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All Journal JOIV : International Journal on Informatics Visualization
Mohd Shukran, Mohd Afizi
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Computer Science & IT

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Detecting Distributed Denial-of-Service (DDoS) Attacks Through the Log Consolidation Processing (LCP) Framework Khairuddin, Mohammad Adib; Mohd Isa, Mohd Rizal; Mohd Shukran, Mohd Afizi; Ismail, Mohd Nazri; Maskat, Kamaruzaman
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2184

Abstract

One major problem commonly faced by organizations is a network attack especially if the network is vulnerable due to poor security policies. Network security is vital in protecting not only the infrastructure but most importantly, the data that moves around the network and is stored within the organization. Ensuring a secure network requires a complex combination of hardware including both network and security devices, specialized applications such as web filtering and log management, and a group of well-trained network administrators and highly skilled analysts.  This paper aims to present an alternative to the current log management solution. A hindrance to the current log management solution is the difficulty in amalgamating and correlating a vast number of logs with different formats and variables. This paper uses a novel framework called Log Consolidation Processing (LCP) based on the System Information Event Management (SIEM) technology, to monitor the behavior and the fitness of a network. LCP provides a flexible and complete solution to collect, correlate, and analyze logs from multiple devices as well as applications. An experiment testing the effectiveness of LCP in detecting DDoS attacks in a campus network environment was conducted, demonstrating a highly successful rate of detection. Besides threat detection and avoidance through log monitoring and analysis, other benefits of implementing the LCP framework are also included. This paper concludes by mentioning suggested enhancements for the LCP framework.
Examining the Impact Factors Influencing Higher Education Institution (HEI) Students’ Security Behaviours in Cyberspace Environment Syed Zulkiplee, Syed Muzammer; Mohd Shukran, Mohd Afizi; Isa, Mohd Rizal Mohd; Adib Khairuddin, Mohammad; Wahab, Norshahriah; Hidayat, Hendra
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2296

Abstract

The Internet’s increasing connectivity through devices and systems, particularly with the Internet of Things (IoT), has expanded the threat landscape, making cybersecurity a constantly evolving field. Phishing is a common and emerging cyber-attack that attempts to deceive individuals and persuade them to disclose sensitive information, such as passwords, financial information, or personal data. Researchers have studied phishing extensively in recent years to understand its mechanisms, strategies, and potential solutions. This research examines essential factors that affect how online users behave regarding security in cyberspace, focusing on phishing attacks through the Health Belief Model (HBM). Understanding what influences users' security behaviors is crucial for building strong defenses. A survey was sent to students via WhatsApp and email, with 252 participants. The results were analyzed using quantitative methods. Principal Component Analysis (PCA) revealed that perceived barriers, self-efficacy, and privacy concerns were the main determinants of students' security behaviors. Students were particularly concerned about the misuse of their personal information. Despite varying levels of formal cybersecurity education, most students demonstrated confidence in configuring web browser security settings. The findings underscore the importance of tailored educational interventions and user-friendly security tools. Future research could explore additional security issues such as spyware, adware, and spam attacks. Additionally, leveraging machine learning and deep learning algorithms offers promising avenues for enhancing phishing detection and mitigation strategies. Furthermore, this study contributes to understanding cybersecurity behaviors, providing valuable insights for policymakers, educators, and developers to foster a safer online environment.
Co-Authors Adib Khairuddin, Mohammad Hendra Hidayat Isa, Mohd Rizal Mohd Khairuddin, Mohammad Adib Maskat, Kamaruzaman Mohd Isa, Mohd Rizal Mohd Nazri Ismail, Mohd Nazri Syed Zulkiplee, Syed Muzammer Wahab, Norshahriah