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  1. 1
    An approach to improve detecting and removing cross- site scripting vulnerabilities in web applications

    An approach to improve detecting and removing cross- site scripting vulnerabilities in web applications by Hydara, Isatou

    Published 2015
    “…This approach is also able to remove the vulnerabilities detected in the tested web application source codes. …”
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  2. 2
    Early detection and mitigation of DDoS attacks in software defined networks

    Early detection and mitigation of DDoS attacks in software defined networks by Al-Saadi, Mustafa Yahya Zakariya

    Published 2018
    “…To detect and mitigate this attack at its early stages, an entropy-based DDoS attack detection and mitigation algorithm was proposed. …”
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  3. 3
    A novel energy-efficient sybil node detection algorithm for intrusion detection system in wireless sensor networks

    A novel energy-efficient sybil node detection algorithm for intrusion detection system in wireless sensor networks by Karuppiah, A. Babu, Dalfiah, J., Yuvashri, K., Rajaram, S., Pathan, Al-Sakib Khan

    Published 2014
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  4. 4
    An approach for cross-site scripting detection and removal based on genetic algorithms.

    An approach for cross-site scripting detection and removal based on genetic algorithms. by Hydara, Isatou, Md Sultan, Abu Bakar, Zulzalil, Hazura, Admodisastro, Novia

    Published 2014
    “…In this paper, we propose to explore an approach based on genetic algorithms that will be able to detect and remove cross-site scripting vulnerabilities from the source code before an application is deployed. …”
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  5. 5
    Cross-site scripting detection based on an enhanced genetic algorithm

    Cross-site scripting detection based on an enhanced genetic algorithm by Hydara, Isatou, Md Sultan, Abu Bakar, Zulzalil, Hazura, Admodisastro, Novia Indriaty

    Published 2015
    “…In this paper, we propose to explore an approach based on genetic algorithms that will be able to detect cross-site scripting vulnerabilities in the source code before an application is deployed. …”
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  6. 6
    Designing a New Model for Worm Response Using Security Metrics

    Designing a New Model for Worm Response Using Security Metrics by Madihah Mohd Saudi, Taib, BM

    Published 2024
    “…Once the user's computer detects any indication of the worm attacks, the apoptosis is triggered. …”
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  7. 7
    A new mobile botnet classification based on permission and API calls

    A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

    Published 2024
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  8. 8
    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
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  9. 9
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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  10. 10
    Designing a new model for worm response using security metrics

    Designing a new model for worm response using security metrics by Madihah Mohd Saudi, Taib B.M.

    Published 2024
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  11. 11
    A New Mobile Botnet Classification based on Permission and API Calls

    A New Mobile Botnet Classification based on Permission and API Calls by Yusof, M, Saudi, MM, Ridzuan, F

    Published 2024
    “…As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. …”
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  12. 12
    A systematic analysis on worm detection in cloud-based systems

    A systematic analysis on worm detection in cloud-based systems by Kanaker H.M., Madihah Mohd Saudi, Mohd Fadzli Marhusin

    Published 2024
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  13. 13
    Secure communication in Vehicular ad hoc network using modified ad hoc on demand distance vector

    Secure communication in Vehicular ad hoc network using modified ad hoc on demand distance vector by Abdulkader, Zaid A.

    Published 2018
    “…The OMNET++ simulation program is used to justify the proposed algorithms. …”
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  14. 14
    A multi-filter feature selection in detecting distributed denial-of-service attack

    A multi-filter feature selection in detecting distributed denial-of-service attack by Yon, Yi Jun, Leau, Yu-Beng, Suraya Alias, Park, Yong Jin

    Published 2019
    “…Thus, making it hard to detect and successfully bypasses the detection mechanism. …”
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  15. 15
    AI recommendation penetration testing tool for cross-site scripting: support vector machine algorithm

    AI recommendation penetration testing tool for cross-site scripting: support vector machine algorithm by Salim, Nur Saadah, Saad, Shahadan

    Published 2025
    “…This research introduces a new approach to enhancing cybersecurity by integrating Support Vector Machine (SVM) algorithms with penetration testing to develop a recommendation system focused on Cross-Site Scripting (XSS) attack detection. …”
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  16. 16
    Precise ICMP Traceback Based On Normal Flow Filtration in Denial of Services Attack

    Precise ICMP Traceback Based On Normal Flow Filtration in Denial of Services Attack by Izaddoost, Alireza

    Published 2008
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  17. 17
    Security testing of web applications for detecting and removing second-order SQL injection vulnerabilities

    Security testing of web applications for detecting and removing second-order SQL injection vulnerabilities by Draib, Najla'a Ateeq Mohammed

    Published 2022
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  18. 18
    Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application

    Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application by Yana Aditia, Gerhana, Nur, Lukman, Arief Fatchul, Huda, Cecep Nurul, Alam, Undang, Syaripudin, Devi, Novitasari

    Published 2020
    “…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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  19. 19
    A stylometry approach for blind linguistic steganalysis model against translation-based steganography

    A stylometry approach for blind linguistic steganalysis model against translation-based steganography by Mohd Lokman, Syiham

    Published 2023
    “…While targeted steganalysis is designed to attack a specific embedding algorithm, blind steganalysis use features extracted or selection from the medium to detect any anomalies that indicate a possibility that a secret data has been embedded within the medium. …”
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  20. 20
    An Analysis Of System Calls Using J48 And JRip For Malware Detection

    An Analysis Of System Calls Using J48 And JRip For Malware Detection by Abdollah, Mohd Faizal, Abdullah, Raihana Syahirah, S. M. M Yassin, S. M. Warusia Mohamed, Selamat, Siti Rahayu, Mohd Saudi, Nur Hidayah

    Published 2018
    “…The evolution of malware possesses serious threat ever since the concept of malware took root in the technology industry.The malicious software which is specifically designed to disrupt,damage,or gain authorized access to a computer system has made a lot of researchers try to develop a new and better technique to detect malware but it is still inaccurate in distinguishing the malware activities and ineffective.To solve the problem,this paper proposed the integrated machine learning methods consist of J48 and JRip in detecting the malware accurately.The integrated classifier algorithm applied to examine,classify and generate rules of the pattern and program behaviour of system call information.The outcome then revealed the integrated classifier of J48 and JRip outperforming the other classifier with 100% detection of attack rate. …”
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