Machine learning algorithm for malware detection: Taxonomy, current challenges, and future directions

NZ Gorment, A Selamat, LK Cheng, O Krejcar - IEEE Access, 2023 - ieeexplore.ieee.org
Malware has emerged as a cyber security threat that continuously changes to target
computer systems, smart devices, and extensive networks with the development of …

[PDF][PDF] Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

VDH De Carvalho, A Costa - Socioecon. Anal, 2023 - periodicos.ufpe.br
Text mining and related analytics emerge as a technological approach to support human
activities in extracting useful knowledge through texts in several formats. From a managerial …

Enhancing file entropy analysis to improve machine learning detection rate of ransomware

CM Hsu, CC Yang, HH Cheng, PE Setiasabda… - IEEE …, 2021 - ieeexplore.ieee.org
Cybersecurity is the biggest threat in the world. More and more people are used to storing
personal data on a computer and transmitting it through the Internet. Cybersecurity will be an …

Distinguishing malicious programs based on visualization and hybrid learning algorithms

S Kumar, B Janet - Computer Networks, 2021 - Elsevier
Modern malware threats demand a robust and scalable detection system. This paper
presents a novel proactive monitoring and analysis architecture called malware threat …

Analysis of machine learning models for malware detection

Rahul, P Kedia, S Sarangi, Monika - Journal of Discrete …, 2020 - Taylor & Francis
With the increasing importance of the internet and computers in the modern world, the task
of its maintenance and protection from the threats posed by malicious software has become …

Caught-in-Translation (CiT): Detecting Cross-level Inconsistency Attacks in Network Functions Virtualization (NFV)

S Lakshmanan, M Zhang, S Majumdar… - … on Dependable and …, 2023 - ieeexplore.ieee.org
As one of the main technology pillars of 5G networks, Network Functions Virtualization (NFV)
enables agile and cost-effective deployment of network services. However, the multi-level …

MDEA: Malware detection with evolutionary adversarial learning

X Wang, R Miikkulainen - 2020 IEEE Congress on Evolutionary …, 2020 - ieeexplore.ieee.org
Malware detection have used machine learning to detect malware in programs. These
applications take in raw or processed binary data to neural network models to classify as …

Ransomware detection based on network behavior using machine learning and hidden markov model with gaussian emission

A Srivastava, N Kumar, A Handa… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Ransomware poses a deadly threat to any device system and organization. Several studies
and techniques are proposed in response to a dire need for a solution to detect ransomware …

Exploit internal structural information for IoT malware detection based on hierarchical transformer model

X Hu, R Sun, K Xu, Y Zhang… - 2020 IEEE 19th …, 2020 - ieeexplore.ieee.org
The number of IoT devices continues to increase, but the security of IoT devices cannot be
guaranteed. Many IoT devices are infected with malware, forming huge botnets, which could …

[PDF][PDF] Method and algorithms of visual audit of program interaction.

MV Buinevich, KE Izrailov, IV Kotenko… - J. Internet Serv. Inf. Secur., 2021 - jisis.org
Modern software products consist of a lot of executable files. Simultaneously, there are
complex data flows between them. As a result, the task of auditing such data interactions of …