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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advanced Science Computing and Engineering
Cik Feresa Mohd Foozy, Cik Feresa
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A Generic Review of Messenger Application: Wechat And Whatsapp Anak Apon, Ewiena Bivie; Mohd Foozy, Cik Feresa; Shamala, Palaniappan
International Journal of Advanced Science Computing and Engineering Vol. 1 No. 1 (2019)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (620.842 KB) | DOI: 10.62527/ijasce.1.1.2

Abstract

Over the Internet today, communication environment is more easy and interesting to the citizen. There are much interesting in modern technology when they provided a good substrate for creating large-scale data sharing, content distribution, and application-level multicast applications. The important objective of this article is getting information about comparison of two distributed system in messenger application. Messenger application is known as a tool communication that can get any information. The distributed system is one of the independent and single coherent systems it was built an application that will be informed that section is being fixed or replaces to serve more applications or users. The distributed system for this article about the comparison of two distributed system in messenger application for WeChat and Whatsapp, it is also known as the famous application for this generations nowadays.
A Generic Review of Web Technology: DJango and Flask Idris, Nuruldelmia; Mohd Foozy, Cik Feresa; Shamala, Palaniappan
International Journal of Advanced Science Computing and Engineering Vol. 2 No. 1 (2020)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (419.434 KB) | DOI: 10.62527/ijasce.2.1.29

Abstract

The research is discussing the relevance of web technology based on its history, current development, future working process, threats, or attacks that are common in web technology, and its solutions to encounter the threats. Nowadays, web services have become one of the most vital components in peoples' daily lifestyle. People are talking how the advancement in the technology leads the world now. The evolution of network has triggered the demands especially in internet connection as everyone around the world are depending on the connection to do their activities especially for communication. It is required to have high speed internet and bandwidth to prevent poor connection. Besides that, due to advance development in computing products and network, people enables to catch up with the current trends which spark the existence of web development that are used to gather information.
Hybrid N-gram-based framework for payload distributed denial of service detection and classification Maslan, Andi; Mohd Foozy, Cik Feresa; Bin Mohamad, Kamaruddin Malik; Hamid, Abdul; Fitriawan, Dedy; Hasugian, Joni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4763-4774

Abstract

There are three primary approaches to DDoS detection: anomaly-based, pattern-based, and heuristic-based. The heuristic-based method integrates both anomaly- and pattern-based techniques. However, existing DDoS detection systems face challenges in performing HTTP payload-level analysis, mainly due to high false positive rates and insufficient granularity in current datasets. To address this, the study introduces a novel heuristic approach based on a hybrid N-Gram model. This hybrid combines two components: CSDPayload+N-Gram and CSPayload+N-Gram. CSDPayload represents the gap (measured via Chi-Square Distance) between a given payload and normal traffic payloads, while CSPayload reflects the similarity (measured via Cosine Similarity) between them. These metrics form a new feature set evaluated using three datasets: CIC2019, MIB2016, and H2N-Payload. The methodology begins with packet extraction and conversion of TCP/IP traffic—specifically HTTP traffic—into hexadecimal payloads. N-Gram analysis (from 1-Gram to 6-Gram) is then applied to these payloads. For each N-Gram, frequency counts are computed, followed by calculations of Chi-Square Distance (CSD), Cosine Similarity (CS), and Pearson’s Chi-Square test to classify payloads as either benign or malicious. Subsequently, feature selection is performed using weight correlation, and the resulting features are fed into three machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network. Experimental results demonstrate high detection accuracy, particularly in the 4-Gram feature category: Neural Network achieves 99.65%, KNN 95.14%, and SVM 99.73% accuracy on average.
Co-Authors Abdul Hamid Anak Apon, Ewiena Bivie Bin Mohamad, Kamaruddin Malik Dedy Fitriawan Hasugian, Joni Idris, Nuruldelmia Maslan, Andi Shamala, Palaniappan