Nowadays, computer network is very important because of the many advantages it has. However, it is also vulnerable to a lot of threats from attackers and the most common of such attack is the Distributed Denial of Service (DDoS) attack. This paper presents an overview of the existing detection and defense algorithms to mitigate four types of DDoS attacks and they are the UDP flood, TCP SYN flood, Ping of Death and Smurf attack. A detection and defense algorithm will be proposed in this paper and it will be evaluated using the existing Intrusion Detection and Prevention tool to determine whether it is the best algorithm to mitigate the DDoS attacks on a network environment. The proposed algorithm will be measured in terms of false positive rates and detection accuracy. Index Terms-DDoS, detection and defense algorithm, UDP flood, TCP SYN flood, ping of death and Smurf attack.
In today's digital landscape, the identification of malicious software has become a crucial undertaking. The evergrowing volume of malware threats renders conventional signature-based methods insufficient in shielding against novel and intricate attacks. Consequently, machine learning strategies have surfaced as a viable means of detecting malware. The following research report focuses on the implementation of classification machine learning methods for detecting malware. The study assesses the effectiveness of several algorithms, including Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Logistic Regression, through an examination of a publicly accessible dataset featuring both benign files and malware. Additionally, the influence of diverse feature sets and preprocessing techniques on the classifiers' performance is explored. The outcomes of the investigation exhibit that machine learning methods can capably identify malware, attaining elevated precision levels and decreasing false positive rates. Decision Tree and Random Forest display superior performance compared to other algorithms with 100.00% accuracy. Furthermore, it is observed that feature selection and dimensionality reduction techniques can notably enhance classifier effectiveness while mitigating computational complexity. Overall, this research underscores the potential of machine learning approaches for detecting malware and offers valuable guidance for the development of successful malware detection systems.
In this era of big data explosion, humans widely use the movie recommendation system as an information tool. There are two common issues found in the machine learning movie recommendation system that is still undeniable: first, cold start, and second, data sparsity. To minimize the problems, a research study is conducted to find a decision-making algorithm to solve the complex start problem in a movie recommendation system with precise parameters. It involves the implementation of the proposed demographics filtering technique with the k-means clustering method. The research findings present the effects of demographic filtering for movie recommendations. Demographic filtering can group users into clusters based on gender, age group, and occupation. The clusters distribution representative group based on the top 100 results of the experiment. The user with the least distance to the cluster center is chosen as the usual group in that cluster. Three clusters were experimented: Cluster 0, Cluster 1, and Cluster 2. Cluster 0 has a representative group of male, college, or graduate students aged 25 to 34. Cluster 1 has a representative group of females, executive or managerial, aged 25 to 34. Cluster 2 has a representative group of males, sales or marketing aged 35 to 44. It is shown that user from different collection has various preferred movie genre. The preferred movie genre in Cluster 0 is action, adventure, comedy, drama, and war. Cluster 1 has preferred comedy, crime, drama, horror, romance, and sci-fi movie genres. Cluster 2 has chosen action, comedy, drama, film-noir, mystery, and thriller movie genres. This research has contributed to the demographic filtering studies as an alternative solution for future technical development work.
The electronic voting system (E-voting System) is a web-based application that enables voters to record safe and confidential votes electronically. This research aims to develop an E-Voting System by exploiting the ‘reCAPTCHA’ security component for a private international college. The Waterfall Model is deemed the most suitable to be used after data was gathered prior to developing the system and it is also considered simple to be employed. The functional testing was conducted with thirty students and results revealed that 63% of the respondents are in agreement that the traditional voting system should be supplanted with electronic voting system, especially to cut down the time in the voting process. Ultimately, the system developed demonstrated that it efficient, reliable and displayed transparency in the E-voting System.
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