Visible to the public Biblio

Filters: Keyword is information forensics  [Clear All Filters]
2021-04-08
Boato, G., Dang-Nguyen, D., Natale, F. G. B. De.  2020.  Morphological Filter Detector for Image Forensics Applications. IEEE Access. 8:13549—13560.
Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.
Zhang, J., Liao, Y., Zhu, X., Wang, H., Ding, J..  2020.  A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics. IEEE Signal Processing Letters. 27:276—280.
This letter presents a novel median filtering forensics approach, based on a convolutional neural network (CNN) with an adaptive filtering layer (AFL), which is built in the discrete cosine transform (DCT) domain. Using the proposed AFL, the CNN can determine the main frequency range closely related with the operational traces. Then, to automatically learn the multi-scale manipulation features, a multi-scale convolutional block is developed, exploring a new multi-scale feature fusion strategy based on the maxout function. The resultant features are further processed by a convolutional stream with pooling and batch normalization operations, and finally fed into the classification layer with the Softmax function. Experimental results show that our proposed approach is able to accurately detect the median filtering manipulation and outperforms the state-of-the-art schemes, especially in the scenarios of low image resolution and serious compression loss.
Mayer, O., Stamm, M. C..  2020.  Forensic Similarity for Digital Images. IEEE Transactions on Information Forensics and Security. 15:1331—1346.
In this paper, we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g., training samples, of a forensic trace is not required to make a forensic similarity decision on it in the future. To do this, we propose a two-part deep-learning system composed of a convolutional neural network-based feature extractor and a three-layer neural network, called the similarity network. This system maps the pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated the system accuracy of determining whether two image patches were captured by the same or different camera model and manipulated by the same or a different editing operation and the same or a different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces and importantly show efficacy on “unknown” forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification.
Rhee, K. H..  2020.  Composition of Visual Feature Vector Pattern for Deep Learning in Image Forensics. IEEE Access. 8:188970—188980.

In image forensics, to determine whether the image is impurely transformed, it extracts and examines the features included in the suspicious image. In general, the features extracted for the detection of forgery images are based on numerical values, so it is somewhat unreasonable to use in the CNN structure for image classification. In this paper, the extraction method of a feature vector is using a least-squares solution. Treat a suspicious image like a matrix and its solution to be coefficients as the feature vector. Get two solutions from two images of the original and its median filter residual (MFR). Subsequently, the two features were formed into a visualized pattern and then fed into CNN deep learning to classify the various transformed images. A new structure of the CNN net layer was also designed by hybrid with the inception module and the residual block to classify visualized feature vector patterns. The performance of the proposed image forensics detection (IFD) scheme was measured with the seven transformed types of image: average filtered (window size: 3 × 3), gaussian filtered (window size: 3 × 3), JPEG compressed (quality factor: 90, 70), median filtered (window size: 3 × 3, 5 × 5), and unaltered. The visualized patterns are fed into the image input layer of the designed CNN hybrid model. Throughout the experiment, the accuracy of median filtering detection was 98% over. Also, the area under the curve (AUC) by sensitivity (TP: true positive rate) and 1-specificity (FP: false positive rate) results of the proposed IFD scheme approached to `1' on the designed CNN hybrid model. Experimental results show high efficiency and performance to classify the various transformed images. Therefore, the grade evaluation of the proposed scheme is “Excellent (A)”.

