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 231–240 of 800 papers

TitleStatusHype
Anomaly detection optimization using big data and deep learning to reduce false-positive—0
Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures—0
Data Analysis of Wireless Networks Using Classification Techniques—0
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—0
Anomaly Detection via Minimum Likelihood Generative Adversarial 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
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—0
Show:102550
← PrevPage 24 of 80Next →
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