
• Date: Sat, 16 Apr 2022 10:10:59 GMT
• Title: SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment
• Authors: Marjan Golmaryami, Rahim Taheri, Zahra Pooranian, Mohammad Shojafar, Pei Xiao
• Abstract summary: We propose an adversarial self-supervised architecture for detecting malware in Internet of Things networks, SETTI. In the SETTI architecture, we design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and ASelf-MDS. To validate the attack and defence algorithms, we conduct experiments on two recent IoT datasets: IoT23 and NBIoT.
• Score: 20.586904296213007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practice available malware features for IoT devices and lack real-time prediction behaviors. More research is thus required on malware detection to cope with real-time misclassification of the input IoT data. Motivated by this, in this paper we propose an adversarial self-supervised architecture for detecting malware in IoT networks, SETTI, considering samples of IoT network traffic that may not be labeled. In the SETTI architecture, we design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and ASelf-MDS. The Self-MDS method considers the IoT input data and the adversarial sample generation in real-time. The GSelf-MDS builds a generative adversarial network model to generate adversarial samples in the self-supervised structure. Finally, ASelf-MDS utilizes three well-known perturbation sample techniques to develop adversarial malware and inject it over the self-supervised architecture. Also, we apply a defence method to mitigate these attacks, namely adversarial self-supervised training to protect the malware detection architecture against injecting the malicious samples. To validate the attack and defence algorithms, we conduct experiments on two recent IoT datasets: IoT23 and NBIoT. Comparison of the results shows that in the IoT23 dataset, the Self-MDS method has the most damaging consequences from the attacker's point of view by reducing the accuracy rate from 98% to 74%. In the NBIoT dataset, the ASelf-MDS method is the most devastating algorithm that can plunge the accuracy rate from 98% to 77%.

Related papers

• Enhancing IoT Malware Detection through Adaptive Model Parallelism and Resource Optimization [0.6856683556201506]
  This study introduces a novel approach to malware detection tailored for IoT devices. Based on resource availability, ongoing workload, and communication costs, the malware detection task is dynamically allocated either on-device or offloaded to neighboring IoT nodes. Experimental results demonstrate that this proposed technique achieves a significant speedup of 9.8 x compared to on-device inference.
  arXiv  Detail & Related papers  (2024-04-12T20:51:25Z)
• 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)
• The Adversarial Implications of Variable-Time Inference [47.44631666803983]
  We present an approach that exploits a novel side channel in which the adversary simply measures the execution time of the algorithm used to post-process the predictions of the ML model under attack. We investigate leakage from the non-maximum suppression (NMS) algorithm, which plays a crucial role in the operation of object detectors. We demonstrate attacks against the YOLOv3 detector, leveraging the timing leakage to successfully evade object detection using adversarial examples, and perform dataset inference.
  arXiv  Detail & Related papers  (2023-09-05T11:53:17Z)
• DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness [58.23214712926585]
  We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection. Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables. We are the first to offer certified robustness in the realm of static detection of malware executables.
  arXiv  Detail & Related papers  (2023-03-20T17:25:22Z)
• Intrusion Detection in Internet of Things using Convolutional Neural Networks [4.718295605140562]
  We propose a novel solution to the intrusion attacks against IoT devices using CNNs. The data is encoded as the convolutional operations to capture the patterns from the sensors data along time. The experimental results show significant improvement in both true positive rate and false positive rate compared to the baseline using LSTM.
  arXiv  Detail & Related papers  (2022-11-18T07:27:07Z)
• Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network [0.0]
  Internet of Things (IoT) has altered living by controlling devices/things over the Internet. To bring down the IoT network, attackers can utilise these devices to conduct a variety of network attacks. In this paper, we have developed an unsupervised ensemble learning model that is able to detect new or unknown attacks in an IoT network from an unlabelled dataset.
  arXiv  Detail & Related papers  (2022-07-16T11:12:32Z)
• Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection [3.3659034873495632]
  Internet of Things (IoT) devices are constructed to build a huge communications network. These devices are insecure in reality, it means that the communications network are exposed by the attacker. In this paper, we propose SS-VTCN, a semi-supervised network for IoT multiple anomaly detection.
  arXiv  Detail & Related papers  (2021-04-05T08:51:24Z)
• Lightweight Collaborative Anomaly Detection for the IoT using Blockchain [40.52854197326305]
  Internet of things (IoT) devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. We present a distributed IoT simulation platform, which consists of 48 Raspberry Pis.
  arXiv  Detail & Related papers  (2020-06-18T14:50:08Z)
• 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)
• IoT Behavioral Monitoring via Network Traffic Analysis [0.45687771576879593]
  This thesis is the culmination of our efforts to develop techniques to profile the network behavioral pattern of IoTs. We develop a robust machine learning-based inference engine trained with attributes from traffic patterns. We demonstrate real-time classification of 28 IoT devices with over 99% accuracy.
  arXiv  Detail & Related papers  (2020-01-28T23:13:12Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

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.