Ventje Jeremias Lewi Engel
Harapan Bangsa Institute of Technology

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DETECTION OF CYBER MALWARE ATTACK BASED ON NETWORK TRAFFIC FEATURES USING NEURAL NETWORK Engel, Ventje Jeremias Lewi; Joshua, Evan; Engel, Mychael Maoeretz
Khazanah Informatika Vol. 6 No. 1 April 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i1.8869

Abstract

Various techniques have been developed to detect cyber malware attacks, such as behavior based method which utilizes the analysis of permissions and system calls made by a process. However, this technique cannot handle the types of malware that continue to evolve. Therefore, an analysis of other suspicious activities ? namely network traffic or network traffic ? need to be conducted. Network traffic acts as a medium for sending information used by malware developers to communicate with malware infecting a victim's device. Malware analyzed in this study is divided into 3 classes, namely adware, general malware, and benign. The malware classification implements 79 features extracted from network traffic flow and an analysis of these features using a Neural Network that matches the characteristics of a time-series feature. The total flow of network traffic used is 442,240 data. The results showed that 15 main features selected based on literature studies resulted in F-measure 0.6404 with hidden neurons 12, learning rate 0.1, and epoch 300. As a comparison, the researchers chose 12 features based on the nature of the malware possessed, with the F-measure score of 0.666 with hidden neurons 12, learning rate 0.05, and epoch 300. This study found the importance of data normalization technique to ensure that no feature was far more dominant than other features. It was concluded that the analysis of network traffic features using Neural Network can be used to detect cyber malware attacks and more features does not imply better detection performance, but real-time malware detection is required for network traffic on IoT devices and smartphones.
Interaction monitoring model of logo counseling website for college students’ healthy self-esteem Jacob Daan Engel; Ventje Jeremias Lewi Engel; Evangs Mailoa
International Journal of Evaluation and Research in Education (IJERE) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v9i3.20525

Abstract

The purpose of this research is to develop the client-counselor interaction monitoring model of the logo counseling website. The model attempts to help counselors in guiding and helping the students (clients) to achieve healthy self-esteem. Machine learning techniques integrated into the model will ensure that the recommendations can be available for counselors and supervisors in the near real-time environment. For the first implementation, a chatbot application is developed and tested with excellent responses from the students. Further research is needed to implement the complete specifications of the interaction monitoring model on the logo counseling website.
Innovative Model for Logo Counseling Website Jacob Daan Engel; Ventje Jeremias Lewi Engel; Evangs Mailoa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 5: October 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i5.7593

Abstract

Logo counseling is a counseling model specifically to treat low spiritual self-esteem problem affecting the life and attitudes of college students. Nevertheless, the problem relies on distance, time, and psychological burdens which preventing face-to-face logo counseling. By reviewing past research regarding online counseling practices, the innovative model for online logo counseling was designed and then demonstrated via logo counseling website. There are four objectives and thirty-five specifications defined in the model. The result showed that logo counseling website is helpful and easy to understand. Further research needed to address the issue of security and confidentiality, furthermore future research needed to examine the integration of text-mining and multimedia analysis techniques to better helping counselors in online counseling intervention.