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  1. 1
    Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system

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

    Published 2022
    “…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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  2. 2
    Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak

    Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

    Published 2019
    “…Thus, this study proposes the development of algorithms to detect and classify jamming attacks using a set of parameters on the physical layer and MAC in MANET. …”
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  3. 3
    DeepIoT.IDS: Hybrid deep learning for enhancing IoT network intrusion detection

    DeepIoT.IDS: Hybrid deep learning for enhancing IoT network intrusion detection by A. Mostafa, Salama, Al-Azzawi, Ziadoon Kamil Maseer, Bahaman, Nazrulazhar, Yusof, Robiah, Musa, Omar, Al-rimy, Bander Ali Saleh

    Published 2021
    “…Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. …”
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  4. 4
    Algorithm enhancement for host-based intrusion detection system using discriminant analysis

    Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

    Published 2004
    “…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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  5. 5
    A study on advanced statistical analysis for network anomaly detection

    A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

    Published 2005
    “…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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    Monograph
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  6. 6
    Hybrid Classification Algorithm For Denial Of Service Attack Detection Using Rough Set Theory And Artificial Immune

    Hybrid Classification Algorithm For Denial Of Service Attack Detection Using Rough Set Theory And Artificial Immune by Fatin Norsyafawati, Mohd Sabri

    Published 2024
    thesis::master thesis
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  7. 7
    Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm

    Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm by Mohamed, M. E, Samir, B. B., Azween, Abdullah

    Published 2010
    “…In this work, we integrate artificial immune algorithm with non-linear classification of pattern recognition and machine learning methods to solve the problem of intrusion detection in network systems. …”
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  8. 8
    SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan

    SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan by Mazlan, Muhammad Muhaimin Aiman

    Published 2018
    “…The software will be trained by using NSL KDD Cup dataset in order to make it learns about the Neptune attack and as a result, it will be able to detect and prevent such attack. …”
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  9. 9
    Application of machine learning and artificial intelligence in detecting SQL injection attacks

    Application of machine learning and artificial intelligence in detecting SQL injection attacks by Md Sultan, Abu Bakar, Agiliga, Nwabudike Augustine, Osman, Mohd Hafeez Bin, Sharif, Khaironi Yatim

    Published 2024
    “…Datasets of well-known SQL injection attack patterns and AI/ML models intended for cybersecurity anomaly detection are among the resources underexplored, these findings show the potential for boosting detection capabilities by deploying ML and AI-based security solutions, with some algorithms scoring up to an 80 percent success rate in identifying SQL injections. …”
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  10. 10
    A lightweight graph-based pattern recognition scheme in mobile ad hoc networks.

    A lightweight graph-based pattern recognition scheme in mobile ad hoc networks. by Raja Mahmood, Raja Azlina, Muhamad Amin, Anang Hudaya, Amir, Amiza, Khan, Asad I.

    Published 2012
    “…Both algorithms show comparable detection results. Thus, the lightweight, low computation DHGN based detection scheme offers an effective security solution in MANETs.…”
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  11. 11
    Adaptive feature selection for denial of services (DoS) attack

    Adaptive feature selection for denial of services (DoS) attack by Yusof, Ahmad Riza'ain, Udzir, Nur Izura, Selamat, Ali, Hamdan, Hazlina, Abdullah @ Selimun, Mohd Taufik

    Published 2017
    “…Adaptive detection is the learning ability to detect any changes in patterns in intrusion detection systems. …”
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    Conference or Workshop Item
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  12. 12
    Network intrusion detection and alert system

    Network intrusion detection and alert system by To, Jin Yi

    Published 2024
    “…Signature-based detection compares network traffic packets with a real-time updated database of known attack patterns, while anomaly-based detection algorithms learn normal behavior patterns and identify deviations. …”
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    Final Year Project / Dissertation / Thesis
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  13. 13
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…Finally, the algorithm found, which would solve the image segmentation problem.…”
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  14. 14
    An enhanced android botnet detection approach using feature refinement

    An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

    Published 2019
    “…In order to detect botnet attacks which causes immense chaos and problems to smartphones, first the Android botnet need to be analysed. …”
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  15. 15
    Hybrid of rough set theory and Artificial Immune Recognition System as a solution to decrease false alarm rate in intrusion detection system

    Hybrid of rough set theory and Artificial Immune Recognition System as a solution to decrease false alarm rate in intrusion detection system by Sabri F.N.M., Norwawi N.M., Seman K.

    Published 2024
    Conference Paper
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  16. 16
    Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest

    Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest by Ghulam Hussain, Muhammad Thaqif, Shafeeq Lone, Aman, Maspo, Nur-Adib, Attarbashi, Zainab

    Published 2026
    “…This paper presents an unsupervised network-based anomaly detection framework that integrates deep autoencoders with the Isolation Forest algorithm. …”
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  17. 17
    An integrated anomaly intrusion detection scheme using statistical, hybridized classifiers and signature approach

    An integrated anomaly intrusion detection scheme using statistical, hybridized classifiers and signature approach by Mohamed Yassin, Warusia

    Published 2015
    “…Although the statistical-based anomaly detection (SAD) method fascinates researchers, the low attack detection rates (also known as the detection of true positive) that reflect the effectiveness of the detection system generally persist. …”
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  18. 18
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
    “…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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  19. 19
    Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling

    Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling by Zulaiha Ali Othman, Afaf Muftah Adabashi, Suhaila Zainudin, Saadat M. Al Hashmi

    Published 2011
    “…The result has shown that this algorithm has increased the detection rate and reduced the false alarm rate compared with Fuzzy-ART.…”
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  20. 20
    Q-Learning-based detection of IPv6 intrusions: a behavioral and performance study

    Q-Learning-based detection of IPv6 intrusions: a behavioral and performance study by Daru, April Firman, Hirzan, Alauddin Maulana, Mahmod Attar Bashi, Zainab Senan, Fanani, Fajriannoor

    Published 2025
    “…This study launched different ICMPv6 attacks individually and gathered five million samples for each intrusion attack. …”
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