Enhancing JavaScript Malware Detection through Weighted Behavioral DFAs

• URL: http://arxiv.org/abs/2505.21406v1
• Date: Tue, 27 May 2025 16:29:47 GMT
• Title: Enhancing JavaScript Malware Detection through Weighted Behavioral DFAs
• Authors: Pedro Pereira, José Gonçalves, João Vitorino, Eva Maia, Isabel Praça,
• Abstract summary: This work addresses JavaScript malware detection to enhance client-side web application security with a behavior-based system.<n>The ability to detect malicious JavaScript execution sequences is a critical problem in modern web security as attack techniques become more sophisticated.<n>This study introduces a new system for detecting JavaScript malware using a Deterministic Finite Automaton (DFA) along with a weighted-behavior system, which we call behavior DFA.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work addresses JavaScript malware detection to enhance client-side web application security with a behavior-based system. The ability to detect malicious JavaScript execution sequences is a critical problem in modern web security as attack techniques become more sophisticated. This study introduces a new system for detecting JavaScript malware using a Deterministic Finite Automaton (DFA) along with a weighted-behavior system, which we call behavior DFA. This system captures malicious patterns and provides a dynamic mechanism to classify new sequences that exhibit partial similarity to known attacks, differentiating them between benign, partially malicious, and fully malicious behaviors. Experimental evaluation on a dataset of 1,058 sequences captured in a real-world environment demonstrates the capability of the system to detect and classify threats effectively, with the behavior DFA successfully identifying exact matches and partial similarities to known malicious behaviors. The results highlight the adaptability of the system in detecting emerging threats while maintaining transparency in decision making.

Related papers

• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• OMNISEC: LLM-Driven Provenance-based Intrusion Detection via Retrieval-Augmented Behavior Prompting [4.71781133841068]
  Provenance-based Intrusion Detection Systems (PIDSes) have been widely used for endpoint threat analysis.<n>Due to the evolution of attack techniques, rules cannot dynamically model all the characteristics of attackers.<n>Anomaly-based detection systems face a massive false positive problem because they cannot distinguish between changes in normal behavior and real attack behavior.
  arXiv  Detail & Related papers  (2025-03-05T02:08:12Z)
• Efficient Phishing URL Detection Using Graph-based Machine Learning and Loopy Belief Propagation [12.89058029173131]
  We propose a graph-based machine learning model for phishing URL detection.<n>We integrate URL structure and network-level features such as IP addresses and authoritative name servers.<n>Experiments on real-world datasets demonstrate our model's effectiveness by achieving F1 score of up to 98.77%.
  arXiv  Detail & Related papers  (2025-01-12T19:49:00Z)
• Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection [0.3199881502576702]
  Intrusion detection system (IDS) can essentially mitigate threats by providing alerts. In order to detect these threats various machine learning (ML) and deep learning (DL) models have been proposed. In this paper, we propose an explainable artificial intelligence (XAI) based visual analysis approach using overlapping SHAP plots.
  arXiv  Detail & Related papers  (2024-11-04T23:08:34Z)
• PARIS: A Practical, Adaptive Trace-Fetching and Real-Time Malicious Behavior Detection System [6.068607290592521]
  We propose adaptive trace fetching, lightweight, real-time malicious behavior detection system. Specifically, we monitor malicious behavior with Event Tracing for Windows (ETW) and learn to selectively collect maliciousness-related APIs or call stacks. As a result, we can monitor a wider range of APIs and detect more intricate attack behavior.
  arXiv  Detail & Related papers  (2024-11-02T14:52:04Z)
• 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)
• Explainability-Informed Targeted Malware Misclassification [0.0]
  Machine learning models for malware classification into categories have shown promising results. Deep neural networks have shown vulnerabilities against intentionally crafted adversarial attacks. Our paper explores such adversarial vulnerabilities of neural network based malware classification system.
  arXiv  Detail & Related papers  (2024-05-07T04:59:19Z)
• 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)
• 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)
• Learning Explainable Representations of Malware Behavior [3.718942345103135]
  We develop a neural network that processes network-flow data into comprehensible emphnetwork events We then use the emphintegrated-gradients method to highlight events that jointly constitute the characteristic behavioral pattern of the threat. We demonstrate how this system detects njRAT and other malware based on behavioral patterns.
  arXiv  Detail & Related papers  (2021-06-23T11:50:57Z)
• 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)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.