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Unknown Security Attack Detection Using Shallow and Deep ANN Classifiers

https://doi.org/10.3390/electronics9122006

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Comparative Results with Unsupervised Techniques in Cyber Attack Novelty Detection

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Smart Intrusion Detection with Expert Systems

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A Heuristic Intrusion Detection System for Internet-of-Things (IoT)

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Cyber Security Risks in MENA Region: Threats, Challenges and Countermeasures

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Proposed Approach for Targeted Attacks Detection

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An Approach for Host-Based Intrusion Detection System Design Using Convolutional Neural Network

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Predicting New Attacks for Information Security

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A comprehensive survey on network anomaly detection

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A data-driven metric learning-based scheme for unsupervised network anomaly detection

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Toward developing a systematic approach to generate benchmark datasets for intrusion detection

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A survey of network-based intrusion detection data sets

inferredcites →https://doi.org/10.1016/j.cose.2020.101984openalex-snapshot

SwiftIDS: Real-time intrusion detection system based on LightGBM and parallel intrusion detection mechanism

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Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset

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A survey of network anomaly detection techniques

inferredcites →https://doi.org/10.1016/j.jnca.2017.02.009openalex-snapshot

A survey of intrusion detection in Internet of Things

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Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling

inferredcites →https://doi.org/10.1016/j.jnca.2018.12.006openalex-snapshot

A holistic review of Network Anomaly Detection Systems: A comprehensive survey

inferredcites →https://doi.org/10.1016/j.jocs.2017.03.006openalex-snapshot

Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model

inferredcites →https://doi.org/10.1016/j.knosys.2019.105124openalex-snapshot

Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues

inferredcites →https://doi.org/10.1109/access.2018.2841987openalex-snapshot

Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection

inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-snapshot

A detailed analysis of the KDD CUP 99 data set

inferredcites →https://doi.org/10.1109/comst.2015.2494502openalex-snapshot

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

inferredcites →https://doi.org/10.1109/comst.2018.2854724openalex-snapshot

From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods

inferredcites →https://doi.org/10.1109/iciteed.2016.7863293openalex-snapshot

A comparison between shallow and deep architecture classifiers on small dataset

inferredcites →https://doi.org/10.1109/ictcs.2017.29openalex-snapshot

Experimental Evaluation of a Multi-layer Feed-Forward Artificial Neural Network Classifier for Network Intrusion Detection System

inferredcites →https://doi.org/10.1109/ictcs.2019.8923067openalex-snapshot

Optimized Multi - Layer Hierarchical Network Intrusion Detection System with Genetic Algorithms

inferredcites →https://doi.org/10.1109/milcis.2015.7348942openalex-snapshot

UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)

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The UCI KDD archive of large data sets for data mining research and experimentation

inferredcites →https://doi.org/10.1166/asl.2016.7991openalex-snapshot

Detection of New Attacks on Ubiquitous Services in Cloud Computing and Against Measure

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A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

inferredcites →https://doi.org/10.1186/s42400-019-0038-7openalex-snapshot

Survey of intrusion detection systems: techniques, datasets and challenges

inferredcites →https://doi.org/10.1201/b16390-16openalex-snapshot

New Unknown Attack Detection with the Neural Network–Based IDS

inferredcites →https://doi.org/10.2478/v10065-010-0035-7openalex-snapshot

Analysis of neural networks usage for detection of a new attack in IDS

inferredcites →https://doi.org/10.3390/app9204396openalex-snapshot

Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

inferredcites →https://doi.org/10.3390/computers8030059openalex-snapshot

RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection

inferredcites →https://doi.org/10.3390/electronics9010173openalex-snapshot

Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

inferredcites →https://doi.org/10.3390/electronics9040692openalex-snapshot

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

inferredcites →https://doi.org/10.3390/electronics9060916openalex-snapshot

CNN-Based Network Intrusion Detection against Denial-of-Service Attacks

inferredcites →https://doi.org/10.3390/electronics9071151openalex-snapshot

Packet Preprocessing in CNN-Based Network Intrusion Detection System

inferredcites →https://doi.org/10.3390/electronics9091533openalex-snapshot

DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

inferredcites →https://doi.org/10.48550/arxiv.1009.2406openalex-snapshot

Adaptation of the neural network-based IDS to new attacks detection

inferredcites →https://doi.org/10.48550/arxiv.1701.02145openalex-snapshot

Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey

inferredcites →https://doi.org/10.5120/13209-0587openalex-snapshot

Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection Methods

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Supervised Machine Learning and Detection of Unknown Attacks: An Empirical Evaluation

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Machine Learning Approaches for Intrusion Detection in IoT Systems: Taxonomy and Challenges

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Detecting Unknown Vulnerabilities in Smart Contracts with Binary Classification Model Using Machine Learning

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Zero-day attack detection: a systematic literature review

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Empirical evaluation of ensemble learning and hybrid CNN-LSTM for IoT threat detection on heterogeneous datasets

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Analyzing shallow and deep classifiers in detecting unseen attacks on internet of things network

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HERO: From High-dimensional network traffic to zERO-Day attack detection

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NERO: NEural algorithmic reasoning for zeRO-day attack detection in the IoT: A hybrid approach

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Artificial neural network training using a multi selection artificial algae algorithm

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Detecting unknown vulnerabilities in smart contracts using opcode sequences

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Open Set Recognition With Dissimilarity Weight for Unknown Attack Detection

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A Computer Network Security Intrusion Detection Algorithm Based on Deep Learning

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Network Attack Detection in IoT using Artificial Intelligence

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DeepInsight-Convolutional Neural Network for Intrusion Detection Systems

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A Review and Analysis of the Bot-IoT Dataset

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COMPARATIVE ANALYSIS OF RANDOM FOREST AND ADABOOST LEARNING MODELS FOR THE CLASSIFICATION OF ATTACKS IN INTERNET OF THINGS

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Impact of Network Topology on Machine Learning-Based DDoS and Anomaly Detection in Software-Defined Networks

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