International Journal of Information Technology & Computer Engineering (IJITC) ISSN : 2455-5290

Bridging the Gap: Aligning Cybersecurity Education with Industry Needs

Oluwatosin Islamiyat Yusuf — 2024-04-01

As technology has revolutionized every facet of the world and the rate of cyber attacks is continuously increasing, the need for cybersecurity professionals has also been on the rise. However, there has been little match between the industry needs and the skills exhibited by the students of cybersecurity. This paper identifies some issues as well as gaps associated with the mismatch between the industry needs and the education pipeline designed to fulfil these needs. The paper also provides recommendations and guides to follow to tailor the cybersecurity curriculum design.

Library Management System

Divyansh Patidar — 2024-04-01

This library management system leverages modern technologies to streamline library operations in educational institutions. Built with MongoDB, JavaScript, HTML, CSS, React, and Node.js, it offers a user-friendly platform for managing resources, users, and borrowing processes.

Pro-Coder: A Visual Coding Platform with Gif’s Interaction

Mr. Garugu Suresh Kumar — 2024-04-01

This innovative project introduces an online platform designed to elevate practical programming skills through meticulously curated challenges across diverse programming languages. Participants engage in mini-projects that replicate real-world scenarios, enabling them to construct a tangible portfolio essential for bolstering their resumes. With challenges tailored to accommodate individuals of all skill levels, the platform fosters continuous growth and improvement. More than just honing technical proficiency, it emphasizes critical thinking, problem-solving, and creativity, essential traits in the programming landscape. The resulting portfolio serves as a compelling asset during job interviews, effectively bridging the gap between theoretical knowledge and its real-world application. By addressing the increasing demand for practical coding skills, this initiative empowers users to excel in a competitive job market and thrive in various industries. Through hands-on experience and iterative learning, individuals gain the confidence and expertise needed to navigate complex coding challenges and make meaningful contributions to the ever-evolving field of technology.

Application of Deep Reinforcement Learning (DRL) for Malware Detection

Mangadevi Atti — 2024-04-02

Malware poses a significant threat to computer systems and networks, necessitating advanced detection methods to safeguard against potential cyber-attacks. This paper investigates the application of Deep Reinforcement Learning (DRL) for malware detection, leveraging its ability to learn complex patterns and behaviours from raw data. The study employs a DRL framework to train an agent to identify malicious software based on dynamic features extracted from executable files. A comprehensive evaluation is conducted using a diverse dataset, encompassing various types of malware samples. The experimental results demonstrate the effectiveness of the proposed DRL based approach in accurately detecting malware, achieving competitive performance compared to traditional methods and state-of-the-art techniques. Additionally, the paper discusses the interpretability and scalability of the model, along with potential challenges and future research directions in applying DRL to cybersecurity.

Application of Federated Learning for Smart Agriculture System

Aiswarya Dwarampudi — 2024-04-12

Federated Learning (FL) presents a ground breaking approach to addressing data privacy concerns while harnessing the power of machine learning in the agricultural sector. This paper explores the application of FL for smart agriculture, examining its potential benefits and implications. FL enables collaborative model training across decentralized data sources, allowing farmers to contribute their data without compromising privacy. In smart agriculture, FL facilitates the development of customized machine learning models for tasks such as crop yield prediction, disease detection, resource optimization, and livestock management. By leveraging data from diverse geographical regions, FL models can provide localized recommendations tailored to specific farming conditions. This paper discusses the significance of FL in enabling data-driven decision-making, promoting sustainable agricultural practices, and fostering collaboration among stakeholders. Furthermore, it explores the challenges and considerations associated with implementing FL in the agricultural sector, including data heterogeneity, communication constraints, and model aggregation. Despite these challenges, FL offers immense potential for revolutionizing agriculture by empowering farmers with actionable insights while safeguarding their data privacy.