Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
Social media networks are a vital platform for the virtual community, connecting billions of people for mutual interaction. However, hackers are aggressively exploiting these platforms for malicious intentions. Despite the implementation of preventive measures, hacker activity has surged, leading to the need for a social media intrusion detection system. Online social networks have provided users with conveniences but also pose significant threats to their security and privacy. Users' attempts to adjust their privacy settings are less than their efforts to implement other security measures. A significant proportion of individuals using social media platforms have limited technical expertise, resulting in less apprehension about the privacy implications of their personal content. To address privacy concerns, a comprehensive set of well- defined policies should be established, including robust passwords, periodic password changes, caution when sharing personal information, the importance of antivirus software, and proprietary software use. Machine learning algorithms can be employed to examine user sentiment, identify deceptive news, and combat child trafficking. Researchers are currently investigating the incorporation of improving cyber security of social media platforms by using artificial intelligence, focusing particularly on adversarial machine learning. The growing popularity of AI for Good project and the emphasis on Fair AI and Bias in AI highlight the need for further research on how these fields can be used in relation to the social media. This research provides a thorough analysis of the most recent advancements in social media security and dependability, presenting a groundbreaking approach to enhance security and dependability. Organizations must safeguard information broadcasted on social media due to frequent security breaches, which can hinder economic growth....
This paper introduces Certis, a powerful framework that addresses the challenges of cloud asset tracking, management, and threat detection in modern cybersecurity landscapes. It enhances asset identification and anomaly detection through SSL certificate parsing, cloud service provider integration, and advanced fingerprinting techniques like JARM at the application layer. Current work will focus on cross-layer malicious behavior identification to further enhance its capabilities, including minimizing false positives through AI-based learning techniques. Certis promises to offer a powerful solution for organizations seeking proactive cybersecurity defenses in the face of evolving threats....
The adoption of Docker containers has revolutionized software deployment by providing a lightweight and efficient way to isolate applications in data centers. However, securing these containers, especially when handling sensitive data, poses significant challenges. Traditional Linux Security Modules (LSMs) such as SELinux and AppArmor have limitations in providing finegrained access control to files within containers. This paper presents a novel approach using eBPF (extended Berkeley Packet Filter) to implement a LSM that focuses on file-oriented access control within Docker containers. The module allows the specification of policies that determine which programs can access sensitive files, providing enhanced security without relying solely on the host operating system’s major LSM....
Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks....
As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solution’s technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis....
Loading....