Garuda - Garba Rujukan Digital

Course Attendance Mobile for Monitoring Student’s Activity Saragih, Rijois I. E.; Simanjuntak, Thandy
International Journal of Information System and Innovative Technology Vol. 1 No. 2 (2022): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/935fvf54

The learning and teaching process is an important activity to achieve learning objectives. However, in practice it often happens that the process does not run as it should. One important component in the learning and teaching process is attendance. The discipline of student attendance shows that they have a good interest in their studies and needs to be recorded so that attendance data is stored and presented properly. Manual attendance recording is a problem of effectiveness and efficiency. This research proposes a system for monitoring student attendance using mobile applications. The results of the study show that it is easier and faster for students to take attendance and easier for teachers to recap student attendance and also become the basis for decision making when giving final grades.

Anticipating Cybersecurity on Artificial Intelligence Simanjuntak, Thandy
International Journal of Information System and Innovative Technology Vol. 2 No. 1 (2023): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/273wdm25

As artificial intelligence (AI) continues to revolutionize various sectors, the intersection of AI and cybersecurity has emerged as a critical frontier. This paper delves into the realm of "Anticipating Cybersecurity on Artificial Intelligence," exploring the challenges, opportunities, and strategies inherent in safeguarding AI systems against an evolving landscape of cyber threats. With AI playing an increasingly pivotal role in decision-making, automation, and data analysis, the security of AI systems becomes paramount to ensuring the integrity and trustworthiness of their outcomes. This paper investigates various dimensions of anticipating cybersecurity concerns in AI, including identifying potential vulnerabilities, developing proactive defense mechanisms, and fostering collaboration between AI and cybersecurity experts. Through an in-depth analysis of recent case studies and trends, we highlight the importance of pre-emptive measures to thwart adversarial attacks, data poisoning, and model manipulations. Moreover, we explore the role of explainable AI in enhancing cybersecurity transparency and the potential for leveraging AI techniques to enhance intrusion detection and threat mitigation. By delving into these interconnected aspects, this paper not only underscores the urgency of addressing cybersecurity within AI but also emphasizes the necessity of anticipating future threats to ensure the continued success and trustworthiness of AI technologies. In sum, this study contributes to the discourse surrounding AI cybersecurity, shedding light on strategies to anticipate and counteract threats, and fostering a holistic approach to secure the AI-driven future.

User-Centric Security Education and Awareness for Web Users Simanjuntak, Thandy
International Journal of Information System and Innovative Technology Vol. 2 No. 2 (2023): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/t97ch871

In an era marked by ubiquitous digital interactions, the security of web users becomes paramount in safeguarding sensitive information and mitigating the risks associated with cyber threats. This research addresses the problem of the persistently high incidence of online security breaches, often attributed to inadequate user awareness and education. The study aims to investigate the effectiveness of user-centric security education and awareness programs in enhancing the online safety practices of individuals, ensuring they are equipped with the necessary knowledge and skills to protect their personal information and navigate the digital landscape securely. The research problem centers around the need to understand how various user demographics respond to different forms of security education, the retention of knowledge over time, and the potential influence of corporate culture on individuals' security practices. Source: This paper examines the role and value of information security awareness efforts in defending against social engineering attacks. It categorizes the different social engineering threats and tactics used in targeting employees and explores approaches to defend against such attacks. The research aims to determine the effectiveness of user-centric security education and awareness programs in mitigating social engineering attacks by categorizing different threats and tactics used to target employees. To address the persistent issue of online security breaches and the lack of user awareness and education, this research aims to investigate the effectiveness of user-centric security education and awareness programs in enhancing the online safety practices of individuals. The research problem focuses on understanding how different user demographics respond to various forms of security education, the retention of knowledge over time, and the potential impact of corporate culture on individuals' security practices.

Enhancing Cyber-Physical System Security: A Review of Detection and Mitigation Techniques Simanjuntak, Thandy
International Journal of Information System and Innovative Technology Vol. 3 No. 2 (2024): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/zny3qg45

The increasing sophistication of cyber threats has driven the demand for a well-trained cybersecurity workforce. Cyber Ranges have emerged as essential platforms for hands-on training, skill development, and cybersecurity research. This paper presents a comprehensive review of Cyber Ranges, focusing on their role in cybersecurity workforce development across different regions and industries. We examine global practices, existing frameworks, and standardization efforts that shape the implementation and effectiveness of Cyber Ranges. Furthermore, we analyze the challenges in designing scalable, realistic, and adaptive training environments, considering advancements in artificial intelligence (AI), gamification, and cloud-based simulations. By evaluating best practices and identifying gaps in current methodologies, this review provides actionable insights for policymakers, educators, and industry stakeholders. The findings underscore the necessity of harmonizing Cyber Range capabilities with real-world cybersecurity demands, ensuring a resilient and highly skilled workforce ready to combat emerging threats.

Emerging Cybersecurity Threats in the Era of AI and IoT: A Risk Assessment Framework Using Machine Learning for Proactive Threat Mitigation Simanjuntak, Thandy
International Journal of Information System and Innovative Technology Vol. 3 No. 1 (2024): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/y3bfp253

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has revolutionized various industries, enabling automation, real-time decision-making, and enhanced connectivity. However, these advancements have also introduced new cybersecurity threats, increasing the vulnerability of interconnected systems. The proliferation of IoT devices and AI-driven applications has expanded the attack surface, making them prime targets for cyber adversaries. Traditional security mechanisms, which often rely on signature-based threat detection, struggle to address sophisticated attacks such as adversarial AI manipulations, IoT botnet infiltrations, and real-time data breaches. This study examines emerging cybersecurity risks in AI and IoT environments, emphasizing the limitations of existing security frameworks in detecting and mitigating evolving threats. One of the key challenges is the inability of conventional methods to adapt to novel attack patterns in dynamic and complex networks. To address this issue, we introduce a machine learning-based risk assessment framework designed for proactive threat mitigation. This framework leverages anomaly detection, behavioral analytics, and predictive threat modeling to identify potential cybersecurity risks in real time. By integrating adaptive learning algorithms and continuous monitoring, the proposed system enhances resilience against AI-driven cyberattacks and IoT-based vulnerabilities. The findings highlight the critical need for AI-driven cybersecurity solutions capable of evolving alongside emerging threats, ensuring the safety and reliability of interconnected digital ecosystems.

Title

Found 5 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 5 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 5 Documents
Search