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Intrusion Detection

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하위 태스크: 1
평가표: 6
보존된 평가 기록: 7

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AFAgarap/gru-svm

공식 코드

A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

https://github.com/AbertayMachineLearningGroup/network-threats-taxonomy

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A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems

https://github.com/Abrar2652/Network-Intrusion-Detection

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CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion Detection

https://github.com/ArielCyber/When_a_RF_Beats_a_CNN_and_GRU

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When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification

https://github.com/BNN-UPC/GNN-NIDS

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Unveiling the potential of Graph Neural Networks for robust Intrusion Detection

https://github.com/CN-TU/adversarial-recurrent-ids

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Explainability and Adversarial Robustness for RNNs

https://github.com/CN-TU/ids-backdoor

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Walling up Backdoors in Intrusion Detection Systems

https://github.com/CN-TU/machine-learning-in-ebpf

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A flow-based IDS using Machine Learning in eBPF

https://github.com/CN-TU/tpsdos-experiments

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SDOoop: Capturing Periodical Patterns and Out-of-phase Anomalies in Streaming Data Analysis

https://github.com/CrySyS/can-log-infector

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Detecting message modification attacks on the CAN bus with Temporal Convolutional Networks

https://github.com/GuansongPang/deviation-network

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Deep Anomaly Detection with Deviation Networks

https://github.com/ISorokos/SafeML

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SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

https://github.com/JRC-ISIA/paper-2024-eurocast-honeypot

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A Generative Model Based Honeypot for Industrial OPC UA Communication

https://github.com/MateoLopez00/Imbalanced-Learning-Empirical-Evaluation

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Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets

https://github.com/RCSL-TCD/SecCAN

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SecCAN: An Extended CAN Controller with Embedded Intrusion Detection

https://github.com/RussellTNY/PPO-optimized-Tab-Transformer-for-NIDS-on-TON_IoT

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A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT Systems

https://github.com/SNL-UCSB/netFound

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netFound: Foundation Model for Network Security

https://github.com/Stream-AD/AnoGraph

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Sketch-Based Anomaly Detection in Streaming Graphs

https://github.com/Stream-AD/MStream

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MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams

https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning

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Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

https://github.com/abdelmageed95/Synthesis-of-Adversarial-DDos-Attacks-Using-Tabular-Generative-Adversarial-Networks

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Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks

https://github.com/abhijeet1990/dempstershafer_fusion_gui

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Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

https://github.com/abhilash12iec002/intrusion_detection

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A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks

https://github.com/ambroggi/pruning-project-for-deep-neural-networks

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Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge

https://github.com/anandsagarthumati9848/HIDS

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Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion Detection

https://github.com/andreacorsini1/cyberood

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Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?

https://github.com/andreaventuri01/Structural_attacks_GNN_NIDS

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Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks

https://github.com/andrewli77/DISC

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Interpretable Sequence Classification via Discrete Optimization

https://github.com/antonpuz/DeROL

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Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems

https://github.com/arielcyber/osf-eimtc

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Open-Source Framework for Encrypted Internet and Malicious Traffic Classification

https://github.com/asapegin/pyspark-kmetamodes

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K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analytics

https://github.com/billmj/graphml-controller-area-network-attack-detection

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Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

https://github.com/bingleilou/PolyLUT-Add

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PolyLUT-Add: FPGA-based LUT Inference with Wide Inputs

https://github.com/bit-ml/anoshift

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AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection

https://github.com/c2dc/ab-trap

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Bridging the gap to real-world for network intrusion detection systems with data-centric approach

https://github.com/comsys/ml-iids-generalizability

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A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion Detection

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