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 121–130 of 800 papers

TitleStatusHype
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm—0
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach—0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security—0
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
Adversarial Machine Learning in Network Intrusion Detection Systems—0
A Content-Based Deep Intrusion Detection System—0
A cognitive based Intrusion detection system—0
Are Trees Really Green? A Detection Approach of IoT Malware Attacks—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