AIRCC's International Journal of Computer Science and Information Technology

Bambang Sugiantoro ¹, Retantyo Wardoyo ², Sri Hartati ², Jazi Eko Istiyanto ²

Affiliations
1 Dept. of Informatics Engineering, State Islamic University Sunan Kalijaga, Yogyakarta, ID
2 Dept. of Computer Sciences and Electronics, Gadjah Mada University, Yogyakarta, ID

Abstract

Mobile agent is a certain agent actively able to move from one computer to another or even travel across network to perform the task assigned. This research aimed to develop mobile agent model to detect distributed intrusion (Distributed Intrusion Detection System).

It was expected that the produced model was able to perform its task in nearly real time manner, to be immune against attack, and also fairly small overhead at the host, either in memory consumption or CPU usage. In this research, the Aglets concept was also developed to aid intrusion detection. This approach was also maintained to solve certain issues such as scalability, reliability, and configurability. Hence, the advantages and limitations of agent-based approaches will be when it is applied in real world.

Keywords

Developing Mobile Agent Model, Aglets and Intrusion Detection.

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Andi Wahju Rahardjo Emanuel ¹, Retantyo Wardoyo ², Jazi Eko Istiyanto ², Khabib Mustofa ²

Affiliations
1 Maranatha Christian University, ID
2 Department of Computer Science and Electronics, Gadjah Mada University, ID

Abstract

Modularity has been identified by many researchers as one of the success factors of Open Source Software (OSS) Projects. This modularity trait are influenced by some aspects of software metrics such as size, complexity, cohesion, and coupling. In this research, we analyze the software metrics such as Size Metrics (NCLOC, Lines, and Statements), Complexity Metrics (McCabe's Cyclomatic Complexity), Cohesion Metrics (LCOM4), and Coupling Metrics (RFC, Afferent coupling and Efferent coupling) of 59 Java-based OSS Projects from Sourceforge.net. By assuming that the number of downloads can be used as the indication of success of these projects, the OSS Projects being selected are the projects which have been downloaded more than 100,000 times. The software metrics reflecting the modularity of these projects are collected using SONAR tool and then statistically analyzed using scatter graph, Pearson r product-moment correlation, and least-square-fit linear approximation. It can be shown that there are only three independent metrics reflecting modularity which are NCLOC, LCOM4, and Afferent Coupling, whereas there is also one inconclusive result regarding Efferent Coupling.

Keywords

Open Source Software Project, Modularity, Software Metrics, Statistical Analysis, Java.

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