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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning
2023
Computers Materials & Continua
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At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. ...
With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. ...
[17] presented a hybrid DL technique termed 'URLdeepDetect' to analyze the time-of-click for URLs and a classifier to detect malicious URLs. ...
doi:10.32604/cmc.2023.031371
fatcat:tsla4376jze73lsk7rlbgxay64
Trustworthy Intrusion Detection in E-Healthcare Systems
2021
Frontiers in Public Health
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This paper proposes an approach for effective intrusion detection in the e-healthcare environment to maintain PHR in a safe IoT-net using an adaptive neuro-fuzzy inference system (ANFIS). ...
In the proposed security model, the experiments present a security tool that helps to detect malicious network traffic. ...
URLdeepDetect: a deep learning approach for detecting malicious urls using semantic vector models. ...
doi:10.3389/fpubh.2021.788347
pmid:34926397
pmcid:PMC8678532
fatcat:4iso64ixvndtniok2igg5xapeq
Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention
2024
Applied Sciences
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This paper aims to address the increasingly severe security threats in financial systems by proposing a novel financial attack detection model, Finsformer. ...
Comparative experiments with traditional deep learning models such as RNN, LSTM, Transformer, and BERT have demonstrated that Finsformer excels in key metrics such as precision, recall, and accuracy, achieving ...
Afzal Sara et al. proposed a hybrid deep learning method named URLdeepDetect for detecting malicious URLs, ultimately achieving 98.3% accuracy [14] . ...
doi:10.3390/app14010460
fatcat:ahmwrqwoyzddbeuzjhbcnuysje