Self-Supervised Vision Transformers for Malware Detection

• URL: http://arxiv.org/abs/2208.07049v1
• Date: Mon, 15 Aug 2022 07:49:58 GMT
• Title: Self-Supervised Vision Transformers for Malware Detection
• Authors: Sachith Seneviratne, Ridwan Shariffdeen, Sanka Rasnayaka and Nuran Kasthuriarachchi
• Abstract summary: This paper presents SHERLOCK, a self-supervision based deep learning model to detect malware based on the Vision Transformer (ViT) architecture. Our proposed model is also able to outperform state-of-the-art techniques for multi-class malware classification of types and family with macro-F1 score of.497 and.491 respectively.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Previously unseen malware which is not determined by security vendors are often used in these attacks and it is becoming inevitable to find a solution that can self-learn from unlabeled sample data. This paper presents SHERLOCK, a self-supervision based deep learning model to detect malware based on the Vision Transformer (ViT) architecture. SHERLOCK is a novel malware detection method which learns unique features to differentiate malware from benign programs with the use of image-based binary representation. Experimental results using 1.2 million Android applications across a hierarchy of 47 types and 696 families, shows that self-supervised learning can achieve an accuracy of 97% for the binary classification of malware which is higher than existing state-of-the-art techniques. Our proposed model is also able to outperform state-of-the-art techniques for multi-class malware classification of types and family with macro-F1 score of .497 and .491 respectively.

Related papers

• MASKDROID: Robust Android Malware Detection with Masked Graph Representations [56.09270390096083]
  We propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware. We introduce a masking mechanism into the Graph Neural Network based framework, forcing MASKDROID to recover the whole input graph. This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2024-09-29T07:22:47Z)
• Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach [0.0]
  We present a machine learning-based system for detecting obfuscated malware that is highly accurate, lightweight and interpretable. Our system is capable of detecting 15 malware subtypes despite being exclusively trained on one malware subtype, namely the Transponder from the Spyware family. The Transponder-focused model exhibited high accuracy, exceeding 99.8%, with an average processing speed of 5.7 microseconds per file.
  arXiv  Detail & Related papers  (2024-07-07T12:41:40Z)
• Understanding crypter-as-a-service in a popular underground marketplace [51.328567400947435]
  Crypters are pieces of software whose main goal is to transform a target binary so it can avoid detection from Anti Viruses (AVs) applications. The crypter-as-a-service model has gained popularity, in response to the increased sophistication of detection mechanisms. This paper provides the first study on an online underground market dedicated to crypter-as-a-service.
  arXiv  Detail & Related papers  (2024-05-20T08:35:39Z)
• 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)
• Towards a Fair Comparison and Realistic Design and Evaluation Framework of Android Malware Detectors [63.75363908696257]
  We analyze 10 influential research works on Android malware detection using a common evaluation framework. We identify five factors that, if not taken into account when creating datasets and designing detectors, significantly affect the trained ML models. We conclude that the studied ML-based detectors have been evaluated optimistically, which justifies the good published results.
  arXiv  Detail & Related papers  (2022-05-25T08:28:08Z)
• Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors [49.34155921877441]
  We propose an explainability-guided and model-agnostic testing framework for robustness of malware detectors. We then use this framework to test several state-of-the-art malware detectors' abilities to detect manipulated malware. Our findings shed light on the limitations of current malware detectors, as well as how they can be improved.
  arXiv  Detail & Related papers  (2021-11-19T08:02:38Z)
• Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware [5.293553970082943]
  Existing malware detection methods fail to correctly classify different malware families when obfuscated malware samples are present in the training dataset. We propose a novel task-aware few-shot-learning-based Siamese Neural Network that is resilient against such control flow obfuscation techniques. Our proposed approach is highly effective in recognizing unique malware signatures, thus correctly classifying malware samples that belong to the same malware family.
  arXiv  Detail & Related papers  (2021-10-26T04:44:13Z)
• Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery [23.294653273180472]
  We show how a malicious actor trains a surrogate model to discover binary mutations that cause an instance to be misclassified. Then, mutated malware is sent to the victim model that takes the place of an antivirus API to test whether it can evade detection.
  arXiv  Detail & Related papers  (2021-06-15T03:31:02Z)
• Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model [11.701290164823142]
  MalRNN is a novel approach to automatically generate evasive malware variants without restrictions. MalRNN effectively evades three recent deep learning-based malware detectors and outperforms current benchmark methods.
  arXiv  Detail & Related papers  (2020-12-14T22:54:53Z)
• Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers [47.828297621738265]
  We show how an attacker can launch a sophisticated and efficient poisoning attack targeting the dataset used to train a malware classifier. As opposed to other poisoning attacks in the malware detection domain, our attack does not focus on malware families but rather on specific malware instances that contain an implanted trigger. We propose a comprehensive detection approach that could serve as a future sophisticated defense against this newly discovered severe threat.
  arXiv  Detail & Related papers  (2020-10-30T15:27:44Z)
• Classifying Malware Images with Convolutional Neural Network Models [2.363388546004777]
  In this paper, we use several convolutional neural network (CNN) models for static malware classification. The Inception V3 model achieves a test accuracy of 99.24%, which is better than the accuracy of 98.52% achieved by the current state-of-the-art system.
  arXiv  Detail & Related papers  (2020-10-30T07:39:30Z)

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.