A Secure Open-Source Intelligence Framework For Cyberbullying Investigation

Cyberbullying has become a pervasive issue based on the rise of cell phones and internet usage affecting individuals worldwide. This paper proposes an open-source intelligence pipeline using data from Twitter to track keywords relevant to cyberbullying in social media to build dashboards for law enforcement agents. We discuss the prevalence of cyberbullying on social media, factors that compel individuals to indulge in cyberbullying, and the legal implications of cyberbullying in different countries also highlight the lack of direction, resources, training, and support that law enforcement officers face in investigating cyberbullying cases. The proposed interventions for cyberbullying involve collective efforts from various stakeholders, including parents, law enforcement, social media platforms, educational institutions, educators, and researchers. Our research provides a framework for cyberbullying and provides a comprehensive view of the digital landscape for investigators to track and identify cyberbullies, their tactics, and patterns. An OSINT dashboard with real-time monitoring empowers law enforcement to swiftly take action, protect victims, and make significant strides toward creating a safer online environment.Comment: 8 pages, 5 figure, under revie

AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities

Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in different regions across the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems to detect and respond to traffic accidents spontaneously. Integrating the proposed framework with emergency services will harness the power of traffic cameras and machine learning algorithms to create an efficient solution for responding to traffic accidents and reducing human errors. Advanced intelligence technologies, such as the proposed accident detection systems in smart cities, will improve traffic management and traffic accident severity. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution to enhance the safety and efficiency of transportation systems.Comment: 8,

Evaluation of User Perception on Biometric Fingerprint System

Biometric systems involve security assurance to make our system highly secured and robust. Nowadays, biometric technology has been fixed into new systems with the aim of enforcing strong privacy and security. Several innovative system have been introduced, and most of them have biometrics installed to protect military bases, banking machines, and other sophisticated systems, such as online tracking systems. Businesses can now focus on their core functions and feel confident about their data security. Despite the benefits and enhancements in security that biometrics offer, there are also some vulnerabilities. This study aimed to investigate the biometric vulnerabilities in a healthcare facility and propose possible countermeasures for biometric system vulnerabilities

Threat Actors’ Tenacity to Disrupt: Examination of Major Cybersecurity Incidents

The exponential growth in the interconnectedness of people and devices, as well as the upward trend in cyberspace usage will continue to lead to a greater reliance on the internet. Most people’s daily activities are dependent on their ability to navigate the internet to access and manage information. There are usually real risks associated with managing or accessing information, and these risks when exploited by threat actors, often lead to cybersecurity incidents. It is a common knowledge that a major cybersecurity incident is likely to result in significant financial losses, legal liability, privacy violations, reputational damage, sensitive data compromises, as well as national security implications. Threat actors usually employ various attack techniques to cause these incidents. After we identified the major cybersecurity incident report that is consolidated by the Center for Strategic & International Studies (CSIS) from which we derived the data of about the 803 major incidents that we analyzed, we then verified its (CSIS) credibility, non-partisan, global outreach and cybersecurity attack coverage by cross-referencing it with Data Breach Investigation Report (DBIR). We also through the lens of the Global Cybersecurity Index (GCI) ensured that this study is conducted within the context of cybersecurity principles. In reference to these attack techniques employed by threat actors, we conducted an exploratory investigation of 803 major cybersecurity incidents that were reported over the last decade. From a group of 244 of these major security incidents that happened and were reported between 2005 and 2021, this study reports that malware attack techniques were employed by threat actors to cause 48 percent of them and phishing attack techniques account for 19.7 percent of them. As many sources have confirmed the fact that major incidents will always happen, we echo the importance of readiness of organizations to conduct cybersecurity incident triage and or thorough investigation as necessary. Given the relevance of the guidelines outlined in the National Institute of Standards and Technology (NIST) incident response framework, we also recommend that organizations should adopt it or at least embrace similar guidelines as best as possible

A review on action recognition for accident detection in smart city transportation systems

Abstract Accident detection and public traffic safety is a crucial aspect of safe and better community. Monitoring traffic flow in smart cities using different surveillance cameras plays a crucial role in recognizing accidents and alerting first responders. In computer vision tasks, utilizing action recognition (AR) has contributed to high-precision video surveillance, medical imaging, and digital signal processing applications. This paper presents an intensive review focusing on action recognition in accident detection and autonomous transportation systems for smart city. This paper focused on AR systems that use diverse sources of traffic video, such as static surveillance cameras on traffic intersections, highway monitoring cameras, drone cameras, and dash-cams. Through this review, we identified the primary techniques, taxonomies, and algorithms used in AR for autonomous transportation and accident detection. We also examined datasets utilized in the AR tasks, identifying the primary sources of datasets and features of the datasets. This paper provides a potential research direction to develop and integrate accident detection systems for autonomous cars and public traffic safety systems by alerting emergency personnel and law enforcement in the event of road traffic accidents to minimize the human error in accident reporting and provide a spontaneous response to victims