Understanding crypter-as-a-service in a popular underground marketplace
- URL: http://arxiv.org/abs/2405.11876v2
- Date: Thu, 6 Jun 2024 11:11:03 GMT
- Title: Understanding crypter-as-a-service in a popular underground marketplace
- Authors: Alejandro de la Cruz, Sergio Pastrana,
- Abstract summary: 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.
- Score: 51.328567400947435
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Crypters are pieces of software whose main goal is to transform a target binary so it can avoid detection from Anti Viruses (AVs from now on) applications. They work similar to packers, by taking a malware binary and applying a series of modifications, obfuscations and encryptions to output a binary that evades one or more AVs. The goal is to remain fully undetected, or FUD in the hacking jargon, while maintaining its (often malicious) functionality. In line to the growth of commoditization in cybercrime, the crypter-as-a-service model has gained popularity, in response to the increased sophistication of detection mechanisms. In this business model, customers receive an initial crypter which is soon updated once becomes detected by anti-viruses. This paper provides the first study on an online underground market dedicated to crypter-as-a-service. We compare the most relevant products in sale, analyzing the existent social network on the platform and comparing the different features that they provide. We also conduct an experiment as a case study, to validate the usage of one of the most popular crypters sold in the market, and compare the results before and after crypting binaries (both benign and malware), to show its effectiveness when evading antivirus engines.
Related papers
- Ransomware Detection Using Federated Learning with Imbalanced Datasets [0.0]
This paper presents a weighted cross-entropy loss function approach to mitigate dataset imbalance.
A detailed performance evaluation study is then presented for the case of static analysis using the latest Windows-based ransomware families.
arXiv Detail & Related papers (2023-11-13T21:21:39Z) - RansomAI: AI-powered Ransomware for Stealthy Encryption [0.5172201569251684]
RansomAI is a framework that learns the best encryption algorithm, rate, and duration that minimizes its detection.
It evades the detection of Ransomware-PoC affecting the Raspberry Pi 4 in a few minutes with >90% accuracy.
arXiv Detail & Related papers (2023-06-27T15:36:12Z) - 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) - 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) - Check Your Other Door! Establishing Backdoor Attacks in the Frequency
Domain [80.24811082454367]
We show the advantages of utilizing the frequency domain for establishing undetectable and powerful backdoor attacks.
We also show two possible defences that succeed against frequency-based backdoor attacks and possible ways for the attacker to bypass them.
arXiv Detail & Related papers (2021-09-12T12:44:52Z) - 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) - Data Augmentation Based Malware Detection using Convolutional Neural
Networks [0.0]
Cyber-attacks have been extensively seen due to the increase of malware in the cyber world.
The most important feature of this type of malware is that they change shape as they propagate from one computer to another.
This paper aims at providing an image augmentation enhanced deep convolutional neural network models for the detection of malware families in a metamorphic malware environment.
arXiv Detail & Related papers (2020-10-05T08:58:07Z) - 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) - MDEA: Malware Detection with Evolutionary Adversarial Learning [16.8615211682877]
MDEA, an Adversarial Malware Detection model uses evolutionary optimization to create attack samples to make the network robust against evasion attacks.
By retraining the model with the evolved malware samples, its performance improves a significant margin.
arXiv Detail & Related papers (2020-02-09T09:59:56Z)
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.