Results 51 to 60 of about 14,194 (229)
Machine Learning DDoS Detection for Consumer Internet of Things Devices
, 2018 An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks. Botnets such as Mirai have used insecure consumer IoT devices to
Apthorpe, Noah +2 more
core +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL‐KDD benchmark dataset, this paper presents an improved anomaly detection system that combines an optimised sequential neural network ...
Seong‐O Shim +4 more
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 +3 more
wiley +1 more source
Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data
, 2017 Many systems are partially stochastic in nature. We have derived data driven approaches for extracting stochastic state machines (Markov models) directly from observed data.
Bao Ly Van +18 more
core +1 more source
An Overview of Deep Learning Techniques for Big Data IoT Applications
International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur +2 more
wiley +1 more source
Energy Science &Engineering, Volume 14, Issue 3, Page 1431-1455, March 2026.Overview of the proposed work. ABSTRACT Identifying cyber threats maintains the security and operational stability of smart grid systems because they experience escalating attacks that endanger both operating data reliability and system stability and electricity grid performance.
Priya R. Karpaga +3 more
wiley +1 more source
Detecting Botnets Through Log Correlation [PDF]
SSRN Electronic Journal, 2006 4 pages, 7 figures, Workshop on Monitoring, Attack Detection and Mitigation (MonAM2006)
Al-Hammadi, Yousof, Aickelin, Uwe
openaire +3 more sources
Edge‐Oriented DoS/DDoS Intrusion Detection and Supervision Platform
SECURITY AND PRIVACY, Volume 9, Issue 2, March/April 2026.ABSTRACT This work presents an Edge Node‐Oriented DoS/DDoS Intrusion Detection and Monitoring Platform, a novel anomaly detection system based on temporal analysis with machine learning (ML) and deep learning (DL) algorithms, specifically designed to operate on edge servers with limited resources.
Geraldo Eufrazio Martins Júnior +3 more
wiley +1 more source
Botnet Detection Using On-line Clustering with Pursuit Reinforcement Competitive Learning (PRCL)
Emitter: International Journal of Engineering Technology, 2018 Botnet is a malicious software that often occurs at this time, and can perform malicious activities, such as DDoS, spamming, phishing, keylogging, clickfraud, steal personal information and important data.
Yesta Medya Mahardhika +2 more
doaj +1 more source
Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
Applied Sciences, 2021 Internet of Things (IoT) is promising technology that brings tremendous benefits if used optimally. At the same time, it has resulted in an increase in cybersecurity risks due to the lack of security for IoT devices.
Majda Wazzan +4 more
doaj +1 more source