RX-INT: A Kernel Engine for Real-Time Detection and Analysis of In-Memory Threats

• URL: http://arxiv.org/abs/2508.03879v1
• Date: Tue, 05 Aug 2025 19:43:25 GMT
• Title: RX-INT: A Kernel Engine for Real-Time Detection and Analysis of In-Memory Threats
• Authors: Arjun Juneja,
• Abstract summary: We present RX-INT, a kernel-assisted system featuring an architecture that provides resilience against TOCTOU attacks.<n> RX-INT introduces a detection engine that combines a real-time thread creation monitor with a stateful Virtual Address Descriptor (VAD) scanner.<n>In our evaluation, RX-INT successfully detected a manually mapped region that was not identified by PE-sieve.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Malware and cheat developers use fileless execution techniques to evade traditional, signature-based security products. These methods include various types of manual mapping, module stomping, and threadless injection which work entirely within the address space of a legitimate process, presenting a challenge for detection due to ambiguity between what is legitimate and what isn't. Existing tools often have weaknesses, such as a dependency on Portable Executable (PE) structures or a vulnerability to time-of-check-to-time-of-use (TOCTOU) race conditions where an adversary cleans up before a periodic scan has the chance to occur. To address this gap, we present RX-INT, a kernel-assisted system featuring an architecture that provides resilience against TOCTOU attacks. RX-INT introduces a detection engine that combines a real-time thread creation monitor with a stateful Virtual Address Descriptor (VAD) scanner alongside various heuristics within. This engine snapshots both private and image-backed memory regions, using real-time memory hashing to detect illicit modifications like module stomping. Critically, we demonstrate a higher detection rate in certain benchmarks of this approach through a direct comparison with PE-sieve, a commonly used and powerful memory forensics tool. In our evaluation, RX-INT successfully detected a manually mapped region that was not identified by PE-sieve. We then conclude that our architecture represents a tangible difference in the detection of fileless threats, with direct applications in the fields of anti-cheat and memory security.

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)
• SHIELD: Secure Host-Independent Extensible Logging for Tamper-Proof Detection and Real-Time Mitigation of Ransomware Threats [17.861324495723487]
  We introduce SHIELD: a metric acquisition framework leveraging low-level monitoring and Network Block Device (NBD) technology to provide off-host, tamper-proof measurements for continuous observation of disk activity.<n>We employ deep features along with simplified metrics aggregated based on frequency of disk actions, making the metrics impervious to obfuscation while avoiding reliance on vulnerable host-based logs.<n>In a proof-of-concept deployment, we demonstrate real-time mitigation using models trained on these metrics by halting malicious disk operations after ransomware detection with minimum file loss and memory corruption.
  arXiv  Detail & Related papers  (2025-01-28T01:33:03Z)
• 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)
• A Lean Transformer Model for Dynamic Malware Analysis and Detection [0.0]
  Malware is a fast-growing threat to the modern computing world and existing lines of defense are not efficient enough to address this issue. Previous works have shown some success leveraging Neural Networks and API calls sequences extracted from execution reports. In this paper, we design an emulation-Only model, based on the Transformers architecture, to detect malicious files.
  arXiv  Detail & Related papers  (2024-08-05T08:46:46Z)
• The Reversing Machine: Reconstructing Memory Assumptions [2.66610643553864]
  A malicious kernel-level driver can bypass OS-level anti-virus mechanisms easily. We present textitThe Reversing Machine (TRM), a new hypervisor-based memory introspection design for reverse engineering. We show that TRM can detect each threat and that, out of 24 state-of-the-art AV solutions, only TRM can detect the most advanced threats.
  arXiv  Detail & Related papers  (2024-05-01T03:48:22Z)
• JITScanner: Just-in-Time Executable Page Check in the Linux Operating System [6.725792100548271]
  JITScanner is developed as a Linux-oriented package built upon a Loadable Kernel Module (LKM) It integrates a user-level component that communicates efficiently with the LKM using scalable multi-processor/core technology. JITScanner's effectiveness in detecting malware programs and its minimal intrusion in normal runtime scenarios have been extensively tested.
  arXiv  Detail & Related papers  (2024-04-25T17:00:08Z)
• Discovering Malicious Signatures in Software from Structural Interactions [7.06449725392051]
  We propose a novel malware detection approach that leverages deep learning, mathematical techniques, and network science. Our approach focuses on static and dynamic analysis and utilizes the Low-Level Virtual Machine (LLVM) to profile applications within a complex network. Our approach marks a substantial improvement in malware detection, providing a notably more accurate and efficient solution.
  arXiv  Detail & Related papers  (2023-12-19T23:42:20Z)
• 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)
• Implicit Motion Handling for Video Camouflaged Object Detection [60.98467179649398]
  We propose a new video camouflaged object detection (VCOD) framework. It can exploit both short-term and long-term temporal consistency to detect camouflaged objects from video frames.
  arXiv  Detail & Related papers  (2022-03-14T17:55:41Z)
• 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)
• 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)

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.