Guerrini, F., Dalai, M., Leonardi, R..  2020.  Minimal Information Exchange for Secure Image Hash-Based Geometric Transformations Estimation. IEEE Transactions on Information Forensics and Security. 15:3482—3496.
Signal processing applications dealing with secure transmission are enjoying increasing attention lately. This paper provides some theoretical insights as well as a practical solution for transmitting a hash of an image to a central server to be compared with a reference image. The proposed solution employs a rigid image registration technique viewed in a distributed source coding perspective. In essence, it embodies a phase encoding framework to let the decoder estimate the transformation parameters using a very modest amount of information about the original image. The problem is first cast in an ideal setting and then it is solved in a realistic scenario, giving more prominence to low computational complexity in both the transmitter and receiver, minimal hash size, and hash security. Satisfactory experimental results are reported on a standard images set.
Zheng, Y., Cao, Y., Chang, C..  2020.  A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification. IEEE Transactions on Information Forensics and Security. 15:620—634.
With the increasing prevalent of digital devices and their abuse for digital content creation, forgeries of digital images and video footage are more rampant than ever. Digital forensics is challenged into seeking advanced technologies for forgery content detection and acquisition device identification. Unfortunately, existing solutions that address image tampering problems fail to identify the device that produces the images or footage while techniques that can identify the camera is incapable of locating the tampered content of its captured images. In this paper, a new perceptual data-device hash is proposed to locate maliciously tampered image regions and identify the source camera of the received image data as a non-repudiable attestation in digital forensics. The presented image may have been either tampered or gone through benign content preserving geometric transforms or image processing operations. The proposed image hash is generated by projecting the invariant image features into a physical unclonable function (PUF)-defined Bernoulli random space. The tamper-resistant random PUF response is unique for each camera and can only be generated upon triggered by a challenge, which is provided by the image acquisition timestamp. The proposed hash is evaluated on the modified CASIA database and CMOS image sensor-based PUF simulated using 180 nm TSMC technology. It achieves a high tamper detection rate of 95.42% with the regions of tampered content successfully located, a good authentication performance of above 98.5% against standard content-preserving manipulations, and 96.25% and 90.42%, respectively, for the more challenging geometric transformations of rotation (0 360°) and scaling (scale factor in each dimension: 0.5). It is demonstrated to be able to identify the source camera with 100% accuracy and is secure against attacks on PUF.
Al-Dhaqm, A., Razak, S. A., Ikuesan, R. A., Kebande, V. R., Siddique, K..  2020.  A Review of Mobile Forensic Investigation Process Models. IEEE Access. 8:173359—173375.
Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.
Verdoliva, L..  2020.  Media Forensics and DeepFakes: An Overview. IEEE Journal of Selected Topics in Signal Processing. 14:910—932.
With the rapid progress in recent years, techniques that generate and manipulate multimedia content can now provide a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, and video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos. These can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Therefore, there is an urgent need for automated tools capable of detecting false multimedia content and avoiding the spread of dangerous false information. This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos. Special emphasis will be placed on the emerging phenomenon of deepfakes, fake media created through deep learning tools, and on modern data-driven forensic methods to fight them. The analysis will help highlight the limits of current forensic tools, the most relevant issues, the upcoming challenges, and suggest future directions for research.
Guo, T., Zhou, R., Tian, C..  2020.  On the Information Leakage in Private Information Retrieval Systems. IEEE Transactions on Information Forensics and Security. 15:2999—3012.
We consider information leakage to the user in private information retrieval (PIR) systems. Information leakage can be measured in terms of individual message leakage or total leakage. Individual message leakage, or simply individual leakage, is defined as the amount of information that the user can obtain on any individual message that is not being requested, and the total leakage is defined as the amount of information that the user can obtain about all the other messages except the one being requested. In this work, we characterize the tradeoff between the minimum download cost and the individual leakage, and that for the total leakage, respectively. Coding schemes are proposed to achieve these optimal tradeoffs, which are also shown to be optimal in terms of the message size. We further characterize the optimal tradeoff between the minimum amount of common randomness and the total leakage. Moreover, we show that under individual leakage, common randomness is in fact unnecessary when there are more than two messages.
Al-Dhaqm, A., Razak, S. A., Dampier, D. A., Choo, K. R., Siddique, K., Ikuesan, R. A., Alqarni, A., Kebande, V. R..  2020.  Categorization and Organization of Database Forensic Investigation Processes. IEEE Access. 8:112846—112858.
Database forensic investigation (DBFI) is an important area of research within digital forensics. It's importance is growing as digital data becomes more extensive and commonplace. The challenges associated with DBFI are numerous, and one of the challenges is the lack of a harmonized DBFI process for investigators to follow. In this paper, therefore, we conduct a survey of existing literature with the hope of understanding the body of work already accomplished. Furthermore, we build on the existing literature to present a harmonized DBFI process using design science research methodology. This harmonized DBFI process has been developed based on three key categories (i.e. planning, preparation and pre-response, acquisition and preservation, and analysis and reconstruction). Furthermore, the DBFI has been designed to avoid confusion or ambiguity, as well as providing practitioners with a systematic method of performing DBFI with a higher degree of certainty.
Yang, Z., Sun, Q., Zhang, Y., Zhu, L., Ji, W..  2020.  Inference of Suspicious Co-Visitation and Co-Rating Behaviors and Abnormality Forensics for Recommender Systems. IEEE Transactions on Information Forensics and Security. 15:2766—2781.
The pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.
2020-06-03
Cedillo, Priscila, Camacho, Jessica, Campos, Karina, Bermeo, Alexandra.  2019.  A Forensics Activity Logger to Extract User Activity from Mobile Devices. 2019 Sixth International Conference on eDemocracy eGovernment (ICEDEG). :286—290.

