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All Journal JOIV : International Journal on Informatics Visualization
Khairuddin, Mohammad Adib
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Computer Science & IT

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Journal : joiv international journal on informatics visualization

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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.
A Novel Network Optimization Framework Based on Software-Defined Networking (SDN) and Deep Learning (DL) Approach Osman, Muhammad Fendi; Mohd Isa, Mohd Rizal; Khairuddin, Mohammad Adib; Mohd Shukran, Mohd ‘Afizi; Mat Razali, Noor Afiza
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2169

Abstract

Access to networks and the Internet has multiplied, and data traffic is growing exponentially and quickly. High network utilization, along with varied traffic types in the network, poses a considerable challenge and impact on the ICT Infrastructure, particularly affecting the performance and responsiveness of real-time application users who will experience slowness and poor performance. Conventional/traditional Quality of Service (QoS) mechanisms, designed to ensure reliable and efficient data transmission, are increasingly insufficient due to their static nature and inability to adapt to the dynamic demands of modern networks.  As such, this study introduces a Novel Network Optimization Framework leveraging the combined strengths of Software-Defined Networking (SDN) and Deep Learning (DL) to dynamically manage multiple QoS of network devices in enterprise and campus network environments. The proposed system is a dynamic QoS that utilizes SDN's global monitoring and centralized management control capabilities to programmatically control network devices, ensuring that sensitive traffic is allocated with appropriate bandwidth and minimized latency. Concurrently, DL algorithms enhance the framework's decision-making process by proposing an accurate preferred configuration for the best adequate bandwidth for sensitive traffic transmission. This integration facilitates real-time adjustments to network conditions and improves overall network performance by ensuring high-priority applications receive the bandwidth they require without manual/human intervention. By providing a dynamic, intelligent solution to QoS management, this framework represents a significant step forward in developing more adaptable, resilient, and efficient networks capable of supporting the demands of contemporary and future digital ecosystems.
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.
Co-Authors Kamarudin, Nur Diyana Maskat, Kamaruzaman Mat Razali, Noor Afiza Mohd Isa, Mohd Rizal Mohd Nazri Ismail, Mohd Nazri Mohd Shukran, Mohd Afizi Mohd Shukran, Mohd ‘Afizi Osman, Muhammad Fendi