https://woasjournals.com/index.php/ijitas/issue/feed International Journal of Information Technology and Applied Sciences (IJITAS) 2021-12-16T11:58:58+00:00 IJITAS Journal contact@woasjournals.com Open Journal Systems <p><strong>International Journal of Information Technology and Applied Sciences (IJITAS) -ISSN 2709-2208 (Online)-</strong> is a peer-reviewed International Journal that currently publishes 4 issues annually. IJITAS is published by the <a href="http://www.woasjournals.com/" target="_blank" rel="noopener">World Organization of Applied Sciences (WOAS)</a>. IJITAS journal publishes technical papers, as well as review articles and surveys, describing recent research and development work that covers all areas of computer science, information systems, and computer / electrical engineering.</p> <p align="justify"><em><strong>Cross Reference</strong></em></p> <p align="justify"><strong>International Journal of Information Technology and Applied Sciences (IJITAS)</strong> is a member of the <strong>CrossRef. </strong>The DOI prefix allotted for IJITAS is <a href="https://doi.org/10.52502/ijitas"><strong>10.52502/ijitas</strong></a></p> https://woasjournals.com/index.php/ijitas/article/view/172 Malware Detection Using a Machine-Learning Based Approach 2021-10-20T18:53:00+00:00 Safa Rkhouya ijitas@woasjournals.com Khalid Chougdali ijitas@woasjournals.com <p>The purpose of this research work is to study the usage of machine learning in detecting malware. This paper presents a versatile framework, in which a dataset of more than 130000 files has been analyzed, to train and test four machine learning algorithms: Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting; The performance of each algorithm in malware classification, has been studied based on the: Accuracy, execution time, rate of false positives and false negatives, and area under the Receiver Operating Characteristic curve.</p> 2021-10-25T00:00:00+00:00 Copyright (c) 2021 International Journal of Information Technology and Applied Sciences (IJITAS) https://woasjournals.com/index.php/ijitas/article/view/195 A Web-based Andhra University Spatial Information System (AUSIS) and a Building Information Extraction Model using WebGIS & Image Recognition Technique 2021-12-05T05:14:46+00:00 Boddepalli Navjoth navjothbn@gmail.com <p>A university campus is an intricate infrastructure. Especially new students, who are thereon for the first time, have a tough time orienting themselves and finding places. The campus of Andhra University occupies more than 422 acres (170.7 hectares). The campus has many different buildings. Every year, thousands of new students join the university. These students either take a campus commuter or walk around to get familiar with the campus compound. Visitors to Andhra University might have a hard time searching for a particular location on the campus. Every day, uncountable numbers of students, staff, and visitors move around the campus compound to perform tasks by walking, cycling, driving, or riding campus commuters. Even if there are maps at various points on the campus premises, users do not have continuous help to reach their destination. On these static maps, they can try to figure out a way to get to their target, but as soon as they start walking in the target direction, they have no help anymore. The main objective of this study is to develop a Spatial Information System for Andhra University (a Progressive Web App). Which provides several features like a voice-enabled optimal navigation solution, shows nearby places within campus premises, and a geo-tagged university (Geo-tagging of all entities within campus premises). To make the web application more operative, the application is appended with more features. For instance, a map shows statistical data with pie charts visualization (statistical data like monthly attendance), machine learning's image recognition model for extracting the building information from the digital or captured images.</p> 2021-12-13T00:00:00+00:00 Copyright (c) 2021 International Journal of Information Technology and Applied Sciences (IJITAS) https://woasjournals.com/index.php/ijitas/article/view/201 Attacks on Graphical Password: A Study on Defense Mechanisms and Limitations 2021-12-16T11:58:58+00:00 Indrani Roy indraniroy389@yahoo.com Ajmerry Hossain ajmerry_hossain@yahoo.com Sarker T. Ahmed Rumee ijitas@woasjournals.com <p>User authentication is mostly reliant on password-based based verification. Users generally used text-based passwords, which are user-friendly but often predictable and vulnerable to some common attacks. To overcome these shortcomings, graphical authentication methods have emerged. Here, users choose a sequence of images as passwords. Though such methods help users to better remember their passwords, they too suffer from attacks seen in the case of textual passwords. This paper presents a comprehensive summary of the vulnerabilities state of the art graphical password schemes against the following well-known attacks -&nbsp; Dictionary, Guessing, Brute force, Shoulder surfing, Spyware, and Social engineering. We believe the findings of this study can help researchers design more secure graphical password schemes making them more usable and a realistic replacement for text-based passwords.</p> <p>&nbsp;</p> 2021-12-25T00:00:00+00:00 Copyright (c) 2021 International Journal of Information Technology and Applied Sciences (IJITAS)