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

Showing 231240 of 800 papers

TitleStatusHype
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?0
Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures0
Data Analysis of Wireless Networks Using Classification Techniques0
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security0
Blockchain Large Language Models0
Data Mining model in the discovery of trends and patterns of intruder attacks on the data network as a public-sector innovation0
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review0
Dealing with Imbalanced Classes in Bot-IoT Dataset0
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach0
A New Clustering Approach for Anomaly Intrusion Detection0
Show:102550
← PrevPage 24 of 80Next →

Benchmark Results

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