Nowadays, mobile devices have become one of the most popular instruments used by a person on its regular life, mainly due to the importance of their applications. In that context, mobile devices store user's personal information and even more data, becoming a personal tracker for daily activities that provides important information about the user. Derived from this gathering of information, many tools are available to use on mobile devices, with the restrain that each tool only provides isolated information about a specific application or activity. Therefore, the present work proposes a tool that allows investigators to obtain a complete report and timeline of the activities that were performed on the device. This report incorporates the information provided by many sources into a unique set of data. Also, by means of an example, it is presented the operation of the solution, which shows the feasibility in the use of this tool and shows the way in which investigators have to apply the tool.

Reeva, Patel, Siddhesh, Dhuri, Preet, Gada, Pratik, Shah, Jain, Nilakshi.  2019.  Digital Forensics Capability Analyzer: A tool to check forensic capability. 2019 International Conference on Nascent Technologies in Engineering (ICNTE). :1—7.

Digital forensics is process of identifying, preserving, analyzing and preserving digital evidence. Due to increasing cybercrimes now a days, it is important for all organizations to have a well-managed digital forensics cell. So to overcome this, we propose a framework called digital forensics capability analyser. [1]The main advantage of developing digital analyzer is cost minimization. This tool will provide fundamental information for setting up a digital forensic cell and will also offer services like online sessions. [2] [3]It will help organizations by providing them with a perfect solution according to their requirements to start a digital forensic cell in their respective lnstitution.[4] [5].

Qawasmeh, Ethar, Al-Saleh, Mohammed I., Al-Sharif, Ziad A..  2019.  Towards a Generic Approach for Memory Forensics. 2019 Sixth HCT Information Technology Trends (ITT). :094—098.

The era of information technology has, unfortunately, contributed to the tremendous rise in the number of criminal activities. However, digital artifacts can be utilized in convicting cybercriminal and exposing their activities. The digital forensics science concerns about all aspects related to cybercrimes. It seeks digital evidence by following standard methodologies to be admitted in court rooms. This paper concerns about memory forensics for the unique artifacts it holds. Memory contains information about the current state of systems and applications. Moreover, an application's data explains how a criminal has been interacting the application just before the memory is acquired. Memory forensics at the application level is currently random and cumbersome. Targeting specific applications is what forensic researchers and practitioner are currently striving to provide. This paper suggests a general solution to investigate any application. Our solution aims to utilize an application's data structures and variables' information in the investigation process. This is because an application's data has to be stored and retrieved in the means of variables. Data structures and variables' information can be generated by compilers for debugging purposes. We show that an application's information is a valuable resource to the investigator.

Chopade, Mrunali, Khan, Sana, Shaikh, Uzma, Pawar, Renuka.  2019.  Digital Forensics: Maintaining Chain of Custody Using Blockchain. 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :744—747.

The fundamental aim of digital forensics is to discover, investigate and protect an evidence, increasing cybercrime enforces digital forensics team to have more accurate evidence handling. This makes digital evidence as an important factor to link individual with criminal activity. In this procedure of forensics investigation, maintaining integrity of the evidence plays an important role. A chain of custody refers to a process of recording and preserving details of digital evidence from collection to presenting in court of law. It becomes a necessary objective to ensure that the evidence provided to the court remains original and authentic without tampering. Aim is to transfer these digital evidences securely using encryption techniques.

Ellison, Dagney, Ikuesan, Richard Adeyemi, Venter, Hein S..  2019.  Ontology for Reactive Techniques in Digital Forensics. 2019 IEEE Conference on Application, Information and Network Security (AINS). :83—88.

Techniques applied in response to detrimental digital incidents vary in many respects according to their attributes. Models of techniques exist in current research but are typically restricted to some subset with regards to the discipline of the incident. An enormous collection of techniques is actually available for use. There is no single model representing all these techniques. There is no current categorisation of digital forensics reactive techniques that classify techniques according to the attribute of function and nor is there an attempt to classify techniques in a means that goes beyond a subset. In this paper, an ontology that depicts digital forensic reactive techniques classified by function is presented. The ontology itself contains additional information for each technique useful for merging into a cognate system where the relationship between techniques and other facets of the digital investigative process can be defined. A number of existing techniques were collected and described according to their function - a verb. The function then guided the placement and classification of the techniques in the ontology according to the ontology development process. The ontology contributes to a knowledge base for digital forensics - essentially useful as a resource for the various people operating in the field of digital forensics. The benefit of this that the information can be queried, assumptions can be made explicit, and there is a one-stop-shop for digital forensics reactive techniques with their place in the investigation detailed.

