Search Results - (( security classifications _ algorithm ) OR ( java application optimization algorithm ))

Refine Results
  1. 1

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

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
    Get full text
    Get full text
    Thesis
  2. 2
  3. 3

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
    Get full text
    Get full text
    Conference or Workshop Item
  4. 4

    Symmetric Key Size for Different Level of Information Classification by Ibrahim, Subariah, Maarof, Mohd. Aizaini

    Published 2006
    “…Therefore confidential information is normally protected by using cryptographic algorithms. In these algorithms, key is an important element since it is one of the parameters that determine the level of security that the algorithms can provide. …”
    Get full text
    Get full text
    Conference or Workshop Item
  5. 5

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
    Get full text
    Get full text
    Final Year Project
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

    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. …”
    Get full text
    Get full text
    Thesis
  11. 11
  12. 12

    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. To this end, this paper studies the classification analysis of intrusion detection using various supervised learning algorithms such as SVM, Naive Bayes, KNN, Random Forest, Logistic Regression and Decision tree on the NSL-KDD dataset. …”
    Get full text
    Get full text
    Get full text
    Article
  13. 13
  14. 14
  15. 15
  16. 16

    Power system security assessment using artificial neural network: article / Mohd Fathi Zakaria by Zakaria, Mohd Fathi

    Published 2010
    “…This paper presented an application of Artificial Neural Network (ANN) in steady state stability classifications. A multi layer feed forward ANN with Back Propagation Network algorithm is proposed in determining the steady state stability classifications. …”
    Get full text
    Get full text
    Article
  17. 17

    K-NN classifier for data confidentiality in cloud computing by Zardari, M.A., Jung, L.T., Zakaria, N.

    Published 2014
    “…After data classification we got that what data need security and what data does not need security. …”
    Get full text
    Get full text
    Conference or Workshop Item
  18. 18
  19. 19
  20. 20

    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. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis