A Compact Network Intrusion Classifier Using Fuzzy Logic

Patel, Kanubhai K.

doi:10.1007/978-3-030-66763-4_13

Cited by 2 publications

(1 citation statement)

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“…Therefore, securing digital information is very important for numerous applications. Traditional approaches are based on statistical or rule‐based methods 7 to detect network anomalies and do not effectively handle complex and dynamic network traffic. In this case, network intrusion detection systems have played a paramount role in protecting computer networks, detecting and preventing unauthorized access, malicious attacks, data breaches, and potential cyber threats within computer networks in real time 4,5,8,9 .…”

Section: Introductionmentioning

confidence: 99%

An LSTM‐based novel near‐real‐time multiclass network intrusion detection system for complex cloud environments

Vibhute,

Khan,

Kanade

et al. 2024

Concurrency and Computation

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SummaryThe Internet is connected with everyone for sharing and monitoring digital information. However, securing network resources from malicious activities is critical for several applications. Numerous studies have recently used deep learning‐based models in detecting intrusions and received relatively robust recognition outcomes. Nevertheless, most investigations have operated old datasets, so they could not detect the most delinquent attack information. Therefore, the current research proposes the long short‐term memory (LSTM)‐based near real‐time multiclass network intrusion detection system (NIDS) utilizing complex cloud CSE‐CICIDSS2018 datasets to secure and detect the network anomalous. The proposed strategy utilizes a random forest algorithm for dimensionality reduction and feature selection. In addition, the selected best suitable features were used in a deep learning‐based LSTM model developed for detecting network intrusions. The experimental outcomes reveal that the presented LSTM model obtained 99.66% testing accuracy with 0.12% loss. Thus, the suggested approach can detect network intrusions with the highest precision and lowest rate over the earlier designs.

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Section: Introductionmentioning

confidence: 99%