Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

LNNLS-KH: A Feature Selection Method for Network Intrusion Detection

(2021) Xin Li et al.   Security and Communication Networks

Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning

(2021) Seungjin Lee et al.   PeerJ Computer Science

Deep Learning Techniques for Android Botnet Detection

(2021) Suleiman Y. Yerima et al.   Electronics

An efficient XGBoost–DNN-based classification model for network intrusion detection system

(2020) Preethi Devan et al.   NEURAL COMPUTING & APPLICATIONS

A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

(2020) Smitha Rajagopal et al.   Security and Communication Networks

Botnet Forensic Analysis Using Machine Learning

(2020) Anchit Bijalwan   Security and Communication Networks

Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches

(2020) Roberto Magán-Carrión et al.   Applied Sciences-Basel

Adaptive inertia weight Bat algorithm with Sugeno-Function fuzzy search

(2020) Hafiz Tayyab Rauf et al.   APPLIED SOFT COMPUTING

Deep learning-based classification model for botnet attack detection

(2020) Abdulghani Ali Ahmed et al.   Journal of Ambient Intelligence and Humanized Computing

A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks

(2020) Imtiaz Ullah et al.   Electronics

LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection

(2020) Robertas Damasevicius et al.   Electronics

M-AdaBoost-A Based Ensemble System for Network Intrusion Detection

(2020) Ying Zhou et al.   EXPERT SYSTEMS WITH APPLICATIONS

Detecting botnet by using particle swarm optimization algorithm based on voting system

(2020) Mehdi Asadi et al.   Future Generation Computer Systems-The International Journal of eScience

A network intrusion detection method based on semantic Re-encoding and deep learning

(2020) Zhendong Wu et al.   JOURNAL OF NETWORK AND COMPUTER APPLICATIONS

Detecting Internet of Things attacks using distributed deep learning

(2020) Gonzalo De La Torre Parra et al.   JOURNAL OF NETWORK AND COMPUTER APPLICATIONS

Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

(2020) Yan Naung Soe et al.   SENSORS

A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

(2020) Omar Almomani   Symmetry-Basel

Near real-time security system applied to SDN environments in IoT networks using convolutional neural network

(2020) Marcos V.O. de Assis et al.   COMPUTERS & ELECTRICAL ENGINEERING

Ensemble machine learning approaches for webshell detection in Internet of things environments

(2020) Binbin Yong et al.   Transactions on Emerging Telecommunications Technologies

Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles

(2020) Abdul Rehman Javed et al.   Transactions on Emerging Telecommunications Technologies

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

(2020) Zeeshan Ahmad et al.   Transactions on Emerging Telecommunications Technologies

Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning

(2020) Jiyeon Kim et al.   Applied Sciences-Basel

An efficient approach to detect IoT botnet attacks using machine learning

(2020) Zainab Alothman et al.   JOURNAL OF HIGH SPEED NETWORKS

Deep Learning-Based DDoS-Attack Detection for Cyber–Physical System Over 5G Network

(2020) Bilal Hussain et al.   IEEE Transactions on Industrial Informatics

Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection

(2020) MohammadNoor Injadat et al.   IEEE Transactions on Network and Service Management

Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection

(2019) Amaal Al Shorman et al.   Journal of Ambient Intelligence and Humanized Computing

Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic

(2019) Wisam Elmasry et al.   Computer Networks

An efficient reinforcement learning-based Botnet detection approach

(2019) Mohammad Alauthman et al.   JOURNAL OF NETWORK AND COMPUTER APPLICATIONS

Mobile network intrusion detection for IoT system based on transfer learning algorithm

(2018) Lianbing Deng et al.   Cluster Computing-The Journal of Networks Software Tools and Applications

A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection

(2018) Vajiheh Hajisalem et al.   Computer Networks

Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection

(2018) Fadi Salo et al.   Computer Networks

Firefly algorithm based feature selection for network intrusion detection

(2018) Selvakumar B et al.   COMPUTERS & SECURITY

Self-adaptive projection algorithms for solving the split equality problems

(2017) Qiao-Li Dong et al.   Fixed Point Theory

Dynamic Reconfiguration in 5G Mobile Networks to Proactively Detect and Mitigate Botnets

(2017) Manuel Gil Perez et al.   IEEE INTERNET COMPUTING

Taxonomy and Survey of Collaborative Intrusion Detection

(2015) Emmanouil Vasilomanolakis et al.   ACM COMPUTING SURVEYS

Botnet in DDoS Attacks: Trends and Challenges

(2015) Nazrul Hoque et al.   IEEE Communications Surveys and Tutorials

Low-Sidelobe Antenna Beamforming Via Stochastic Optimization

(2014) Steven I. Krich et al.   IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION

Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm

(2014) Kuan-Cheng Lin et al.   Journal of Applied Mathematics

Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

(2013) Sannasi Ganapathy et al.   EURASIP Journal on Wireless Communications and Networking

A hybrid network intrusion detection system using simplified swarm optimization (SSO)

(2012) Yuk Ying Chung et al.   APPLIED SOFT COMPUTING