Duy, Phan The, Do Hoang, Hien, Thu Hien, Do Thi, Ba Khanh, Nguyen, Pham, Van-Hau.  2019.  SDNLog-Foren: Ensuring the Integrity and Tamper Resistance of Log Files for SDN Forensics using Blockchain. 2019 6th NAFOSTED Conference on Information and Computer Science (NICS). :416—421.

Despite bringing many benefits of global network configuration and control, Software Defined Networking (SDN) also presents potential challenges for both digital forensics and cybersecurity. In fact, there are various attacks targeting a range of vulnerabilities on vital elements of this paradigm such as controller, Northbound and Southbound interfaces. In addition to solutions of security enhancement, it is important to build mechanisms for digital forensics in SDN which provide the ability to investigate and evaluate the security of the whole network system. It should provide features of identifying, collecting and analyzing log files and detailed information about network devices and their traffic. However, upon penetrating a machine or device, hackers can edit, even delete log files to remove the evidences about their presence and actions in the system. In this case, securing log files with fine-grained access control in proper storage without any modification plays a crucial role in digital forensics and cybersecurity. This work proposes a blockchain-based approach to improve the security of log management in SDN for network forensics, called SDNLog-Foren. This model is also evaluated with different experiments to prove that it can help organizations keep sensitive log data of their network system in a secure way regardless of being compromised at some different components of SDN.

Amato, Giuseppe, Falchi, Fabrizio, Gennaro, Claudio, Massoli, Fabio Valerio, Passalis, Nikolaos, Tefas, Anastasios, Trivilini, Alessandro, Vairo, Claudio.  2019.  Face Verification and Recognition for Digital Forensics and Information Security. 2019 7th International Symposium on Digital Forensics and Security (ISDFS). :1—6.

In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.

Khalaf, Rayan Sulaiman, Varol, Asaf.  2019.  Digital Forensics: Focusing on Image Forensics. 2019 7th International Symposium on Digital Forensics and Security (ISDFS). :1—5.

The world is continuously developing, and people's needs are increasing as well; so too are the number of thieves increasing, especially electronic thieves. For that reason, companies and individuals are always searching for experts who will protect them from thieves, and these experts are called digital investigators. Digital forensics has a number of branches and different parts, and image forensics is one of them. The budget for the images branch goes up every day in response to the need. In this paper we offer some information about images and image forensics, image components and how they are stored in digital devices and how they can be deleted and recovered. We offer general information about digital forensics, focusing on image forensics.

2020-05-15
Al-Saleh, Mohammed I., Al-Sharif, Ziad A., Alawneh, Luay.  2019.  Network Reconnaissance Investigation: A Memory Forensics Approach. 2019 10th International Conference on Information and Communication Systems (ICICS).

Perpetrators utilize different network reconnaissance techniques in order to discover vulnerabilities and conduct their attacks. Port scanning can be leveraged to conclude open ports, available services, and even running operating systems along with their versions. Even though these techniques are effective, their aggressiveness for information gain could leave an apparent sign of attack, which can be observed by the variety of security controls deployed at the network perimeter of an organization. However, not all such attacks can be stopped nor the corresponding security controls can defend against insiders. In this paper, we tackle the problem of reconnaissance detection using a different approach. We utilize the rich information that is kept in memory (or RAM). We observe that packets sent or received stay in memory for a while. Our results show that inspecting memory for attack signs is beneficial. Furthermore, correlating contents that are obtained from different memories empowers the investigation process and helps reach to conclusions.

2020-04-17
Chen, Guangxuan, Wu, Di, Chen, Guangxiao, Qin, Panke, Zhang, Lei, Liu, Qiang.  2019.  Research on Digital Forensics Framework for Malicious Behavior in Cloud. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 1:1375—1379.

