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All Journal JOIV : International Journal on Informatics Visualization Innovation in Research of Informatics (INNOVATICS) International Journal of Informatics and Computing
Selamat, Siti Rahayu
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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An Algorithm for Color-Based Password Authentication to Increase Security Level Selamat, Siti Rahayu; Cai, Soung Young; Hassan, Nor Hafeizah; Yusof, Robiah
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10396

Abstract

Security level in authentication is essential to decrease the possibility of an account being guessed. Several authentication methods are widely used nowadays, covering digital aspects such as passwords, challenge-response, public and private key / digital certificates, and physical elements such as fingerprints, iris, or retina scanning. This paper aims to focus on solving the problem of the password. This textual authentication consists of many vulnerabilities open to attacks like eavesdropping, dictionary attack, and brute force attack by increasing the level of complexity in the authentication algorithm. In this paper, we proposed a new color-based password authentication algorithm to solve the vulnerabilities in textual authentication. The color-based password authentication algorithm consists of three main processes: color selection, hexadecimal password encryption, and password verification. This research contributes to a new color-based authentication by increasing the complexity of the verification process that can solve the vulnerabilities of textual authentication and harden the level of security in the authentication layer. This color-based authentication algorithm could fully replace textual authentication in the future and is worth using in sensitive data domains such as medical and health or banking institutions.
Advanced Phishing Attack Detection Through Network Forensic Methods and Incident Response Planning Based on Machine Learning Selamat, Siti Rahayu; Rizal, Randi; Nursihab, Cucu; Amien, Nashihun
JICO: International Journal of Informatics and Computing Vol. 1 No. 1 (2025): May 2025
Publisher : IAICO

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Abstract

The widespread use of smartphones has led to an increase in cybercrimes, particularly phishing attacks. Phishing attacks are commonly propagated through email, WhatsApp groups, and other communication channels. The stolen data is then used to commit further crimes, exploiting the victims' personal information. This study addresses the detection of phishing attacks using network forensic methods and incident response planning. Unlike previous approaches that relied solely on Incident Response Plans (IRPs) and Incident Handling methods to react to phishing attacks, this research emphasizes proactive detection. By employing network forensics, suspicious websites can be identified and differentiated from legitimate ones, enabling early detection and prevention of phishing attacks. The results demonstrate that network forensics can significantly enhance the ability to detect phishing sites before they can harm users. In our experiments, we analyzed a dataset of 10,000 websites, identifying 95% of phishing sites with a false positive rate of only 2%. Utilizing the Random Forest machine learning algorithm, we achieved high performance metrics with an accuracy of 96.5%, precision of 97.1%, recall of 95.8%, and an F1-score of 96.4%. This proactive approach not only mitigates the risk of phishing but also provides a robust framework for incident response, ensuring that potential threats are identified and neutralized promptly.
Impact of Data Balancing and Feature Selection on Machine Learning-based Network Intrusion Detection Barkah, Azhari Shouni; Selamat, Siti Rahayu; Abidin, Zaheera Zainal; Wahyudi, Rizki
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1041

Abstract

Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning. Therefore, the machine learning model is more effective at recognizing the majority classes than the minority classes. Naturally, imbalanced data, such as disease data and data networking, has emerged in real life. DDOS is one of the network intrusions found to happen more often than R2L. There is an imbalance in the composition of network attacks in Intrusion Detection System (IDS) public datasets such as NSL-KDD and UNSW-NB15. Besides, researchers propose many techniques to transform it into balanced data by duplicating the minority class and producing synthetic data. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) algorithms duplicate the data and construct synthetic data for the minority classes. Meanwhile, machine learning algorithms can capture the labeled data's pattern by considering the input features. Unfortunately, not all the input features have an equal impact on the output (predicted class or value). Some features are interrelated and misleading. Therefore, the important features should be selected to produce a good model. In this research, we implement the recursive feature elimination (RFE) technique to select important features from the available dataset. According to the experiment, SMOTE provides a better synthetic dataset than ADASYN for the UNSW-B15 dataset with a high level of imbalance. RFE feature selection slightly reduces the model's accuracy but improves the training speed. Then, the Decision Tree classifier consistently achieves a better recognition rate than Random Forest and KNN.
An Overview Diversity Framework for Internet of Things (IoT) Forensic Investigation Rizal, Randi; Selamat, Siti Rahayu; Mas’ud, Mohd. Zaki
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1520

Abstract

The increasing utilization of IoT technology in various fields creates opportunities and risks for investigating all cybercrimes. At the same time, many research studies have concentrated on security and forensic investigations to collect digital evidence on IoT devices. However, until now, the IoT platform has not fully evolved to adjust the tools, methods, and procedures of IoT forensic investigations. The main reasons for investigators are the characteristics and infrastructure of IoT devices. For example, device number variations, heterogeneity, distribution of protocols used, data duplication, complexity, limited memory, etc. As a result, resulting is a tough challenge to identify, collect, examine, analyze, and present potential IoT digital evidence for forensic investigative processes effectively and efficiently. Indeed, there is not fully used and adapted international standard for the perfect IoT forensic investigation framework. In the research method, a literature review has been carried out by producing previous research studies that have contributed to further facing challenges. To keep the quality of the literature review, research questions (RQ) were conducted for all studies related to the IoT forensic investigation framework between 2015-2022. This research results highlight and provides a comprehensive overview of the twenty current IoT forensic investigation framework that has been proposed. Then, a summary or contribution is presented focusing on the latest research, grouping the forensic phases, and evaluating essential frameworks in the IoT forensic investigation process to obtain digital evidence. Finally, open research issues are presented for further research in developing IoT forensic investigative framework.
Co-Authors Abidin, Zaheera Zainal Amien, Nashihun Azhari Shouni Barkah Cai, Soung Young Hassan, Nor Hafeizah Mas’ud, Mohd. Zaki Nursihab, Cucu Rizal, Randi Rizki Wahyudi Yusof, Robiah