On deceiving malware classification with section injection
- URL: http://arxiv.org/abs/2208.06092v1
- Date: Fri, 12 Aug 2022 02:43:17 GMT
- Title: On deceiving malware classification with section injection
- Authors: Adeilson Antonio da Silva and Mauricio Pamplona Segundo
- Abstract summary: We investigate how to modify executable files to deceive malware classification systems.
This work's main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack and as a defensive method.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We investigate how to modify executable files to deceive malware
classification systems. This work's main contribution is a methodology to
inject bytes across a malware file randomly and use it both as an attack to
decrease classification accuracy but also as a defensive method, augmenting the
data available for training. It respects the operating system file format to
make sure the malware will still execute after our injection and will not
change its behavior. We reproduced five state-of-the-art malware classification
approaches to evaluate our injection scheme: one based on GIST+KNN, three CNN
variations and one Gated CNN. We performed our experiments on a public dataset
with 9,339 malware samples from 25 different families. Our results show that a
mere increase of 7% in the malware size causes an accuracy drop between 25% and
40% for malware family classification. They show that a automatic malware
classification system may not be as trustworthy as initially reported in the
literature. We also evaluate using modified malwares alongside the original
ones to increase networks robustness against mentioned attacks. Results show
that a combination of reordering malware sections and injecting random data can
improve overall performance of the classification. Code available at
https://github.com/adeilsonsilva/malware-injection.
Related papers
- Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits! [51.668411293817464]
Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines.
Academic research is often restrained to public datasets on the order of ten thousand samples.
We devise an approach to generate a benchmark of difficulty from a pool of available samples.
arXiv Detail & Related papers (2023-12-25T21:25:55Z) - MalDICT: Benchmark Datasets on Malware Behaviors, Platforms, Exploitation, and Packers [44.700094741798445]
Existing research on malware classification focuses almost exclusively on two tasks: distinguishing between malicious and benign files and classifying malware by family.
We have identified four tasks which are under-represented in prior work: classification by behaviors that malware exhibit, platforms that malware run on, vulnerabilities that malware exploit, and packers that malware are packed with.
We are releasing benchmark datasets for each of these four classification tasks, tagged using ClarAVy and comprising nearly 5.5 million malicious files in total.
arXiv Detail & Related papers (2023-10-18T04:36:26Z) - EMBERSim: A Large-Scale Databank for Boosting Similarity Search in
Malware Analysis [48.5877840394508]
In recent years there has been a shift from quantifications-based malware detection towards machine learning.
We propose to address the deficiencies in the space of similarity research on binary files, starting from EMBER.
We enhance EMBER with similarity information as well as malware class tags, to enable further research in the similarity space.
arXiv Detail & Related papers (2023-10-03T06:58:45Z) - 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) - Using Static and Dynamic Malware features to perform Malware Ascription [0.0]
We employ various Static and Dynamic features of malicious executables to classify malware based on their family.
We leverage Cuckoo Sandbox and machine learning to make progress in this research.
arXiv Detail & Related papers (2021-12-05T18:01:09Z) - 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) - 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) - 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.