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  1. 1
    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification

    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification by Al-Kharasani, Ameen Mohammed Abdulkarem

    Published 2019
    “…Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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  2. 2
    A new sinkhole attack detection algorithm for RPL in wireless sensor networks (WSN)

    A new sinkhole attack detection algorithm for RPL in wireless sensor networks (WSN) by Ali, Mohammed Qasim

    Published 2019
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  3. 3
    CHID : conditional hybrid intrusion detection system for reducing false positives and resource consumption on malicous datasets

    CHID : conditional hybrid intrusion detection system for reducing false positives and resource consumption on malicous datasets by Alaidaros, Hashem Mohammed

    Published 2017
    “…In addition, it is also aimed to improve the resource consumption of the packet-based detection approach. CHID applied attribute wrapper features evaluation algorithms that marked malicious flows for further analysis by the packet-based detection. …”
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  4. 4
    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
    “…The effectiveness of the IADS has been evaluated under different detection capabilities (i.e., false positive, false negative, true positive, true negative, false alarm, accuracy,detection rate, attack data detection rate, normal data detection rate) and detection times using the DARPA 1999 and ISCX 2012 intrusion detection benchmark datasets as well as with Live-data. …”
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  5. 5
    QoS evaluation of different TCPs congestion control algorithm using NS2

    QoS evaluation of different TCPs congestion control algorithm using NS2 by Tarmizi, M., Albagul, A., Khalifa, Othman Omran, Wahyudi, Martono

    Published 2006
    “…To achieve this, the users implement a flow control algorithm that controls the rate at which data packets are sent into the Internet. …”
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  6. 6
    Anomaly behavior detection using flexible packet filtering and support vector machine algorithms

    Anomaly behavior detection using flexible packet filtering and support vector machine algorithms by Abdul Wahid, Mohammed N.

    Published 2016
    “…FPF of SVM have merged the analyzed results of flexible packet filtering with support vector machine algorithm to get better classification of the captured network traffics and to detect anomalies. …”
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  7. 7
    DDoS Classification using Combined Techniques

    DDoS Classification using Combined Techniques by Mohd Yusof, Mohd Azahari, Mohd Safar, Noor Zuraidin, Abdullah, Zubaile, Hamid Ali, Firkhan Ali, Mohamad Sukri, Khairul Amin, Jofri, Muhamad Hanif, Mohamed, Juliana, Omar, Abdul Halim, Bahrudin, Ida Aryanie, Mohamed Ali @ Md Hani, Mohd Hatta

    Published 2024
    “…Based on the results of detection accuracy and false positive rate for all the techniques involved, it proves the PTA-KNN technique is a more effective technique in the context of detection of incoming packets whether DDoS attacks or normal packets…”
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  8. 8
    DDoS Classification using Combined Techniques

    DDoS Classification using Combined Techniques by Mohd Yusof, Mohd Azahari, Mohd Safar, Noor Zuraidin, Abdullah, Zubaile, Hamid Ali, Firkhan Ali, Mohamad Sukri, Khairul Amin, Jofri, Muhamad Hanif, Mohamed, Juliana, Omar, Abdul Halim, Bahrudin, Ida Aryanie, Mohamed Ali @ Md Hani, Mohd Hatta

    Published 2024
    “…Based on the results of detection accuracy and false positive rate for all the techniques involved, it proves the PTA-KNN technique is a more effective technique in the context of detection of incoming packets whether DDoS attacks or normal packets.…”
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  9. 9
    Effective dimensionality reduction of payload-based anomaly detection in TMAD model for HTTP payload

    Effective dimensionality reduction of payload-based anomaly detection in TMAD model for HTTP payload by Kakavand, Mohsen, Mustapha, Norwati, Mustapha, Aida, Abdullah @ Selimun, Mohd Taufik

    Published 2016
    “…The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. …”
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  10. 10
    Privacy optimization and intrusion detection in modbus/tcp network-based scada in water distribution systems

    Privacy optimization and intrusion detection in modbus/tcp network-based scada in water distribution systems by Franco, Daniel Jose Da Graca Peceguina

    Published 2021
    “…Therefore, the first problem to be tackled is the privacy optimization of Modbus/TCP packet fields. In scientific literatures, packet anonymization is performed according to attribute types (numerical, categorical and hierarchical), not taking into consideration the singular characteristics of the Modbus packet fields, using Euclidean distance algorithms that are not capable to deal with binary data and may result in information loss. …”
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  11. 11
    Non-invasive pathological voice classifications using linear and non-linear classifiers

