[PDF][PDF] Review of cyber attack detection: Honeypot system

MR Amal, P Venkadesh - Webology, 2022 - webology.org
The number of connected devices in the network is growing day by day, and as the number
of linked devices grows, so will the number of cyberattacks. All devices connected to the …

Malware detection using honeypot and machine learning

IMM Matin, B Rahardjo - … conference on cyber and IT service …, 2019 - ieeexplore.ieee.org
Malware is one of the threats to information security that continues to increase. In 2014
nearly six million new malware was recorded. The highest number of malware is in Trojan …

A smart agent design for cyber security based on honeypot and machine learning

N El Kamel, M Eddabbah, Y Lmoumen… - Security and …, 2020 - Wiley Online Library
The development of Internet and social media contributes to multiplying the data produced
on the Internet and the connected nodes, but the default installation and the configuration of …

Improved deep learning model for static PE files malware detection and classification

SS Lad, AC Adamuthe - International Journal of Computer …, 2022 - search.proquest.com
Static analysis and detection of malware is a crucial phase for handling security threats.
Most researchers stated that the problem with the static analysis is an imbalance in the …

New framework for adaptive and agile honeypots

S Dowling, M Schukat, E Barrett - Etri Journal, 2020 - Wiley Online Library
This paper proposes a new framework for the development and deployment of honeypots for
evolving malware threats. As new technological concepts appear and evolve, attack …

Improving adaptive honeypot functionality with efficient reinforcement learning parameters for automated malware

S Dowling, M Schukat, E Barrett - Journal of Cyber Security …, 2018 - Taylor & Francis
This paper presents an intelligent honeypot that uses reinforcement learning to proactively
engage with and learn from attacker interactions. It adapts its behaviour for automated …

SeLINA: A self-learning insightful network analyzer

D Apiletti, E Baralis, T Cerquitelli… - … on Network and …, 2016 - ieeexplore.ieee.org
Understanding the behavior of a network from a large scale traffic dataset is a challenging
problem. Big data frameworks offer scalable algorithms to extract information from raw data …

Melting the snow: Using active DNS measurements to detect snowshoe spam domains

O van der Toorn, R van Rijswijk-Deij… - NOMS 2018-2018 …, 2018 - ieeexplore.ieee.org
Snowshoe spam is a type of spam that is notoriously hard to detect. Anti-abuse vendors
estimate that 15% of spam can be classified as snowshoe spam. Differently from regular …

GroupTracer: Automatic attacker TTP profile extraction and group cluster in Internet of things

Y Wu, C Huang, X Zhang, H Zhou - security and …, 2020 - Wiley Online Library
As Advanced Persistent Threat (APT) becomes increasingly frequent around the world,
security experts are starting to look at how to observe, predict, and mitigate the damage from …

An outlier ensemble for unsupervised anomaly detection in honeypots data

L Boukela, G Zhang, S Bouzefrane… - Intelligent Data …, 2020 - content.iospress.com
Nowadays, computers, as well as smart devices, are connected through communication
networks making them more vulnerable to attacks. Honeypots are proposed as deception …