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All Journal JOIV : International Journal on Informatics Visualization
Maskat, Kamaruzaman
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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.
Enhancing The Server-Side Internet Proxy Detection Technique in Network Infrastructure Based on Apriori Algorithm of Machine Learning Technique Maskat, Kamaruzaman; Mohd Isa, Mohd Rizal; Khairuddin, Mohammad Adib; Kamarudin, Nur Diyana; Ismail, Mohd Nazri
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3410

Abstract

The widespread use of proxy servers has introduced challenges in managing and securing internet connections, particularly in detecting non-transparent proxies that obscure the originating IP address. Proxy servers, while beneficial for bandwidth management and anonymity, can be exploited for malicious purposes, such as bypassing geo-restrictions or concealing cyberattacks. This study aims to address the gap in identifying proxy usage by providing an organized review of existing detection techniques and proposing a hybrid server-side detection framework. The objectives of the research include identifying and comparing proxy detection methods, developing a hybrid approach using machine learning, and evaluating its effectiveness in enhancing network security. The methodology involves collecting primary data through controlled environments simulating direct and proxy-based connections. A machine learning model, based on the Apriori algorithm, is employed to analyze network traffic patterns and identify characteristics indicative of proxy usage. Attributes such as IP addresses, port numbers, and round-trip times are used to train the model. The proposed framework is tested for its robustness, accuracy, and speed against existing detection methods. The results demonstrate the feasibility of the hybrid approach in improving the detection of non-transparent proxies, particularly those not easily identifiable using conventional techniques. The findings have significant implications for securing server-side infrastructure, aiding in cyber threat mitigation, and enforcing organizational policies. Future research can expand on this framework by testing it against broader proxy types and integrating real-world data to enhance its reliability and scope. This study contributes to advancing cybersecurity practices by addressing a critical challenge in proxy detection.
The Impact of Online Learning on NDUM Students During COVID-19 Iskandar, Sari Nashikim Radin; Adib, Mohammad Khairuddin; Isa, Mohd Rizal Mohd; Ali, Sharifah Aishah Syed; Shukran, Mohd Afizi Mohd; Maskat, Kamaruzaman
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1238

Abstract

One of the impacts of the COVID-19 outbreak was the closure of numerous education facilities, including schools and universities. Due to the closing of these institutions, the method used for teaching and learning changed from physical face-to-face lecturing to online contactless learning. This helps curb the spread of infections while ensuring that teaching and learning continue as usually as possible. However, questions arise not only about the effectiveness of online learning but also about the impact of online learning on education stakeholders, namely students and educators. This study aims to assess the effects of the lockdown during COVID-19 on National Defense University of Malaysia (NDUM) students. A link pointing to a custom-built questionnaire was forwarded to students through email and WhatsApp. At the end of the survey period, 445 students responded to the questionnaire. The simple percentage distribution was employed to evaluate the student's learning status and their expectations. Based on the analysis, during the lockdown, students faced issues involving technical, time management, social interactions, and surrounding (home-related) issues. In contrast, during the lockdown, students were also keen to learn new technological skills and favorable towards the ability to replay lectures and class materials. These valuable insights on the impact of online learning on students are essential due to the advancement of technology in education, not only in Malaysia but in other nations as well.
Co-Authors Adib, Mohammad Khairuddin Ali, Sharifah Aishah Syed Isa, Mohd Rizal Mohd Iskandar, Sari Nashikim Radin Kamarudin, Nur Diyana Khairuddin, Mohammad Adib Mohd Isa, Mohd Rizal Mohd Nazri Ismail, Mohd Nazri Mohd Shukran, Mohd Afizi Shukran, Mohd Afizi Mohd