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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Applied Data Sciences
Fauziyyah, Anni Karimatul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analyzing Image Malware with OSINTs after Steganography using Symmetric Key Algorithm Fauziyyah, Anni Karimatul; Adrian, Ronald; Alam, Sahirul
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12266

Abstract

Steganography is the practice of hiding a message or information within another file, such as an image (Singh & Singla, 2022). OSINT (Open Source Intelligence) involves using publicly available information for intelligence gathering purposes. In this research, the asymmetric key algorithm will be applied to the steganography method, using 10 images with different sizes and dimensions. Images tested for steganography are in tiff, gif, png, jpg, and bmp format. A combination of steganography and OSINT could involve analyzing and decoding images found on publicly available platforms, such as social media, to uncover hidden messages. On the other hand, steganography within OSINT can also be used to protect sensitive information from prying eyes. Overall, the combination of Symmetric Key Algorithm steganography and OSINT can be a powerful tool for both intelligence gathering and secure communication. Here in this work, malware is developed, and using that malware the victim’s machine is exploited. Later, an analysis is done via freely available OSINTs to find out which is the best OSINT that gives the best results. OSINTs have been very helpful in identifying whether the URLs and files are malicious or not. But how binding an image with the malware makes it difficult for OSINTs to identify they are malicious or not is being analyzed in this work. The analysis shows that the best OSINT is VirusTotal which has a greater number of engines that could detect the malware whereas others don’t have a variety of engines to detect the malware. Also, when it comes to malware afore binding it with an image is easier to detect whereas for an OSINT it was difficult to identify and detect the malware after binding with an image
Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders Dzakiyullah, Nur Rachman; Pramuntadi, Andri; Fauziyyah, Anni Karimatul
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.16

Abstract

The use of credit cards for online purchases has increased dramatically and led to an explosion in credit card fraud. Credit card companies need to be able to identify fraudulent credit card transactions so that customers are not charged for items they do not buy. In this study, we will use semi-supervised learning and combine it with AutoEncoders to identify fraudulent credit card transactions. In this paper, we will implement the use of T-SNE to visualize fraud and non-fraud transactions, then improve the visualization using autoencoders. Classification report proved that it is possible to achieve very acceptable precision using semi-supervised classification to detect credit card fraud.
Co-Authors Alam, Sahirul Dzakiyullah, Nur Rachman Pramuntadi, Andri Ronald Adrian, Ronald