MalwareDNA: Simultaneous Classification of Malware, Malware Families,
and Novel Malware
- URL: http://arxiv.org/abs/2309.01350v1
- Date: Mon, 4 Sep 2023 04:27:39 GMT
- Title: MalwareDNA: Simultaneous Classification of Malware, Malware Families,
and Novel Malware
- Authors: Maksim E. Eren, Manish Bhattarai, Kim Rasmussen, Boian S. Alexandrov,
Charles Nicholas
- Abstract summary: 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.
- Score: 3.536024441537599
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Malware is one of the most dangerous and costly cyber threats to national
security and a crucial factor in modern cyber-space. However, the adoption of
machine learning (ML) based solutions against malware threats has been
relatively slow. Shortcomings in the existing ML approaches are likely
contributing to this problem. The majority of current ML approaches ignore
real-world challenges such as the detection of novel malware. In addition,
proposed ML approaches are often designed either for malware/benign-ware
classification or malware family classification. Here we introduce and showcase
preliminary capabilities of a new method that can perform precise
identification of novel malware families, while also unifying the capability
for malware/benign-ware classification and malware family classification into a
single framework.
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) - Catch'em all: Classification of Rare, Prominent, and Novel Malware Families [3.147175286021779]
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.
arXiv Detail & Related papers (2024-03-04T23:46:19Z) - 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) - 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) - Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A
Causal Language Model Approach [5.2424255020469595]
Adversarial Malware example Generation aims to generate evasive malware variants.
Black-box method has gained more attention than white-box methods.
In this study, we show that a novel DL-based causal language model enables single-shot evasion.
arXiv Detail & Related papers (2021-12-03T05:29:50Z) - 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)
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.