    Non-invasive pathological voice classifications using linear and non-linear classifiers by Hariharan, Muthusamy

    Published 2010
    “…In the case of detection of specific disorders, wavelet packet and entropy features perform well compared to time-domain energy variations based features and MFCCs and SVD based features. …”
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  12. 12
    Novel Link Establishment Communication Scheme against Selfish Attack Using Node Reward with Trust Level Evaluation Algorithm in MANET

    Novel Link Establishment Communication Scheme against Selfish Attack Using Node Reward with Trust Level Evaluation Algorithm in MANET by Hemalatha, S., Kshirsagar, P.R., Manoharan, H., Vasantha Gowri, N., Vani, A., Qaiyum, S., Vijayakumar, P., Tirth, V., Haleem, S.L.A., Chakrabarti, P., Teressa, D.M.

    Published 2022
    “…It minimizes the transmission rate and network lifetime and improves packet latency and energy usage. …”
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  13. 13
    SVM driven approach for detecting DoS attacks in SDN environment

    SVM driven approach for detecting DoS attacks in SDN environment by Najmun, Nisa, Adnan Shahid, Khan, Azman, Bujang Masli, Nusrat, Shaheen

    Published 2025
    “…To address this challenge, we propose a novel approach that efficiently detects DoS attacks by implementing a packet inspection process using a queuing mechanism, followed by machine learning classification using SVM and KNN algorithms. …”
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  14. 14
    Anomaly detection in ICS datasets with machine learning algorithms

    Anomaly detection in ICS datasets with machine learning algorithms by Mubarak, Sinil, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Abdul Rahman, Farah Diyana, Tahir, Mohammad

    Published 2021
    “…The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as the first line of defense, have been detailed. …”
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  15. 15
    An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons

    An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons by Ghanem, Waheed Ali H. M., Aman, Jantan, Ahmed Ghaleb, Sanaa Abduljabbar, Naseer, Abdullah B.

    Published 2020
    “…This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. …”
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  16. 16
    Multiuser Sensing for Opportunistic Spectrum Access in Cognitive Radio Networks

    Multiuser Sensing for Opportunistic Spectrum Access in Cognitive Radio Networks by Armi, Nasrullah, Yusoff, Mohd Zuki, M Saad, M Naufal

    Published 2011
    “…Subsequently, number of user and packet arrival rate can affect the throughput performance of SUs as well.…”
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  17. 17
    Multiuser Sensing for Opportunistic Spectrum Access in Cognitive Radio Networks

    Multiuser Sensing for Opportunistic Spectrum Access in Cognitive Radio Networks by Armi, Nasrullah, Yusoff, Mohd Zuki, M Saad, M Naufal

    Published 2011
    “…Subsequently, number of user and packet arrival rate can affect the throughput performance of SUs as well.…”
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  18. 18
    Detection of black hole nodes in mobile ad hoc network using hybrid trustworthiness and energy consumption techniques

    Detection of black hole nodes in mobile ad hoc network using hybrid trustworthiness and energy consumption techniques by Mustafa, Ahmed Sudad

    Published 2017
    “…On the other hand, the detection algorithm based on the energy consumption achieves PDR of 91.6%, EED of 14.03 ms and detection rate accuracy of 93%. …”
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  19. 19
    Depth frame loss concealment for wireless transmission utilising motion detection information

    Depth frame loss concealment for wireless transmission utilising motion detection information by Ranjbari, Mohamadreza

    Published 2014
    “…The proposed method is able to improve the quality of video in comparison with frame copy algorithm. The feasibility and performance of the proposed method based on motion detection error concealment is evaluated analytically by considering different packet and frame loss rates. …”
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  20. 20
    Mitigating Slow Hypertext Transfer Protocol Distributed Denial of Service Attacks in Software Defined Networks

    Mitigating Slow Hypertext Transfer Protocol Distributed Denial of Service Attacks in Software Defined Networks by Akanji, Oluwatobi Shadrach, Abisoye, Opeyemi Aderiike, Iliyasu, Mohammed Awwal

    Published 2021
    “…Results obtained showed that the classifier had detection accuracy, Area Under Receiver Operating Curve (AUC), true positive rate, false positive rate and a false negative rate of 99.89%, 99.89%, 99.95%, 0.18%, and 0.05% respectively. …”
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