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A peer-reviewed article of this preprint also exists.
This version is not peer-reviewed
Submitted:
21 June 2023
Posted:
23 June 2023
You are already at the latest version
Ref. No. | Paper title | Problem statement of the paper | The solution proposed by the paper |
---|---|---|---|
[12] | IoTDots: A Digital Forensics Framework for Smart Environments | The amount of data collected by IoT devices and sensors is immense and contains valuable forensic evidence. This data can help identify and prevent unauthorised access within smart environments. | A new framework known as IoTDots is designed to help protect the data collected by various smart devices and applications. It features two main components: the IoTDots Analyzer and the IoTDots modifier. The former scans the source code of the applications and detects forensic information. The latter automatically inserts tracking logs and reports the results. |
[13] | Methodology for the Automated Metadata-Based Classification of Incriminating Digital Forensic Artefacts | One of the most discussed challenges in DFI is the growing volume of data. Since the majority of file artefacts on seized devices are usually irrelevant to the investigation, manually retrieving suspicious files relevant to the investigation is very difficult. | To reduce the amount of manual analysis required in DFI, this research proposed a methodology for the automatic prioritising of suspicious file artefacts. Rather than providing the final analysis results, this methodology aims to predict and recommend the artefacts that are likely to be suspicious. A supervised machine learning approach is used, which makes use of previously processed case results. |
[14] | Intelligent Methods in Digital Forensics: State of the Art | One of the main issues with DF is that a massive amount of data must be analysed for digital evidence. The primary goal of this work is to improve this difficult forensic process by employing intelligent methods for analysing digital evidence. | The ability of computers to learn a specific task from data is known as "intelligent methods", which also includes data mining, machine learning, soft computing, and traditional artificial intelligence. This term is commonly used to express ways to automate problem solving in DF. In support of DF, two main intelligent approaches are utilised, namely rule-based and anomaly-based. |
[15] | The Truth Shall Set Thee Free: Enabling Practical Forensic Capabilities in Smart Environments | The interaction of devices, users, and apps in smart environments creates a large amount of data. Such data contains valuable forensic information about smart environment events and activities. However, current smart platforms lack any digital forensic capability for identifying, tracing, storing, and analysing data generated in these environments. | "VERITAS", a novel and practical DF capability for smart environments, is introduced in this paper. The Collector and Analyzer are the two main components of VERITAS. The Collector employs mechanisms to automatically collect forensically relevant data from the smart environment. The Analyzer then uses a First Order Markov Chain model to extract valuable and usable forensic evidence from the collected data for the purposes of a forensic investigation. |
[16] | Internet of Things Forensic Data Analysis using Machine Learning to Identify Roots of Data Scavenging | To detect attacks and violations in an IoT environment, intensive data analysis and computational intelligence are required. On such platforms, advanced computer systems based on machine learning and smart computing are used to identify enemies. | To discover and declare the presence of adversaries, DF necessitates intensive data analysis, such as retrieving and confirming system logs, blockchain information evaluation, and so on. The blockchain-assisted shared audit framework was proposed in this paper to analyse DF data in an IoT environment. It was created to identify the source and cause of data scavenging attacks in virtualised resources. It uses blockchain technology to manage access logs and controls. Using logistic regression ML and cross-validation, access log data is examined for the consistency of adversary event detection. |
[5] | Quantifying the Need for Supervised Machine Learning in Conducting Live Forensic Analysis of Emergent Configurations (ECO) in IoT Environments | In an IoT system, particularly in the case of emergent configurations (ECOs), data might be dynamic, making it difficult to classify information during live forensics. In this sense, live forensics refers to a forensic investigation that is done in near real-time. | A conceptual framework based on supervised machine learning techniques was proposed in this paper. One of the advantages of using supervised ML techniques in live forensics is the ability of such techniques to predict possible events based on past occurrences. In addition, an automated feature identification was used to prevent redundancy throughout the feature selection and elimination. |
