Results 31 to 40 of about 36,905 (167)

MalHyStack: A hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis

Intelligent Systems with Applications, 2023
Since the advent of malware, it has reached a toll in this world that exchanges billions of data daily. Millions of people are victims of it, and the numbers are not decreasing as the year goes by.
Kowshik Sankar Roy, Tanim Ahmed, Pritom Biswas Udas, Md. Ebtidaul Karim, Sourav Majumdar   +4 more
doaj   +1 more source

MalGEA: A malware analysis framework via matrix factorization based node embedding and graph external attention

Array
As one of the major threats in cybersecurity, malware has been growing continuously and steadily. In recent years, researchers have proposed a number of graph representation learning based malware detection methods by leveraging the intrinsic topological
Ruisheng Li, Qilong Zhang, Huimin Shen
doaj   +1 more source

OntoLogX: Ontology‐Guided Knowledge Graph Extraction From Cybersecurity Logs With Large Language Models

Advanced Intelligent Systems, EarlyView.
OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti, Idilio Drago, Anisa Rula, Devis Bianchini, Federico Cerutti   +4 more
wiley   +1 more source

Harnessing GPT-2 for Feature Extraction in Malware Detection: A Novel Approach to Cybersecurity

Land Forces Academy Review
Abstract In the rapidly advancing digital age, the surge in malware complexity presents a formidable challenge to cybersecurity efforts, rendering traditional signature-based detection methods increasingly obsolete. These methods struggle to keep pace with the swift evolution of malware, particularly with the emergence of polymorphic and
Mahmoud Basharat, Marwan Omar
openaire   +1 more source

AI‐Assisted IoT‐Enabled ECG Monitoring: Integrating Foundational and Generative AI Tools for Sustainable Smart Healthcare—Recent Trends

AI &Innovation, EarlyView.
ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury, Kandarpa Kumar Sarma, Lachit Dutta   +2 more
wiley   +1 more source

A3CM: Automatic Capability Annotation for Android Malware

IEEE Access, 2019
Android malware poses serious security and privacy threats to the mobile users. Traditional malware detection and family classification technologies are becoming less effective due to the rapid evolution of the malware landscape, with the emerging of so ...
Junyang Qiu, Jun Zhang, Wei Luo, Lei Pan, Surya Nepal, Yu Wang, Yang Xiang   +6 more
doaj   +1 more source

Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

, 2017
In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA,
Chen, Aokun, Gregio, Andre, He, Pan, Li, Xiaolin, Oliveira, Daniela, Sun, Ruimin, Yuan, Xiaoyong, Zhu, Qile   +7 more
core   +1 more source

Sustainability Challenges to the Steel Industry in a Developing Country: Sanctions and Security Issues at the Forefront

Business Strategy and the Environment, EarlyView.
ABSTRACT This article contributes to sustainability research by investigating the complex, geopolitically induced challenges faced by industrial supply chains under international sanctions. Using Iran's steel industry as a case, it examines sustainability barriers through the lens of stakeholder theory. A mixed methods approach was employed.
Seyed Hamed Moosavirad, Mohammad Moshtari, Ali Esfahbodi   +2 more
wiley   +1 more source

Data-Driven Malware Detection for 6G Networks: A Survey From the Perspective of Continuous Learning and Explainability via Visualisation

IEEE Open Journal of Vehicular Technology, 2023
5G is inherently prone to security vulnerabilities. We witness that many today's networks contain 5G security flaws due to their reliance on the existing 4G network core.
Dilara T. Uysal, Paul D. Yoo, Kamal Taha
doaj   +1 more source

A Review of Overcurrent Protection in Smart Grids Under Cyber‐Physical Threats With a Cyber‐Physical Evaluation Framework

Energy Science &Engineering, EarlyView.
By manipulating current and voltage measurements, an assailant can induce unwanted relay action while attempting to avoid detection. Detecting advanced cyber intrusions in power protection environments requires specialised data analysis and anomaly detection methods.
Feras Alasali, Anas AlMajali, Naser Naily, Hassen Loukil, Salah AbuGhalyon, William Holderbaum, Abdelaziz Salah Saidi   +6 more
wiley   +1 more source