Results 61 to 70 of about 306 (121)

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, Thales Guimaraes Marques, Wendley Souza da Silva, Joaquim Celestino Junior   +3 more
wiley   +1 more source

Securing the Unseen: A Comprehensive Exploration Review of AI‐Powered Models for Zero‐Day Attack Detection

Expert Systems, Volume 43, Issue 3, March 2026.
ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN ...
Abdullah Al Siam, Nuruzzaman Faruqui, Akm Azad, Mohammad Ali Moni   +3 more
wiley   +1 more source

A Secure Contained Testbed for Analyzing IoT Botnets [PDF]

, 2019
Many security issues have come to the fore with the increasingly widespread adoption of Internet-of-Things (IoT) devices. The Mirai attack on Dyn DNS service, in which vulnerable IoT devices such as IP cameras, DVRs and routers were infected and used to propagate large-scale DDoS attacks, is one of the more prominent recent examples.
Ayush Kumar 0001, Teng Joon Lim
openaire   +2 more sources

A Two‐Phase Detection Method Based on Ensemble Feature Fusion for Detecting Distributed Denial of Service (DDoS) Attacks in Cloud Computing Using Deep Learning Algorithm

Engineering Reports, Volume 8, Issue 2, February 2026.
This study introduces a two‐phase method for detecting DDoS attacks in cloud environments using ensemble feature fusion and a hybrid CNN‐LSTM model. By combining meta‐heuristic feature selection with deep learning, the approach achieves over 99% accuracy on benchmark datasets, reducing false positives and improving cybersecurity resilience.
Hind Saad Hussein, Farhad Navabifar, Fariba Majidi, Hayder Kadhim Hammood   +3 more
wiley   +1 more source

Exploiting Vision Transformer and Ensemble Learning for Advanced Malware Classification

Engineering Reports, Volume 8, Issue 1, January 2026.
Overview of the proposed RF–ViT ensemble for multi‐class malware classification. Textual (BoW/byte‐frequency) and visual representations are combined via a product rule, achieving improved accuracy and robustness over individual models. ABSTRACT Malware remains a significant concern for modern digital systems, increasing the need for reliable and ...
Fadi Makarem, Rida El Chall, Abed Ellatif Samhat, Khouloud Samrouth, Nader Bakir   +4 more
wiley   +1 more source

A novel approach of botnet detection using hybrid deep learning for enhancing security in IoT networks

Alexandria Engineering Journal
In an era dominated by the Internet of Things (IoT), protecting interconnected devices from botnets has become essential. This study introduces an innovative hybrid deep learning model that synergizes LSTM Auto Encoders and Multilayer Perceptrons in ...
Shamshair Ali, Rubina Ghazal, Nauman Qadeer, Oumaima Saidani, Fatimah Alhayan, Anum Masood, Rabia Saleem, Muhammad Attique Khan, Deepak Gupta   +8 more
doaj   +1 more source

RRF‐IPS: A Real‐Time Reputation‐Based Intrusion Prevention System

Engineering Reports, Volume 8, Issue 1, January 2026.
RRF‐IPS: A Real‐Time Reputation‐Based Intrusion Prevention System. ABSTRACT With the rapid development of technologies such as cloud computing and the Internet of Things, organizations face the thorny reality that network attacks are becoming increasingly diverse, covert, and intelligent.
Zhenghao Qian, Fengzheng Liu, Mingdong He, Bo Li, Xuewu Li, Chuangye Zhao, Gehua Fu, Yifan Hu   +7 more
wiley   +1 more source

Latent Semantic Analysis and Graph Theory for Alert Correlation: A Proposed Approach for IoT Botnet Detection

IEEE Open Journal of the Communications Society
In recent times, the proliferation of Internet of Things (IoT) technology has brought a significant shift in the digital transformation of various industries. The enabling technologies have accelerated this adoption.
Moemedi Lefoane, Ibrahim Ghafir, Sohag Kabir, Irfan-Ullah Awan, Khalil El Hindi, Anand Mahendran   +5 more
doaj   +1 more source

Ensemble classification to predict botnet and its impact on IoT networks

Measurement: Sensors
The IoT (Internet of Things) has been rapidly growing to make a stronger influence on huge industrial systems. Since cybercriminals have made IoT a target for harmful operations such as botnets, an attack on the end nodes is now a possibility. Protecting
G. Chandana Swathi, G. Kishor Kumar, A.P. Siva Kumar   +2 more
doaj   +1 more source

Computationally Efficient Deep Federated Learning with Optimized Feature Selection for IoT Botnet Attack Detection

Intelligent Systems with Applications
Internet of Things (IoT) is a technology that has revolutionized various fields, offering numerous benefits, such as remote patient monitoring, enhanced energy efficiency, and automation of routine tasks in homes.
Lambert Kofi Gyan Danquah, Stanley Yaw Appiah, Victoria Adzovi Mantey, Iddrisu Danlard, Emmanuel Kofi Akowuah   +4 more
doaj   +1 more source