SOTAVerified

Intrusion Detection

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 800 papers

TitleStatusHype
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data—0
Adversarial Machine Learning in Network Intrusion Detection Systems—0
A Content-Based Deep Intrusion Detection System—0
Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss—0
CyberRAG: An agentic RAG cyber attack classification and reporting tool—0
A cognitive based Intrusion detection system—0
Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures—0
Data Analysis of Wireless Networks Using Classification Techniques—0
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs—0
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?—0
Data Mining model in the discovery of trends and patterns of intruder attacks on the data network as a public-sector innovation—0
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review—0
Dealing with Imbalanced Classes in Bot-IoT Dataset—0
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach—0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework—0
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security—0
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture—0
Blockchain Large Language Models—0
A New Clustering Approach for Anomaly Intrusion Detection—0
Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure—0
Deep Learning-based Intrusion Detection Systems: A Survey—0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified