Cybercrimes Investigation and Intrusion Detection in Internet of Things Based on Data Science Methods

Hemdan, Ezz El‐Din; Manjaiah, D. H.

doi:10.1007/978-3-319-70688-7_2

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…AI and ML, as previously stated by Hemdan and Manjaiah (2017), can aid in digital forensic analysis by identifying anomalies in network traffic, detecting malware, classifying files based on their content, and recognising objects and people in images and videos. Another crucial application of AI and ML in digital forensics is their ability to enhance investigation consistency and identify new criminal trends.…”

Section: Research Motivationmentioning

confidence: 99%

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

Dunsin,

Ghanem,

Ouazzane

et al. 2023

Preprint

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Section: Research Motivationmentioning

confidence: 99%

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

Dunsin,

Ghanem,

Ouazzane

et al. 2023

Preprint

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“…In the context of the collection process, this stage is a process of extracting evidence based on various platforms, sources, and types of data evidence. Many studies reveal that in the IoT forensic investigation phase, the process of identifying possible data sources [32], [35], [18], determines the physical location of evidence [52] duplication of digital evidence for validated processes [53], and assumes the integrity and validity of digital evidence [32], [15], [52], [54].…”

Section: B Rq2: Core and Essential Phase Of Iot Forensicsmentioning

confidence: 99%

“…In the examination process, two main objectives must be achieved during the IoT forensic investigation process: identifying and validating procedures for finding and analyzing sensitive data [53], [7], extraction of hidden data, identification of complex data and pattern matching [9], [52]. However, some of the additional tasks that are specifically performed during the IoT investigation process include determining how data was collected, where and by whom, identifying visible digital evidence, analyzing the ability levels of suspects, and transforming data to a size and type that is more accessible for analysis [9], [52].…”

Section: B Rq2: Core and Essential Phase Of Iot Forensicsmentioning

confidence: 99%

An Overview Diversity Framework for Internet of Things (IoT) Forensic Investigation

Rizal

Selamat

Mas'ud

2023

JOIV : Int. J. Inform. Visualization

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The increasing utilization of IoT technology in various fields creates opportunities and risks for investigating all cybercrimes. At the same time, many research studies have concentrated on security and forensic investigations to collect digital evidence on IoT devices. However, until now, the IoT platform has not fully evolved to adjust the tools, methods, and procedures of IoT forensic investigations. The main reasons for investigators are the characteristics and infrastructure of IoT devices. For example, device number variations, heterogeneity, distribution of protocols used, data duplication, complexity, limited memory, etc. As a result, resulting is a tough challenge to identify, collect, examine, analyze, and present potential IoT digital evidence for forensic investigative processes effectively and efficiently. Indeed, there is not fully used and adapted international standard for the perfect IoT forensic investigation framework. In the research method, a literature review has been carried out by producing previous research studies that have contributed to further facing challenges. To keep the quality of the literature review, research questions (RQ) were conducted for all studies related to the IoT forensic investigation framework between 2015-2022. This research results highlight and provides a comprehensive overview of the twenty current IoT forensic investigation framework that has been proposed. Then, a summary or contribution is presented focusing on the latest research, grouping the forensic phases, and evaluating essential frameworks in the IoT forensic investigation process to obtain digital evidence. Finally, open research issues are presented for further research in developing IoT forensic investigative framework.

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“…Hemdan and Manjaiah [19] provide an overview of the principles of digital forensics, intrusion detection and suitable types of data science methods in the context of Internet of Things. The authors list prediction, classification, clustering and relation rule techniques as appropriate techniques for intrusion detection.…”

Section: Data Analysis In the Cloudmentioning

confidence: 99%

V2C: A Trust-Based Vehicle to Cloud Anomaly Detection Framework for Automotive Systems

Rosenstatter

Olovsson

Almgren

2021

Proceedings of the 16th International Conference on Availability, Reliability and Security

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Vehicles have become connected in many ways. They communicate with the cloud and will use Vehicle-to-Everything (V2X) communication to exchange warning messages and perform cooperative actions such as platooning. Vehicles have already been attacked and will become even more attractive targets due to their increasing connectivity, the amount of data they produce and their importance to our society. It is therefore crucial to provide cyber security measures to prevent and limit the impact of attacks.As it is problematic for a vehicle to reliably assess its own state when it is compromised, we investigate how vehicle trust can be used to identify compromised vehicles and how fleet-wide attacks can be detected at an early stage using cloud data. In our proposed V2C Anomaly Detection framework, peer vehicles assess each other based on their perceived behavior in traffic and V2X-enabled interactions, and upload these assessments to the cloud for analysis. This framework consists of four modules. For each module we define functional demands, interfaces and evaluate solutions proposed in literature allowing manufacturers and fleet owners to choose appropriate techniques. We detail attack scenarios where this type of framework is particularly useful in detecting and identifying potential attacks and failing software and hardware. Furthermore, we describe what basic vehicle data the cloud analysis can be based upon. CCS CONCEPTS• Security and privacy → Intrusion detection systems; • Computer systems organization → Embedded systems.

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Cybercrimes Investigation and Intrusion Detection in Internet of Things Based on Data Science Methods

Cited by 13 publications

References 15 publications

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

An Overview Diversity Framework for Internet of Things (IoT) Forensic Investigation

V2C: A Trust-Based Vehicle to Cloud Anomaly Detection Framework for Automotive Systems

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