Document Type: Research Article

Authors

1 1Department of Management Information systems, Cyprus International University, North Cyprus

2 Department of Management Information Systems, Cyprus International University, Nicosia, North Cyprus

10.22042/isecure.2019.11.3.0

Abstract

In recent years technology and management information system has been an excellent response too many global challenges, technology innovation has expanded over almost all the sectors of, and it made many processes more accurate and very faster than before. Technology systems playeda big role part in election processes in many democratic countries nowadays. The commission, in Iraq, suffers from many problems such as fraud, time-consuming and delays in the election processes that take a long time and also witness a delay in revealing the results. This research paper focuses on adapting the biometric system in Iraq; there are several different perspectives to specify the IHEC’s employees and manager’s attitude towards technology in general and Biometric system specifically. Most of the staff members feel confident about transforming into a technology system. In their responses to the questionnaires, most of them focused on getting trained before they start using the system. In this research, the data is collected by using survey technique from the independent high electoral commission managers and staff members, and the data is analyzed by using SPSS.

Keywords

[1] F. Valeur, G. Vigna, C. Kruegel, and R.A. Kemmerer. A Comprehensive Approach to Intrusion Detection Alert Correlation. IEEE Transactions on Dependable and Secure Computing, 1(3):146– 169, 2004.

[2] T. Pietraszek. Using Adaptive Alert Classification to Reduce False Positives in Intrusion Detection. In Recent Advances in Intrusion Detection, pages 102–124, 2004.

[3] R. Smith, N. Japkowicz, M. Dondo, and P. Mason. Using Unsupervised Learning for Network Alert Correlation. In Advances in Artificial Intelligence, pages 308–319, 2008.

[4] B. Morin, L. Mé, H. Debar, and M. Ducassé. M2D2: A Formal Data Model for IDS Alert Correlation. In Proceedings of the 5th International Symposium on Recent Advances in Intrusion Detection, RAID ’02, pages 115–137, 2002.

[5] F. Cuppens and A. Miège. Alert Correlation in a Cooperative Intrusion Detection Framework. In Proceedings of the 2002 IEEE Symposium on Security and Privacy, 2002.

[6] X. Peng, Y. Zhang, S. Xiao, Z. Wu, J. Cui, L. Chen, and D. Xiao. An Alert Correlation Method Based on Improved Cluster Algorithm. In Proceedings of Computational Intelligence and Industrial Application, PACIIA ’08, pages 342– 347, 2008.

[7] W. Li, L. Zhi-tang, L. Jie, and L. Yao. A Novel Algorithm SF for Mining Attack Scenarios Model. In Proceedings of IEEE International Conference on e-Business Engineering, ICEBE ’06, pages 55–61, 2006.

[8] B. Zhu and A.A. Ghorbani. Alert Correlation for Extracting Attack Strategies. International Journal of Network Security, 3(3):244–258, 2006.

[9] S.O. Al-Mamory and H. Zhang. IDS Alerts Correlation Using Grammar-based Approach. Journal in Computer Virology, 2008.

[10] S.J. Templeton and K. Levitt. A Requires/Provides Model for Computer Attacks. In Proceedings of New Security Paradigms Workshop, 2000.

[11] M.S. Shin and K.J. Jeong. An Alert Data Mining Framework for Network-Based Intrusion Detection System. In Proceedings of the 6th International Workshop Information Security Applications, pages 38–53, 2006.

[12] P. Bahreini, M. AmirHaeri, and R. Jalili. A Probabilistic Approach to Intrusion Alert Correlation. In Proceedings of 5th International ISC Conference on Information Security & Cryptology, 2008.

[13] A. Valdes and K. Skinner. Probabilistic Alert Correlation. In Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection, 2001.

[14] MIT Lincoln Laboratory. 2000 DARPA Intrusion Detection Scenario Specific Data Sets, 2000. [15] North Carolina State University Cyber Defense Laboratory. TIAA: A Toolkit for Intrusion Alert Analysis, Accessed May 24, 2009. Available from: http://discovery.csc.ncsu.edu/ software/correlator/ver1.0/.

[16] P. Ning, Y. Cui, and D. Reeves. Analyzing Intensive Intrusion Alerts Via Correlation. In Proceedings of the 5th International Symposium on Recent Advances in Intrusion Detection, RAID ’02, pages 74–94, 2002.

[17] O. De Vel, N. Liu, T. Caelli, and T.S. Caetano. An Embedded Bayesian Network Hidden Markov Model for Digital Forensics. In Proceedings of the International Conference on Intelligence and Security Informatics, ISI ’06, pages 459–465, 2006.

[18] D. Ourston, S. Matzner, W. Stump, and B. Hopkins. Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks. In Proceedings of the 36th Annual Hawaii International Conference on System Sciences, HICSS ’03, 2003.

[19] D. Lee, D. Kim, and J. Jung. Multi-Stage Intrusion Detection System Using Hidden Markov Model Algorithm. In Proceedings of the International Conference on Information Science and Security, ICISS ’08, pages 72–77, 2008.

[20] Y. Zhai, P. Ning, P. Iyer, and D.S. Reeves. Reasoning About Complementary Intrusion Evidence. In Proceedings of the 20th Annual Computer Security Applications Conference, ACSAC ’04, pages 39–48, 2004.

[21] A. Ehrenfeucht and J. Mycielski. A Pseudorandom Sequence—How Random Is It? The American Mathematical Monthly, 99:373–375, 1992.

[22] X. Qin and W. Lee. Attack Plan Recognition and Prediction Using Causal Networks. In Proceedings of the 20th Annual Computer Security Applications Conference, ACSAC ’04, pages 370– 379, 2004.

[23] W. Lee and X. Qin. Statistical Causality Analysis of Infosec Alert Data. In Proceedings of the 6th International Symposium on Recent Advances in Intrusion Detection, RAID ’03, pages 73–93, 2003.