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  1. 1
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…An Intrusion Detection System is software or application which is used to detect thread, malicious activities and the unauthorized access to the computer system and warn the administrators by generating alarms. …”
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  2. 2
    Security alert framework using dynamic tweet-based features for phishing detection on twitter

    Security alert framework using dynamic tweet-based features for phishing detection on twitter by Liew, Seow Wooi

    Published 2019
    “…This framework is divided into three phases which are classification model of phishing detection, detection algorithm of phishing tweet detection and security alert mechanism of phishing tweet detection. …”
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  3. 3
    An analysis of intrusion detection classification using supervised machine learning algorithms on NSL-KDD dataset / Sarthak Rastogi ... [et al.]

    An analysis of intrusion detection classification using supervised machine learning algorithms on NSL-KDD dataset / Sarthak Rastogi ... [et al.] by Rastogi, Sarthak, Shrotriya, Archit, Singh, Mitul Kumar, Potukuchi, Raghu Vamsi

    Published 2022
    “…The IDS with machine learning method improves the detection accuracy of the security attacks. …”
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  4. 4
    Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier

    Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier by Siow, Shien Loong

    Published 2018
    “…Iris is one of the biometric that widely used in the field of security due to its uniqueness. There are a lot of feature extraction methods and classification methods for iris classification. …”
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  5. 5
    ChoCD : Usable and secure graphical password authentication scheme

    ChoCD : Usable and secure graphical password authentication scheme by Radhi Rafiee Bin Afandi

    Published 2024
    thesis::master thesis
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  6. 6
    Improved method of classification algorithms for crime prediction

    Improved method of classification algorithms for crime prediction by Babakura, Abba, Sulaiman, Md. Nasir, Yusuf, Mahmud Ahmad

    Published 2014
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  7. 7
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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  8. 8
    Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification

    Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification by Ghanem, W.A.H.M, El-Ebiary, Y.A.B., Abdulnab, M., Tubishat, M., Alduais, N.A.M., Nasser, A.B., Abdullah, N., Al-wesabi, O.A.

    Published 2021
    “…This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. …”
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  9. 9
    Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

    Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions by Gorment N.Z., Selamat A., Cheng L.K., Krejcar O.

    Published 2024
    “…The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. …”
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  10. 10
    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization

    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2015
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  11. 11
    Data classification based on confidentiality in virtual cloud environment

    Data classification based on confidentiality in virtual cloud environment by Zardari, M.A., Jung, L.T., Zakaria, M.N.B.

    Published 2014
    “…It is very difficult to decide (in cloud) which data need what security and which data do not need security. However it will be easy to decide the security level for data after data classification according to their security level based on the characteristics of the data. …”
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  12. 12
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
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  13. 13
    File integrity monitor scheduling based on file security level classification

    File integrity monitor scheduling based on file security level classification by Abdullah, Zul Hilmi, Udzir, Nur Izura, Mahmod, Ramlan, Samsudin, Khairulmizam

    Published 2011
    “…This paper proposes an enhancement to the scheduling algorithm of the current file integrity monitoring approach by combining the off-line and on-line monitoring approach with dynamic inspection scheduling by performing file classification technique. …”
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  14. 14
    A Framework For Classification Software Security Using Common Vulnerabilities And Exposures

    A Framework For Classification Software Security Using Common Vulnerabilities And Exposures by Hassan, Nor Hafeizah

    Published 2018
    “…This inclusive of the investigation of vulnerability classification objectives,processes,classifiers and focus domains among prominent framework.Final step is to construct the framework by establishing the formal presentation of the vulnerability classification algo-rithm.The validation process was conducted empirically using statistical method to assess the accuracy and consistency by using the precision and recall rate of the algorithm on five data sets each with 500 samples.The findings show a significant result with precision's error rate or p value is between 0.01 and 0.02 with error rate for recall's error rate is between 0.02 and 0.04.Another validation was conducted to verify the correctness of the classification by using expert opinions,and the results showed that the ambiguity of several cases were subdue. …”
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  15. 15
    Provider independent cryptographic tools

    Provider independent cryptographic tools by Ibrahim, Subariah, Salleh, Mazleena, Abdul Aziz, Shah Rizan

    Published 2003
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  16. 16
    Enhancement Of Cover Selection – Based Audio Steganography (CAS) Using Block-Based Chaotic Multi – Level Lsb (BCM– LSB) For Balanced Performance

    Enhancement Of Cover Selection – Based Audio Steganography (CAS) Using Block-Based Chaotic Multi – Level Lsb (BCM– LSB) For Balanced Performance by Muhammad Harith bin Noor Azam

    Published 2024
    thesis::doctoral thesis
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  17. 17
    Review of the machine learning methods in the classification of phishing attack

    Review of the machine learning methods in the classification of phishing attack by Jupin, John Arthur, Sutikno, Tole, Mohd Arfian, Ismail, Mohd Saberi, Mohamad, Shahreen, Kasim, Deris, Stiawan

    Published 2019
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  18. 18
    A novel framework for identifying twitter spam data using machine learning algorithms

    A novel framework for identifying twitter spam data using machine learning algorithms by Maziku, Susana Boniphace, Abdul Rahiman, Amir Rizaan, Muhammed, Abdullah, Abdullah @ Selimun, Mohd Taufik

    Published 2020
    “…The research results contribute significantly to the field of cyber-security by forming a real-time system using machine learning algorithms.…”
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  19. 19
    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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  20. 20
    Named entity recognition using a new fuzzy support vector machine.

    Named entity recognition using a new fuzzy support vector machine. by Mansouri, Alireza, Affendy, Lilly Suriani, Mamat, Ali

    Published 2008
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