Intrusion Detection
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-Hadoop-distributed-file-system-log
https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-unstructured-Hadoop-distributed-file-system-logs
https://github.com/robeespi/Weakly-Supervised-Malware-Detection
https://github.com/rylandtikes/Lightweight-IDS
https://github.com/skyguy19/tdamalwaredetection
Reliable Malware Analysis and Detection using Topology Data Analysis
https://github.com/threatrace-detector/threatrace
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning
https://github.com/xilinx/logicnets
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications
https://github.com/xuhongzuo/DeepOD
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
https://github.com/ymirsky/Kitsune-py
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
https://github.com/yuweisunn/segmented-FL
https://github.com/yzhao062/yzhao062
TOD: GPU-accelerated Outlier Detection via Tensor Operations
https://gitlab.ilabt.imec.be/mverkerk/multi-stage-hierarchical-ids
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.