4.6 Article

A vector convolutional deep autonomous learning classifier for detection of cyber attacks

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Optimization of vector convolutional deep neural network using binary real cumulative incarnation for detection of distributed denial of service attacks

N. G. Bhuvaneswari Amma et al.

Summary: This article introduces an optimized deep neural network structure for detecting DDoS attacks, using the CuI optimization technique. Experimental results show that this optimization method outperforms existing techniques and achieves significant performance improvement.

NEURAL COMPUTING & APPLICATIONS (2022)

Article Computer Science, Information Systems

A statistical class center based triangle area vector method for detection of denial of service attacks

N. G. Bhuvaneswari Amma et al.

Summary: The paper proposes a class center based triangle area vector (CCTAV) method for DoS attack detection, which reduces the complexity of feature extraction and enhances attack detection accuracy by computing the mean of target classes and extracting correlations between features. The proposed method is evaluated using tenfold cross validation and demonstrates significant results compared to existing attack detection methods.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2021)

Article Computer Science, Information Systems

A novel architecture for web-based attack detection using convolutional neural network

Adem Tekerek

Summary: Unprotected web applications are susceptible to hacker attacks, making web attack detection critical. This study proposes an anomaly-based web attack detection architecture using deep learning methods, which demonstrated successful outcomes through experimental results.

COMPUTERS & SECURITY (2021)

Article Computer Science, Information Systems

Autoencoder-based deep metric learning for network intrusion detection

Giuseppina Andresini et al.

Summary: In this study, a new intrusion detection method is introduced which leverages a deep metric learning methodology combining autoencoders and Triplet networks. Two separate autoencoders are trained on historical normal network flows and attacks, and a Triplet network is trained to learn the embedding of the feature vector representation of network flows. This methodology achieves better predictive accuracy in detecting new signs of malicious activities in network traffic compared to competitive intrusion detection architectures on benchmark datasets.

INFORMATION SCIENCES (2021)

Article Computer Science, Artificial Intelligence

Hybrid DeepGCL model for cyber-attacks detection on cyber-physical systems

Rasim Alguliyev et al.

Summary: The urgency of ensuring the security of cyber-physical systems lies in their correct functioning, which has a significant impact on various industrial sectors. This paper proposes a deep hybrid model based on three parallel neural architectures and experiments show its superiority over recent works using machine learning techniques.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Engineering, Civil

Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network

Abdul Rehman Javed et al.

Summary: The study proposes an anomaly detection method that combines a multi-stage attention mechanism with an LSTM-based CNN, as well as a weighted fine-tuned ensemble method for detecting anomalies in CAVs. The MSALSTM-CNN method effectively enhances anomaly detection rate, with a gain of up to 3.24% in F-score for detecting mixed anomaly types, showing promising performance compared to state-of-the-art methods.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

Article Computer Science, Information Systems

Analysis of intrusion detection in cyber attacks using DEEP learning neural networks

Parasuraman Kumar et al.

Summary: In the digital era, network security has become crucial, with machine learning techniques playing a key role in network intrusion detection. This study utilized supervised and unsupervised learning methods to enhance the accuracy and efficiency of intrusion detection systems. The results show that different types of attacks have varying detection rates.

PEER-TO-PEER NETWORKING AND APPLICATIONS (2021)

Article Computer Science, Hardware & Architecture

Distributed denial of service attacks and its defenses in IoT: a survey

Mikail Mohammed Salim et al.

JOURNAL OF SUPERCOMPUTING (2020)

Article Computer Science, Information Systems

A deep learning method with wrapper based feature extraction for wireless intrusion detection system

Sydney Mambwe Kasongo et al.

COMPUTERS & SECURITY (2020)

Article Telecommunications

In-vehicle network intrusion detection using deep convolutional neural network

Hyun Min Song et al.

VEHICULAR COMMUNICATIONS (2020)

Article Computer Science, Artificial Intelligence

DeNNeS: deep embedded neural network expert system for detecting cyber attacks

Samaneh Mahdavifar et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Computer Science, Theory & Methods

Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment

Bhuvaneswari N. G. Amma et al.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2020)

Article Computer Science, Information Systems

A Statistical Approach for Detection of Denial of Service Attacks in Computer Networks

N. G. Bhuvaneswari Amma et al.

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT (2020)

Article Computer Science, Artificial Intelligence

Deep Radial Intelligence with Cumulative Incarnation approach for detecting Denial of Service attacks

Bhuvaneswari N. G. Amma et al.

NEUROCOMPUTING (2019)

Article Computer Science, Hardware & Architecture

The hybrid technique for DDoS detection with supervised learning algorithms

Soodeh Hosseini et al.

COMPUTER NETWORKS (2019)

Article Computer Science, Hardware & Architecture

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

Fadi Salo et al.

COMPUTER NETWORKS (2019)

Article Computer Science, Information Systems

Novel Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation on Large-Scale Networks

Nour Moustafa et al.

IEEE TRANSACTIONS ON BIG DATA (2019)

Article Computer Science, Hardware & Architecture

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

Vajiheh Hajisalem et al.

COMPUTER NETWORKS (2018)

Article Automation & Control Systems

Detection of Cyber-attacks to indoor real time localization systems for autonomous robots

Angel Manuel Guerrero-Higueras et al.

ROBOTICS AND AUTONOMOUS SYSTEMS (2018)

Article Computer Science, Information Systems

Detection System of HTTP DDoS Attacks in a Cloud Environment Based on Information Theoretic Entropy and Random Forest

Mohamed Idhammad et al.

SECURITY AND COMMUNICATION NETWORKS (2018)

Article Computer Science, Artificial Intelligence

A Deep Learning Approach to Network Intrusion Detection

Nathan Shone et al.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2018)

Proceedings Paper Computer Science, Artificial Intelligence

DeepDefense: Identifying DDoS Attack via Deep Learning

Xiaoyong Yuan et al.

2017 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP) (2017)

Article Computer Science, Information Systems

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

Anna L. Buczak et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2016)

Article Computer Science, Artificial Intelligence

Detection of known and unknown DDoS attacks using Artificial Neural Networks

Alan Saied et al.

NEUROCOMPUTING (2016)

Article Computer Science, Information Systems

A Survey of Distance and Similarity Measures Used Within Network Intrusion Anomaly Detection

David J. Weller-Fahy et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2015)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Review Computer Science, Artificial Intelligence

Deep learning in neural networks: An overview

Juergen Schmidhuber

NEURAL NETWORKS (2015)

Article Computer Science, Artificial Intelligence

Analysis of network traffic features for anomaly detection

Felix Iglesias et al.

MACHINE LEARNING (2015)

Article Computer Science, Information Systems

A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks

Saman Taghavi Zargar et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2013)

Article Computer Science, Artificial Intelligence

Learn++: An incremental learning algorithm for supervised neural networks

R Polikar et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (2001)