Advanced Malware Detection Techniques

Últimos artículos

• SkillDRE: Dual-Stage Red-Team Evolution of Agent Skills via Pre-Execution and Runtime Feedback

  Pengyu Zhu, Jingyi Yang, Yi Liu, Li Sun, Sen Su · 29 de septiembre de 2026

  Agent skills package instructions, executable code, and task-specific resources into reusable artifacts that agents can improve using execution feedback. The same mechanism also enables attackers to evolve malicious skills, making them more effective and less detectable. However, a candidate skill m…

• Classifier-Dependent Benefits of Pseudo-Labeling for Semi-Supervised Android Malware Attribution

  Md Rafid Islam, Zahid Hasan, Hafiz Abdur Rahman · 25 de septiembre de 2026

  Detecting and classifying Android malware families remains challenging due to high feature dimensionality, class imbalance, and the high cost of expert-labeled data. Semi-supervised learning (SSL) offers a way to leverage unlabeled samples, but prior works rarely test whether SSL benefits generalize…

• Enhancing Multiclass Malware Classification in Resource-Constrained Environments

  Abdul Khalek Alve, Alif Rahman, Saadman Zaman, Sazzad Hossen Himel, Muhammad Iqbal Hossain · 24 de septiembre de 2026

  The emergence of multi-class malware attacks such as ransomware, spyware, trojans, etc., presents an increasing and serious threat to cybersecurity, particularly in resourceconstrained environments like IoT devices. Existing machine learning models have achieved nearly perfect accuracy in binary mal…

• On the Effect of Bit-Level Parameter Perturbations in Machine Learning and Deep Learning Models

  Akanksha Raghapur, Mark Stamp · 23 de septiembre de 2026

  In this chapter, we investigate how classical machine learning models respond to small, targeted modifications in their parameters. We compare and contrast these results to analogous experiments on deep learning models. For classical learning models, we consider Hidden Markov Models (HMM) and Suppor…

• Fast And Accurate Text Content File Type Identification

  Manu Nandan, Michael Brautbar, Edward Raff · 21 de septiembre de 2026

  A common requirement across organizations is to have a tool that can identify file types based on their contents, particularly in the cybersecurity domain where magic numbers and file extensions can not be trusted. While existing tools work well in practice, there is plenty of room for improvement e…

• Delphi Scanner: efficient and interpretable static malware detection via API sequence modeling

  Bijied Brahimi, Vincent Cohadon, Gabriel Glazman, Rayan Al Mohaize, Omran Berjawi, Rida Khatoun · 18 de septiembre de 2026

  Static malware detection for Windows Portable Executable files demands a careful balance between detection effectiveness, computational efficiency, and analytical interpretability. This paper introduces Delphi Scanner, a static malware detection system for Windows PE files that balances efficiency w…

• ALIBI: Adversarial Legitimacy Injection in Binary Input against LLM Malware Analyzers

  Hyeongjun Choi, Wonyoung Jung, Haehoon Seo, Sungyup Nam · 18 de septiembre de 2026

  Large language models are being integrated into malware triage workflows as reasoning components that summarize static evidence and produce analyst-facing verdicts. This paper shows that the same reasoning capability introduces a new attack surface. We present ALIBI, a semantic cover story attack ag…

• Permutation-Based Stegomalware in Large Language Models: Threats and Countermeasures

  Danny Wood, James Stringer · 16 de septiembre de 2026

  The difficulty of training large language models (LLMs), together with their ubiquity, raises the threat of stegomalware, where malicious payloads are embedded into model weights. Recent work has demonstrated the use of permutation symmetry in model weights to mitigate these threats, but failed to s…

• PIDS-Bench: Evaluating Prompt-Injection Detectors Under Over-Defense, Obfuscation, and Distribution Shift

  Yusuf Khalid Shire, Sang-Chul Kim · 15 de septiembre de 2026

  Prompt-injection detectors are typically evaluated using aggregate F1 on in-distribution test data, which offers limited insight into behavior under distribution shift, particularly on the benign side of the decision boundary, where false positives impose direct operational cost yet are seldom measu…

• SENTINEL: A Multi-Pathway Architecture for Detecting Living-Off-the-Land APT Attacks on Windows Command Lines

  Ahad Bin Islam Shoeb, Kamrul Hasan, Jamal Uddin Tanvin, Liang Hong, Imtiaz Ahmed, Md Arif Billah, Al Amin · 15 de septiembre de 2026

  Living-Off-the-Land (LOTL) is the dominant evasion technique of Advanced Persistent Threat (APT) actors, exploiting legitimate Windows utilities to conduct malicious operations without deploying custom malware and enabling state-sponsored campaigns to maintain persistent access within military and c…

• Don't Trust the Super-App: A Case Study of Russia's Max

  Richa Priyanka, Aaron Ortwein, Joel Reardon, Michael Specter, Piyush Kumar Sharma, Roya Ensafi · 11 de septiembre de 2026

  Super-apps, an emerging mobile architecture, host third-party mini-apps inside a single app, allowing users to access diverse services. A decade of security research on the super-app ecosystem has all assumed super-apps to be a trusted intermediary. We argue this implicit trust is difficult to justi…

• Trust Me, I'm Your Developer: Self-Issued Authentication in Large Language Models

