AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections

• URL: http://arxiv.org/abs/2601.06219v1
• Date: Fri, 09 Jan 2026 02:14:34 GMT
• Title: AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections
• Authors: Rakesh Keshava, Sathish Kuppan Pandurangan, M. Sakthivanitha, Sankaranainar Parmsivan, Goutham Sunkara, R. Maruthi,
• Abstract summary: This study presents a new hybrid context-aware malware detection framework based on artificial intelligence (AI)<n>The new approach provides superior performances with an accuracy of 97.3%, only a 1.5% false positive rate and minimal detection delay compared to several existing machine learning(ML) and deep learning(DL) established methods in the same fields.
• Score: 0.24919281650930605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need for intelligent systems to accurately and proactively identify and prevent malware infections. This study presents a new hybrid context-aware malware detection framework(HCAMDF) based on artificial intelligence (AI), which combines static file analysis, dynamic behavioural analysis, and contextual metadata to provide more accurate and timely detection. HCADMF has a multi-layer architecture, which consists of lightweight static classifiers such as Long Short Term Memory (LSTM) for real-time behavioral analysis, and an ensemble risk scoring through the integration of multiple layers of prediction. Experimental evaluations of the new/methodology with benchmark datasets, EMBER and CIC-MalMem2022, showed that the new approach provides superior performances with an accuracy of 97.3%, only a 1.5% false positive rate and minimal detection delay compared to several existing machine learning(ML) and deep learning(DL) established methods in the same fields. The results show strong evidence that hybrid AI can detect both existing and novel malware variants, and lay the foundation on intelligent security systems that can enable real-time detection and adapt to a rapidly evolving threat landscape.

Related papers

• SecureScan: An AI-Driven Multi-Layer Framework for Malware and Phishing Detection Using Logistic Regression and Threat Intelligence Integration [0.0]
  This work presents SecureScan, an AI-driven, triplelayer detection framework.<n>It integrates logistic regression-based classification, analysis, and external threat intelligence via the VirusTotal API.<n>On benchmark datasets, SecureScan achieves 93.1 percent accuracy with balanced precision.
  arXiv  Detail & Related papers  (2026-02-11T11:26:11Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection [38.083049237330826]
  This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weaknessions (CWEs)<n>Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance.<n> challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research.
  arXiv  Detail & Related papers  (2025-06-11T18:43:51Z)
• Algorithmic Segmentation and Behavioral Profiling for Ransomware Detection Using Temporal-Correlation Graphs [0.0]
  A novel framework was introduced, leveraging Temporal-Correlation Graphs to model the intricate relationships and temporal patterns inherent in malicious operations.<n>Experiments demonstrated the framework's effectiveness across diverse ransomware families, with consistently high precision, recall, and overall detection accuracy.<n>The research contributes to advancing cybersecurity technologies by integrating dynamic graph analytics and machine learning for future innovations in threat detection.
  arXiv  Detail & Related papers  (2025-01-29T06:09:25Z)
• A Sysmon Incremental Learning System for Ransomware Analysis and Detection [1.495391051525033]
  In the face of increasing cyber threats, particularly ransomware attacks, there is a pressing need for advanced detection and analysis systems.<n>Most of these proposals leverage non-incremental learning approaches that require the underlying models to be updated from scratch to detect new ransomware.<n>This approach is problematic because it leaves sensitive data vulnerable to attack during retraining, as newly emerging ransomware strains may go undetected until the model is updated.<n>We present the Sysmon Incremental Learning System for Analysis and Detection (SILRAD), which enables continuous updates to the underlying model and effectively closes the training gap.
  arXiv  Detail & Related papers  (2025-01-02T06:22:58Z)
• Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks [47.18575262588692]
  We propose a novel Multi-Space Prototypical Learning framework tailored for few-shot attack detection.<n>By leveraging Polyak-averaged prototype generation, the framework stabilizes the learning process and effectively adapts to rare and zero-day attacks.<n> Experimental results on benchmark datasets demonstrate that MSPL outperforms traditional approaches in detecting low-profile and novel attack types.
  arXiv  Detail & Related papers  (2024-12-28T00:09:46Z)
• A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion [2.3039261241391586]
  This study employs the minhash algorithm to convert binary files of malware into grayscale images. The study utilizes IDA Pro to decompile and extract opcode sequences, applying N-gram and tf-idf algorithms for feature vectorization. A CNN-BiLSTM fusion model is designed to simultaneously process image features and opcode sequences, enhancing classification performance.
  arXiv  Detail & Related papers  (2024-10-12T07:10:44Z)
• Leveraging LSTM and GAN for Modern Malware Detection [0.4799822253865054]
  This paper proposes the utilization of the Deep Learning Model, LSTM networks, and GAN classifiers to amplify malware detection accuracy and speed. The research outcomes come out with 98% accuracy that shows the efficiency of deep learning plays a decisive role in proactive cybersecurity defense.
  arXiv  Detail & Related papers  (2024-05-07T14:57:24Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• 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.