Drone Disrupted Denial of Service Attack (3DOS): Towards an Incident Response and Forensic Analysis of Remotely Piloted Aerial Systems (RPASs)

Salamh, Fahad E.; Karabiyik, Umit; Rogers, Marcus; Al-Hazemi, Fawaz

doi:10.1109/iwcmc.2019.8766538

Cited by 18 publications

(17 citation statements)

References 6 publications

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“…Last but not least, a recent work by Salamh et al [156] suggests an enhanced model of Drone Forensics, as well as an Incident Response plan (DFIR), covering storage mechanisms used by three different types of drones: DJI Phanthom 4 Pro; Typhoon H; and DJI Mavic Pro. The scholars in [156], pointed out that when it comes to digital forensics investigation, the metadata inside the media files represent valuable evidence material. Furthermore, the work discusses drone disrupted denial of service attacks (3DOS), along with some possible anti-forensics techniques that could be implemented to alter digital evidence associated with drones.…”

Section: ) Blockchain-based Investigation Frameworkmentioning

confidence: 99%

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

Stoyanova

Nikoloudakis

Panagiotakis

et al. 2020

IEEE Commun. Surv. Tutorials

578

238

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Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multitenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users' right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.

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Section: ) Blockchain-based Investigation Frameworkmentioning

confidence: 99%

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

Stoyanova

Nikoloudakis

Panagiotakis

et al. 2020

IEEE Commun. Surv. Tutorials

578

238

View full text Add to dashboard Cite

show abstract

“…In [14], machine learning algorithms are utilized as well as taxonomy-ladder of IoT-based-security systems, which include identification, wireless networking, data collection is briefly studied. In [15], DoS attacks for UAVs are proposed through GPS tracking investigation of data using log files [15]. Wireless vision (Wi-Vi) sensors are put in service for self-controlled flying vehicles, which can be used to rescue or detection of intruders even if there is very thick wall of security [16].…”

Section: Literature Surveymentioning

confidence: 99%

Securing Against DoS/DDoS Attacks in Internet of Flying Things using Experience-based Deep Learning Algorithm

Khan¹,

Abdollahi

Khan

et al. 2021

Preprint

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Due to the limited computational resources of small unmanned aerial vehicles (UAVs), the Internet of flying things (IoFT) is vulnerable to cybersecurity attacks, particularly Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. In addition, the transfer of reliable information from source UAV to destination UAV is another big challenge in IoFT networks. Therefore, this article aims to address the security deficiency by proposing an experience-based deep learning algorithm to cater to the DoS, D-DoS and a special kind of threat covering ping-of-death attacks. The proposed scheme uses the notion of the intrusion detection system (IDS). In addition, for reliable communication, a nature-based control routing algorithm AntHocNet is investigated with other contemporary protocols. The proposed approach is implemented in a smart city environment as a case study. The result authenticates the superiority of the proposed schemes in terms of security and QoS requirement from its counterparts.

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“…Researchers in [19] also claim that the process of UAV forensic investigations is unclear due to the complexity of data flow and UAS architecture. While there has been a fair amount of recent work in the area of UAV forensic investigations [20,21], challenges related to drone forensics are still ongoing. One major factor is system architecture, related software components, and data flow mechanisms.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges

Salamh

Karabiyik

Rogers

et al. 2021

Drones

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The raising accessibility of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, is rapidly increasing. Recent studies have discussed challenges that may come in tow with the growing use of this technology. These studies note that in-depth examination is required, especially when addressing challenges that carry a high volume of software data between sensors, actuators, and control commands. This work underlines static and live digital evidence traceability challenges to further enhance the UAV incident response plan. To study the live UAV forensic traceability issues, we apply the `purple-teaming’ exercise on small UAVs while conducting UAV forensic examination to determine technical challenges related to data integrity and repeatability. In addition, this research highlights current static technical challenges that could pose more challenges in justifying the discovered digital evidence. Additionally, this study discusses potential drone anti-forensic techniques and their association with the type of use, environment, attack vector, and level of expertise. To this end, we propose the UAV Kill Chain and categorize the impact and complexity of all highlighted challenges based on the conducted examination and the presented scientific contribution in this work. To the best of our knowledge, there has not been any contribution that incorporates `Purple-Teaming’ tactics to evaluate UAV-related research in cybersecurity and digital forensics. This work also proposes a categorization model that classifies the discovered UAV static and live digital evidence challenges based on their complexity and impact levels

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Drone Disrupted Denial of Service Attack (3DOS): Towards an Incident Response and Forensic Analysis of Remotely Piloted Aerial Systems (RPASs)

Cited by 18 publications

References 6 publications

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

Securing Against DoS/DDoS Attacks in Internet of Flying Things using Experience-based Deep Learning Algorithm

A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges

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