  Syed Ghazanfar Abbas, Dongyan Xu · 10 de septiembre de 2026

  Large language model (LLM) security has largely focused on role-playing jailbreaks, with less attention to what happens when a user asks an LLM to verify an identity claim through a test designed by the model itself. We study this behavior through a staged developer-identity experiment with ChatGPT,…

• WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic

  Heba Osama, Zeyad Ahmed, Mohamed Amgad, Ahmed Saafan, Jana Elfeky, Mariam Abdelati, Haitham Ghalwash · 9 de septiembre de 2026

  Web Application Firewalls (WAFs) mainly rely on signatures to detect known attacks, which can leave gaps against modified or previously unseen payloads. Positive security provides a complementary approach by learning legitimate traffic and blocking inputs that fall outside the learned profile. Howev…

• A TTP by TTP Approach: Precise Malware Detection via Malicious TTP Recognition

  Yashovardhan Sharma · 9 de septiembre de 2026

  Machine learning methods, and especially neural networks, are now routinely used for malware detection in network traffic. Though very effective, systems based on such methods often (i) are purely data-driven, ignoring the substantial body of available knowledge about the tactics, techniques, and pr…

• SCRIPTIOC-BENCH: A Benchmark for Recognizing Actionable Threat Intelligence from Script-Based Malware using LLMs

  Hanna Kim, Jian Cui, Minkyoo Song, Hwanjo Heo, Seungwon Shin, Kimin Lee, Xiaojing Liao · 9 de septiembre de 2026

  Script-based malware remains a prevalent attack technique. These scripts often contain indicators of compromise (IOCs) that provide actionable threat intelligence. However, statically recovering such indicators is challenging, as relevant values may be dispersed or transformed within code. Although …

• Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution

  Mubashar Iqbal, Asifullah Khan · 7 de septiembre de 2026

  Ransomware detection and family attribution require analysis of different modalities because it can use packing, obfuscation, process manipulation and runtime evasion techniques. However, conventional multimodal usually uses all available modalities for every sample resulting in unnecessary computat…

• REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

  Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia, Alexander Herzog, Myles Foley, Chris Hicks, Lorenzo Cavallaro, Fabio Pierazzi · 31 de agosto de 2026

  To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-the-art attacks do not effectively model realistic adversaries, as they often assume access to privileged information su…

• Beyond F1: Evaluating Coverage and Failure Recovery in AI Model Security Scanners

  Qianlong Lan, Vinothini Pandurangan, Anuj Kaul, Indranil Sanyal · 28 de agosto de 2026

  Static scanners are increasingly used to identify executable or otherwise unsafe content in machine- learning artifacts, yet conventional evaluation metrics characterize only cases where a scanner yields a usable security judgment. We evaluate ModelScan, ModelAudit, and Fickling using a controlled, …

• Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition

  Kyle Stein, Guillermo Francia, III Eman El-Sheikh, Andrew Arash Mahyari · 25 de agosto de 2026

  The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forgetting, where previously learned knowledge is overwritten. While continual learni…

• Cross-Corpus Evaluation of Generalizable Vulnerability Detection in IoT Firmware

  Sadib Hassan Rumman, Md. Shariful Islam, Md. Rayhanur Rahman · 13 de agosto de 2026

  IoT firmware vulnerability detection remains challenging due to heterogeneous firmware ecosystems, resource-constrained platforms, and limitations in existing benchmarks. Many datasets are synthetic or general-purpose and lack human-verified, contamination-screened annotations, limiting evidence on …

• The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark

  Jeremy Spence, Nicholas Assaderaghi, Jinhao Zhu, Nikil Ravi, Raluca Ada Popa, Guannan Wei, Yangruibo Ding, Zhuo Zhang · 13 de agosto de 2026

  AI agents are rapidly improving in cybersecurity capabilities when the source code is available for analysis, yet much of the software most consequential to cybersecurity, including malware, firmware, and proprietary applications, is available only as binaries. Analyzing such software requires rever…

• Security and Privacy Taxonomy Generation from Mobile App Reviews

  Moghis Fereidouni, Vinaik Chhetri, Umar Farooq, A. B. Siddique · 11 de agosto de 2026

  Mobile app reviews are a rich, continuously renewing source of how users experience privacy and security, yet existing taxonomies of these concerns are hand-crafted and cannot keep pace with the evolving nature of the data. Automating taxonomy construction is the natural response, but scalability is…

• Robust Context-Aware Detection of Malicious Instructions in Text

  Buzhao Liu, Xinhang Ma, Yevgeniy Vorobeychik · 7 de agosto de 2026

  The remarkable instruction-following ability of modern LLMs has enabled their practical use as the minds of agents that can autonomously complete increasingly complex tasks. Therein, however, also lies their vulnerability to attacks which embed malicious instructions in text, common variants of whic…

• AegisShield: Democratizing Cyber Threat Modeling with Generative AI

  Matthew Grofsky · 7 de agosto de 2026

  The increasing sophistication of technology systems makes traditional threat modeling hard to scale, especially for small organizations with limited resources. This paper develops and evaluates AegisShield, a generative AI enhanced threat modeling tool that implements STRIDE and MITRE ATT&CK to auto…

• ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

  Md Faisal Ahmed, Zarin Tasnim Biash, Abu Raihan Shakil, Ahmed Ann Noor Ryen, Arman Hossain, Faisal Bin Ashraf, Muhammad Iqbal Hossain · 5 de agosto de 2026

  The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications. Although applications are required to undergo malware screening before being published on official a…