A Survey on Cross-Architectural IoT Malware Threat Hunting

• URL: http://arxiv.org/abs/2306.07989v1
• Date: Fri, 9 Jun 2023 19:01:32 GMT
• Title: A Survey on Cross-Architectural IoT Malware Threat Hunting
• Authors: Anandharaju Durai Raju, Ibrahim Abualhaol, Ronnie Salvador Giagone, Yang Zhou, and Shengqiang Huang
• Abstract summary: Research works on hunting Windows PE-based malwares are maturing, whereas the developments on Linux malware threat hunting are relatively scarce. With the advent of the Internet of Things (IoT) era, smart devices that are getting integrated into human life have become a hackers highway for their malicious activities. This study aims at providing a comprehensive survey on the latest developments in cross-architectural IoT malware detection and classification approaches.
• Score: 2.767968065747037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, the increase in non-Windows malware threats had turned the focus of the cybersecurity community. Research works on hunting Windows PE-based malwares are maturing, whereas the developments on Linux malware threat hunting are relatively scarce. With the advent of the Internet of Things (IoT) era, smart devices that are getting integrated into human life have become a hackers highway for their malicious activities. The IoT devices employ various Unix-based architectures that follow ELF (Executable and Linkable Format) as their standard binary file specification. This study aims at providing a comprehensive survey on the latest developments in cross-architectural IoT malware detection and classification approaches. Aided by a modern taxonomy, we discuss the feature representations, feature extraction techniques, and machine learning models employed in the surveyed works. We further provide more insights on the practical challenges involved in cross-architectural IoT malware threat hunting and discuss various avenues to instill potential future research.

Related papers

• Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges [0.0]
  The Internet of Things (IoT) is one of the fastest-growing computing industries. Traditional malware detection methods are becoming ineffective against these new types of malware. Machine Learning (ML) and Deep Learning (DL) approaches have proven effective in dealing with these new IoT malware variants.
  arXiv  Detail & Related papers  (2024-10-14T19:04:43Z)
• Explainable Malware Analysis: Concepts, Approaches and Challenges [0.0]
  We review the current state-of-the-art ML-based malware detection techniques and popular XAI approaches. We discuss research implementations and the challenges of explainable malware analysis. This theoretical survey serves as an entry point for researchers interested in XAI applications in malware detection.
  arXiv  Detail & Related papers  (2024-09-09T08:19:33Z)
• Classification of cyber attacks on IoT and ubiquitous computing devices [49.1574468325115]
  This paper provides a classification of IoT malware. Major targets and used exploits for attacks are identified and referred to the specific malware. The majority of current IoT attacks continue to be of comparably low effort and level of sophistication and could be mitigated by existing technical measures.
  arXiv  Detail & Related papers  (2023-12-01T16:10:43Z)
• 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)
• Malware Detection and Prevention using Artificial Intelligence Techniques [7.583480439784955]
  Security has become a major issue due to the increase in malware activity. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity.
  arXiv  Detail & Related papers  (2022-06-26T02:41:46Z)
• Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art [44.975088044180374]
  This paper focuses on malware with the file format of portable executable (PE) in the family of Windows operating systems, namely Windows PE malware. We first outline the general learning framework of Windows PE malware detection based on ML/DL. We then highlight three unique challenges of performing adversarial attacks in the context of PE malware.
  arXiv  Detail & Related papers  (2021-12-23T02:12:43Z)
• A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmware [0.0]
  This paper employs a number of machine learning algorithms to classify IoT firmware and the best performing models are reported. Deep learning approaches including Convolutional and Fully Connected Neural Networks are also explored.
  arXiv  Detail & Related papers  (2021-11-03T17:55:51Z)
• Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation [2.309914459672557]
  We introduce a novel IoT malware traffic analysis approach using neural network and binary visualisation. The prime motivation of the proposed approach is to faster detect and classify new malware (zero-day malware)
  arXiv  Detail & Related papers  (2021-09-08T00:21:45Z)
• Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [54.478842696269304]
  The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. Our paper addresses both machine learning experts and safety engineers.
  arXiv  Detail & Related papers  (2021-04-29T09:54:54Z)
• Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
  adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
  arXiv  Detail & Related papers  (2020-08-17T07:16:57Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

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.