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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Nurul Fariza Zulkurnain
International Islamic University Malaysia

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On the Review and Setup of Security Audit using Kali Linux Teddy Surya Gunawan; Muhammad Kassim Lim; Nurul Fariza Zulkurnain; Mira Kartiwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp51-59

Abstract

The massive development of technology especially in computers, mobile devices, and networking has bring security issue forward as primarily concern. The computers and mobile devices connected to Internet are exposed to numerous threats and exploits. With the utilization of penetration testing, vulnerabilities of a system can be identified and simulated attack can be launched to determine how severe the vulnerabilities are. This paper reviewed some of the security concepts, including penetration testing, security analysis, and security audit. On the other hand, Kali Linux is the most popular penetration testing and security audit platform with advanced tools to detect any vulnerabilities uncovered in the target machine. For this purpose, Kali Linux setup and installation will be described in more details. Moreover, a method to install vulnerable server was also presented. Further research including simulated attacks to vulnerable server on both web and firewall system will be conducted.
Towards Scalable Algorithm for Closed Itemset Mining in High-Dimensional Data Fatimah Audah Md. Zaki; Nurul Fariza Zulkurnain
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i2.pp487-494

Abstract

Mining frequent itemsets from large dataset has a major drawback in which the explosive number of itemsets requires additional mining process which might filter the interesting ones. Therefore, as the solution, the concept of closed frequent itemset was introduced that is lossless and condensed representation of all the frequent itemsets and their corresponding supports.  Unfortunately, many algorithms are not memory-efficient since it requires the storage of closed itemsets in main memory for duplication checks. This paper presents BFF, a scalable algorithm for discovering closed frequent itemsets from high-dimensional data. Unlike many well-known algorithms, BFF traverses the search tree in breadth-first manner resulted to a minimum use of memory and less running time. The tests conducted on a number of microarray datasets show that the performance of this algorithm improved significantly as the support threshold decreases which is crucial in generating more interesting rules.
Co-Authors Fatimah Audah Md. Zaki Mira Kartiwi Muhammad Kassim Lim Teddy Surya Gunawan