Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
Malicious software or widely known as malware has inflicted a great number of computers and causing many intrusion and damages that wasted a lot of money and resource. Despite having a new variant and type of malware appeared almost every day, traditional worm such as Blaster are still posing threats these days due to its rapid distribution through the internet. This research is previously analyzed manually using packet analyzer namely tcpdump and wireshark which is time-consuming and inefficient. To overcome this problem, an automated script known as Malware Attack Visualization (MAV) Script is developed to automate the visualization of the malware attack scenario. This script is capable to analyze and dissect the network traffic and represent the scenario in visualization. This information is crucial as it helps to identify the sources of the attack and the location of the incurred damage. Thus, this script will help to determine and visualize the malware attack scenario which eases the process of finding the Attacker, Victim, and Victim/Attacker of the attack
Decision tree is an important method in data mining to solve the classification problems. There are several learning algorithms to implement the decision tree but the most commonly-used is ID3 algorithm. Nevertheless, there are some limitations in ID3 algorithm that can affect the performance in the classification of data. The use of information gain in the ID3 algorithm as the attribute selection criteria is not to assess the relationship between classification and the dataset’s attributes. The objective of the study being conducted is to implement the attribute related methods to solve the shortcomings of the ID3 algorithm like the tendency to select attributes with many values and also improve the performance of ID3 algorithm. The techniques of attribute related methods studied in this paper were mutual information, association function and attribute weighted. All the techniques assist the decision tree to find the most optimal attributes in each generation of the tree. Results of the reviewed techniques show that attribute selection methods capable to resolve the limitations in ID3 algorithm and increase the performance of the method. All of the reviewed techniques have their advantages and disadvantages and useful to solve the classification problems. Implementation of the techniques with ID3 algorithm is being discussed thoroughly