Results 71 to 80 of about 3,386 (193)

Hybrid CNN and Autoencoder Deep Learning Model for Network Malware Detection

Sistemasi: Jurnal Sistem Informasi
Malware remains one of the primary threats to network security, continuously evolving with increasingly complex attack patterns that are difficult to detect using conventional methods.
Mayra Anggraini, Rama Aria Megantara
doaj   +1 more source

Governing Cybersecurity in the Digital Age: Mapping Comprehensive Policy Mixes With the Nodality‐Authority‐Treasure‐Organization Lens

Review of Policy Research, Volume 43, Issue 4, July 2026.
ABSTRACT The accelerated digitalisation of society has amplified cybersecurity threats and revealed their cross‐sectoral nature. Yet, the policy instruments used to address these challenges remain insufficiently examined. This study conducts a scoping review of 980 academic articles (2007–2024) and applies Hood's NATO framework (Nodality, Authority ...
Benedetta Cotta, Maria Stella Righettini
wiley   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source

Fighting fire with fire – a Pre-emptive approach to restore control over IT assets from malware infection

, 2012
Malware is a major threat as they induce multiple risks to infected organizations. Current Anti-Malware solutions meant to keep Malware away are challenged on how to keep the risks at bay effectively. When a Malware manages to penetrate an organization’s
Pan, J.Y.
core  

An Intelligent Feature Engineering‐Driven Hybrid Framework for Adversarial Domain Name System Tunneling Detection

Advanced Intelligent Systems, Volume 8, Issue 6, June 2026.
This study presents a novel framework that enhances the reliability of DNS traffic monitoring using a hybrid long short‐term memory‐deep neural network (LSMT‐DNN) architecture, enabling robust detection of adversarial DNS tunneling. The proposed framework leverages feature extraction from DNS traffic patterns, including domain request sequences, query ...
Ahmad Almadhor, Shtwai Alsubai, Monji Mohamed Zaidi, Natalia Kryvinska, Abdullah Al Hejaili, Sidra Abbas   +5 more
wiley   +1 more source

A new approach to malware detection [PDF]

, 2010
Malware is a type of malicious programs, and is one of the most common and serious types of attacks on the Internet. Obfuscating transformations have been widely applied by attackers to malware, which makes malware detection become a more challenging ...
Tang, Hong Ying
core  

N-gram Opcode Analysis for Android Malware Detection [PDF]

, 2016
Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets.
Lundgren, Martin,, Jasmina Marić, Bergström, Erik,, Kang, B., Yerima, Suleiman Y., Lundgren, Martin, Yerima, Suleiman, Maric, Jasmina, Bergström, Erik, Kang, BooJoong, Maric, Jasmina,, McLaughlin, K., Sezer, Sakir, Legg, Philip, McLaughlin, Kieran; id_orcid   +14 more
core   +1 more source

An agent-based model to simulate coordinated response to malware outbreak within an organisation

, 2012
Malware is a major threat to organisations. It affects business continuity and induces risks to organisations. Current anti-malware solutions are challenged to keep the risks at bay.
Fung, C.C., Pan, J.
core  

Metamorphic malware detection based on support vector machine classification of malware sub-signatures [PDF]

, 2016
Achieving accurate and efficient metamorphic malware detection remains a challenge. Metamorphic malware is able to mutate and alter its code structure in each infection that can circumvent signature matching detection. However, some vital functionalities
Khammas, Ban Mohammed, Marsono, Muhammad Nadzir, Mohd. Nor, Sulaiman, Ismail, Ismahani, Monemi, Alireza   +4 more
core  

Dynamic Extraction of Initial Behavior for Evasive Malware Detection

, 2023
Recently, malware has become more abundant and complex as the Internet has become more widely used in daily services. Achieving satisfactory accuracy in malware detection is a challenging task since malicious software exhibit non-relevant features when ...
Faitouri A. Aboaoja, Fuad A. Ghaleb, Murad A. Rassam, Abdullah Marish Ali, Fawaz Jaber Alsolami, Anazida Zainal   +5 more
core   +1 more source