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Social media applications have been ubiquitous in modern society, and their usage has grown exponentially over the years. With the widespread adoption of these platforms, social media has evolved into a significant origin of digital evidence in the domain of digital forensics. The increasing utilization of social media has caused an increase in the number of studies focusing on artifact (digital remnants of data) recovery from these platforms. As a result, we aim to present a comprehensive survey of the existing literature from the past 15 years on artifact recovery from social media applications in digital forensics. We analyze various approaches and techniques employed for artifact recovery, structuring our review on well-defined analysis focus categories, which are memory, disk, and network. By scrutinizing the available literature, we determine the trends and commonalities in existing research and further identify gaps in existing literature and areas of opportunity for future research in this field. The survey is expected to provide a valuable resource for academicians, digital forensics professionals, and researchers by enhancing their comprehension of the current state of the art in artifact recovery from social media applications. Additionally, it highlights the need for continued research to keep up with social media’s constantly evolving nature and its consequent impact on digital forensics.

Current vehicles include electronic features that provide ease and convenience to drivers. These electronic features or nodes rely on in-vehicle communication protocols to ensure functionality. One of the most-widely adopted in-vehicle protocols on the market today is the Controller Area Network, popularly referred to as the CAN bus. The CAN bus is utilized in various modern, sophisticated vehicles. However, as the sophistication levels of vehicles continue to increase, we now see a high rise in attacks against them. These attacks range from simple to more-complex variants, which could have detrimental effects when carried out successfully. Therefore, there is a need to carry out an assessment of the security vulnerabilities that could be exploited within the CAN bus. In this research, we conducted a security vulnerability analysis on the CAN bus protocol by proposing an attack scenario on a CAN bus simulation that exploits the arbitration feature extensively. This feature determines which message is sent via the bus in the event that two or more nodes attempt to send a message at the same time. It achieves this by prioritizing messages with lower identifiers. Our analysis revealed that an attacker can spoof a message ID to gain high priority, continuously injecting messages with the spoofed ID. As a result, this prevents the transmission of legitimate messages, impacting the vehicle’s operations. We identified significant risks in the CAN protocol, including spoofing, injection, and Denial of Service. Furthermore, we examined the latency of the CAN-enabled system under attack, finding that the compromised node (the attacker’s device) consistently achieved the lowest latency due to message arbitration. This demonstrates the potential for an attacker to take control of the bus, injecting messages without contention, thereby disrupting the normal operations of the vehicle, which could potentially compromise safety.

As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects various smart devices (such as smart mobiles, intelligent refrigerators, smartwatches, smart fire alarms, smart door locks, and many more) allowing them to communicate with each other and exchange data seamlessly. We now use IoT technology to carry out our daily activities, for example, transportation. In particular, the field of smart transportation has intrigued researchers due to its potential to revolutionize the way we move people and goods. IoT provides drivers in a smart city with many benefits, including traffic management, improved logistics, efficient parking systems, and enhanced safety measures. Smart transportation is the integration of all these benefits into applications for transportation systems. However, as a way of further improving the benefits provided by smart transportation, other technologies have been explored, such as machine learning, big data, and distributed ledgers. Some examples of their application are the optimization of routes, parking, street lighting, accident prevention, detection of abnormal traffic conditions, and maintenance of roads. In this paper, we aim to provide a detailed understanding of the developments in the applications mentioned earlier and examine current researches that base their applications on these sectors. We aim to conduct a self-contained review of the different technologies used in smart transportation today and their respective challenges. Our methodology encompassed identifying and screening articles on smart transportation technologies and its applications. To identify articles addressing our topic of review, we searched for articles in the four significant databases: IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Consequently, we examined the communication mechanisms, architectures, and frameworks that enable these smart transportation applications and systems. We also explored the communication protocols enabling smart transportation, including Wi-Fi, Bluetooth, and cellular networks, and how they contribute to seamless data exchange. We delved into the different architectures and frameworks used in smart transportation, including cloud computing, edge computing, and fog computing. Lastly, we outlined current challenges in the smart transportation field and suggested potential future research directions. We will examine data privacy and security issues, network scalability, and interoperability between different IoT devices.