Catch'em all: Classification of Rare, Prominent, and Novel Malware Families

• URL: http://arxiv.org/abs/2403.02546v1
• Date: Mon, 4 Mar 2024 23:46:19 GMT
• Title: Catch'em all: Classification of Rare, Prominent, and Novel Malware Families
• Authors: Maksim E. Eren, Ryan Barron, Manish Bhattarai, Selma Wanna, Nicholas Solovyev, Kim Rasmussen, Boian S. Alexandrov, Charles Nicholas,
• Abstract summary: Malware remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens. These challenges include detection of novel malware and the ability to perform malware classification in the face of class imbalance.
• Score: 3.147175286021779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens, motivating the use of data-driven machine learning (ML) methods for analysis. However, shortcomings in existing ML approaches hinder their mass adoption. These challenges include detection of novel malware and the ability to perform malware classification in the face of class imbalance: a situation where malware families are not equally represented in the data. Our work addresses these shortcomings with MalwareDNA: an advanced dimensionality reduction and feature extraction framework. We demonstrate stable task performance under class imbalance for the following tasks: malware family classification and novel malware detection with a trade-off in increased abstention or reject-option rate.

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)
• MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware [3.536024441537599]
  Malware is one of the most dangerous and costly cyber threats to national security. Here we introduce and showcase preliminary capabilities of a new method that can perform precise identification of novel malware families.
  arXiv  Detail & Related papers  (2023-09-04T04:27:39Z)
• CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario [14.059646012441313]
  We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR) In this paper, we propose a novel model that can conservatively synthesize malware instances to mimic unknown malware families. We also build a new large-scale malware dataset, named MAL-100, to fill the gap of lacking large open-set malware benchmark dataset.
  arXiv  Detail & Related papers  (2023-05-02T07:31:42Z)
• 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)
• 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)
• 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)
• 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)
• A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images [0.22843885788439805]
  We use malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. We show that this technique is successful in differentiating classes of malware based on the features extracted.
  arXiv  Detail & Related papers  (2021-04-14T06:55:52Z)
• 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)
• 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)

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.