[4] | SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation | The number of cases needing DF competence and the volume of data to be processed have overburdened digital forensic investigators. Many large data challenges are considered as having been solved by artificial intelligence. Automated evidence processing based on artificial intelligence techniques holds considerable potential for speeding up the digital forensic analysis process while improving case-processing capacity. | In DFI, automation uses ML techniques for classification. ML techniques can obtain important information for investigations more efficiently by exploiting existing digital evidence-processing knowledge. Also, digital evidence triage was developed for the prompt detection, processing and interpretation of digital evidence. Currently, with AI techniques, the investigator determines the priority of device gathering and processing at a crime scene. |
[17] | Digital Forensics Supported by Machine Learning for the Detection of Online Sexual Predatory Chats | With the growth of cybercrime that targets minors, chat logs can be examined to detect and report harmful behaviour to law authorities. This can make a significant difference in protecting youngsters on social media platforms from being abused by cyber predators. Since DFI is done primarily by hand, the enormous volume and variety of data cause DF investigators to have a tough assignment. | To enable the automatic finding of hazardous talks in chat logs, the approach suggested in this research leverages a DF process model backed by ML methodologies. ML has previously been used successfully in the field of text analysis to detect online sexual predatory talks. |
[18] | IoT-based Smart Environment Using Intelligent Intrusion Detection System | One of the most fundamental characteristics of any smart device in an IoT network is its ability to acquire a bigger set of data than has been produced and then send the obtained data to the destination/ receiver server through the internet. Thus, IoT-based networks are particularly vulnerable to simple or sophisticated assaults, which must be discovered early in the data transmission process in order to protect the network against these hostile attacks. | The primary purpose of this study was to develop and build an intelligent intrusion detection system utilising machine learning models so that assaults in the IoT network may be discovered. The approach was designed to account for both regular and malicious attacks on data created in an IoT smart environment. |
[19] | Forensic Analysis on Internet of Things (IoT) Device Using Machine-to-Machine (M2M) Framework | The adaptability of IoT devices raises the probability of continual attacks on them. Due to the low processing power and memory of IoT devices, security researchers have found it challenging to preserve records of diverse attacks performed on these devices during a DFI. | An intelligent forensic analysis mechanism is proposed for the automatic detection of attacks on IoT devices based on the machine-to-machine framework. However, the proposed mechanism combines several ML techniques and different forensic analysis tools to detect different types of attacks. Furthermore, by providing a third-party logging server, the problem of evidence gathering has been overcome. To assess the effect and type of attacks and violations, forensic analysis is done on logs utilising a forensic server. |
[30] | Digital Forensic Tools: A Literature Review | The growth of IoT devices that produce a large amount of forensic data presents the main challenge of IoT forensics. | A smart fridge was selected as an IoT device to examine and investigate. The dataset was examined using two ML algorithms, Bayes net, and decision stump. Each algorithm represents a distinct idea, a stump tree, which is a simple version of the decision tree ML technique. The Bayes net is useful for estimating the likelihood of numerous recognised causes, one of which was the occurrence of an event. The validation results indicate that Bayes net algorithm is more accurate than the decision stump tree. |
[31] | Feature-Sniffer: Enabling IoT Forensics in OpenWrt based Wi-Fi Access Points | IoT forensics and smart environments with their recognised challenges create a great opportunity to develop new forensic tools to make the task of forensic investigators easier, which can be used for acquiring, preserving, and also analysing such forensic data. | A user-friendly tool was proposed for smart devices that support WiFi and used in smart environment scenarios to allow forensic investigators, network administrators, and data scientists access to various features of network traffic with simple steps. The proposed tool allows network traffic features to be computed in real-time on any WiFi access point running the OpenWrt firmware, avoiding the time-consuming tasks of dumping network traffic and implementing the needed procedures to analyse the captured traffic. |
Reference No. | Used ML technique | Readiness processes | Initialisation processes | Acquisitive processes | Investigative processes | Concurrent processes |
---|---|---|---|---|---|---|
[12] | Markov Chain model | X | ||||
[17] | Logistic regression | X | ||||
[5] | Supervised machine learning | X | X | |||
[13] | Supervised machine learning | X | ||||
[14] | Unsupervised identification | X | X | |||
[15] | Markov Chain model | X | ||||
[16] | Logical regression | X | X | |||
[18] | Markov chain model | X | ||||
[19] | Decision tree algorithm | X |
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