The difficult of detecting, response, tracing the malicious behavior in cloud has brought great challenges to the law enforcement in combating cybercrimes. This paper presents a malicious behavior oriented framework of detection, emergency response, traceability, and digital forensics in cloud environment. A cloud-based malicious behavior detection mechanism based on SDN is constructed, which implements full-traffic flow detection technology and malicious virtual machine detection based on memory analysis. The emergency response and traceability module can clarify the types of the malicious behavior and the impacts of the events, and locate the source of the event. The key nodes and paths of the infection topology or propagation path of the malicious behavior will be located security measure will be dispatched timely. The proposed IaaS service based forensics module realized the virtualization facility memory evidence extraction and analysis techniques, which can solve volatile data loss problems that often happened in traditional forensic methods.

2019-05-08
Xiang, Jie, Chen, Long.  2018.  A Method of Docker Container Forensics Based on API. Proceedings of the 2Nd International Conference on Cryptography, Security and Privacy. :159–164.
As one of the main technologies supporting cloud computing virtualization, Docker is featured in its fast and lightweight virtualization which has been adopted by numerous platform-as-a-service (PaaS) systems, but forensics research for Docker has not been paid the corresponding attention yet. Docker exists to store and distribute illegal information as a carrier for initiating attacks like traditional cloud services. The paper explains Docker service principles and structural features, and analyzing the model and method of forensics in related cloud environment, then proposes a Docker container forensics solution based on the Docker API. In this paper, Docker APIs realize the derivation of the Docker container instances, copying and back-up of the container data volume, extraction of the key evidence data, such as container log information, configuration information and image information, thus conducts localized fixed forensics to volatile evidence and data in the Docker service container. Combined with digital signatures and digital encryption technology to achieve the integrity of the original evidence data protection.
Richter, Timo, Escher, Stephan, Schönfeld, Dagmar, Strufe, Thorsten.  2018.  Forensic Analysis and Anonymisation of Printed Documents. Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security. :127–138.
Contrary to popular belief, the paperless office has not yet established itself. Printer forensics is therefore still an important field today to protect the reliability of printed documents or to track criminals. An important task of this is to identify the source device of a printed document. There are many forensic approaches that try to determine the source device automatically and with commercially available recording devices. However, it is difficult to find intrinsic signatures that are robust against a variety of influences of the printing process and at the same time can identify the specific source device. In most cases, the identification rate only reaches up to the printer model. For this reason we reviewed document colour tracking dots, an extrinsic signature embedded in nearly all modern colour laser printers. We developed a refined and generic extraction algorithm, found a new tracking dot pattern and decoded pattern information. Through out we propose to reuse document colour tracking dots, in combination with passive printer forensic methods. From privacy perspective we additional investigated anonymization approaches to defeat arbitrary tracking. Finally we propose our toolkitdeda which implements the entire workflow of extracting, analysing and anonymisation of a tracking dot pattern.
Balogun, A. M., Zuva, T..  2018.  Criminal Profiling in Digital Forensics: Assumptions, Challenges and Probable Solution. 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC). :1–7.

Cybercrime has been regarded understandably as a consequent compromise that follows the advent and perceived success of the computer and internet technologies. Equally effecting the privacy, trust, finance and welfare of the wealthy and low-income individuals and organizations, this menace has shown no indication of slowing down. Reports across the world have consistently shown exponential increase in the numbers and costs of cyber-incidents, and more worriedly low conviction rates of cybercriminals, over the years. Stakeholders increasingly explore ways to keep up with containing cyber-incidents by devising tools and techniques to increase the overall efficiency of investigations, but the gap keeps getting wider. However, criminal profiling - an investigative technique that has been proven to provide accurate and valuable directions to traditional crime investigations - has not seen a widespread application, including a formal methodology, to cybercrime investigations due to difficulties in its seamless transference. This paper, in a bid to address this problem, seeks to preliminarily identify the exact benefits criminal profiling has brought to successful traditional crime investigations and the benefits it can translate to cybercrime investigations, identify the challenges posed by the cyber-scene to its implementation in cybercrime investigations, and proffer a practicable solution.

Ning, W., Zhi-Jun, L..  2018.  A Layer-Built Method to the Relevancy of Electronic Evidence. 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC). :416–420.

T138 combat cyber crimes, electronic evidence have played an increasing role, but in judicial practice the electronic evidence were not highly applied because of the natural contradiction between the epistemic uncertainty of electronic evidence and the principle of discretionary evidence of judge in the court. in this paper, we put forward a layer-built method to analyze the relevancy of electronic evidence, and discussed their analytical process combined with the case study. The initial practice shows the model is feasible and has a consulting value in analyzing the relevancy of electronic evidence.