Malware Detection
90 papers with code • 2 benchmarks • 4 datasets
Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware
Source: The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey
EMBER
MalNet
IoT-23
Latest papers
Crystal ball: From innovative attacks to attack effectiveness classifier
This study presents a set of innovative problem-based evasion attacks against well-known Android malware detection systems, which decrease their detection rate by up to 97%.
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
As a significant representation of dynamic malware behavior, the API (Application Programming Interface) sequence, comprised of consecutive API calls, has progressively become the dominant feature of dynamic analysis methods.
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
seu-proactivesecurity-group/malpurifier
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Experimental results on two Android malware datasets demonstrate that MalPurifier outperforms the state-of-the-art defenses, and it significantly strengthens the vulnerable malware detector against 37 evasion attacks, achieving accuracies over 90. 91%.
Nebula: Self-Attention for Dynamic Malware Analysis
dtrizna/nebula
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Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment, and storing their actions in log reports.
Efficient Concept Drift Handling for Batch Android Malware Detection Models
Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining.
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting
The widespread adoption of the Android operating system has made malicious Android applications an appealing target for attackers.
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
stratosphereips/meme_malware_rl
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However, machine learning models are susceptible to adversarial attacks, requiring the testing of model and product robustness.
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance
As a consequence, our community still lacks an understanding of malware classification results: whether they are tied to the nature and distribution of the collected dataset, to what extent the number of families and samples in the training dataset influence performance, and how well static and dynamic features complement each other.
From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy
The paper also investigates how cyber offenders can use the GenAI tools in developing cyber attacks, and explore the scenarios where ChatGPT can be used by adversaries to create social engineering attacks, phishing attacks, automated hacking, attack payload generation, malware creation, and polymorphic malware.
Creating Valid Adversarial Examples of Malware
matouskozak/amg
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Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results.
