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    Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

    Published 2022
    “…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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    Thesis
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    An improved method using fuzzy system based on hybrid boahs for phishing attack detection by Noor Syahirah, Nordin

    Published 2022
    “…Moreover, Butterfly Optimization Algorithm and Harmony Search Algorithm were combined as optimization method led to a new method named BOAHS. …”
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    A fast learning network with improved particle swarm optimization for intrusion detection system by Ali, Mohammed Hasan

    Published 2019
    “…In current days the intrusion detection systems (IDS) have several shortcomings such as high rates of false positive alerts, low detection rates of rare but dangerous attacks, and the need for a constant human intervention and tuning. …”
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  5. 5

    A hybridization of butterfly optimization algorithm and harmony search for fuzzy modelling in phishing attack detection by Noor Syahirah, Nordin, Mohd Arfian, Ismail

    Published 2023
    “…Therefore, an optimization method is needed to solve this issue, and one of the best methods to be applied is Butterfly Optimization Algorithm. …”
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  6. 6

    Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System by Ali, Mohammed Hasan, Mohamed Fadli, Zolkipli

    Published 2019
    “…As network attackers keep changing their methods of attack execution to evade the deployed intrusion-detection systems (IDS), machine learning (ML) algorithms have been introduced to boost the performance of the IDS. …”
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    Conference or Workshop Item
  7. 7

    An efficient attack detection for Intrusion Detection System (IDS) in internet of medical things smart environment with deep learning algorithm by Abdulkareem, Fatimah Saleem, Mohd Sani, Nor Fazlida

    Published 2023
    “…Therefore, an intrusion detection method for attacking and detecting anomalies in an IoT system must be enhanced. …”
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  8. 8

    Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

    Published 2022
    “…The deep learning approach plays a pertinent role in tracing new attacks in cybersecurity. Recently, researchers suggested a deep belief network (DBN) algorithm to construct and build a network intrusion detection system (NIDS) for detecting attacks that have not been seen before. …”
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    Cooperative multi agents for intelligent intrusion detection and prevention systems / Shahaboddin Shamshirband by Shamshirband, Shahaboddin

    Published 2014
    “…Owing to the distributed nature of modern attacks (e.g. denial-of-service), it is extremely challenging to detect such malicious behaviour using traditional intrusion detection systems. …”
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    Campus hybrid intrusion detection system using SNORT and C4.5 Algorithm by Slamet, ., Izzeldin, I. Mohd, Fahmi, Samsuri

    Published 2020
    “…As a result of implementing this system: misused detection module uses the signature of attacks to detection the known attacks; anomaly detection module can detect the unknown attacks; signature generation module extracts the signature of attacks that are detected by Anomaly Detection System module, and maps the signatures into snort rules.…”
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    Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin by Mohd Nordin, Ahmad Nasreen Aqmal

    Published 2024
    “…The "Remote to Local (R2L) Intrusion Detection System Using Decision Tree" project aims to address the escalating threat of network intrusions by developing an effective intrusion detection system. …”
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    Malware Classification and Detection using Variations of Machine Learning Algorithm Models by Andi Maslan, Andi Maslan, Abdul Hamid, Abdul Hamid

    Published 2025
    “…The purpose of the study is to detect, classify malware attacks using a variety of ML Algorithm models such as SVM, KNN and Neural Network and testing detection performance. …”
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    Article