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This paper examines the Ethereum network in the context of an investigation. The validation of data sources is achieved through different client software on both the Ropsten network and the live blockchain. New scenarios are also used test common patterns in order to track for start and end points for Ethereum and ERC20 tokens. [ABSTRACT FROM AUTHOR]

The increasing use of encrypted data within file storage and in network communications leaves investigators with many challenges. One of the most challenging is the Tor protocol, as its main focus is to protect the privacy of the user, in both its local footprint within a host and over a network connection. The Tor browser, though, can leave behind digital artefacts which can be used by an investigator. This paper outlines an experimental methodology and provides results for evidence trails which can be used within real-life investigations. [ABSTRACT FROM AUTHOR]

Decrypting and inspecting encrypted malicious communications may assist crime detection and prevention. Access to client or server memory enables the discovery of artefacts required for decrypting secure communications. This paper develops the MemDecrypt framework to investigate the discovery of encrypted artefacts in memory and applies the methodology to decrypting the secure communications of virtual machines. For Secure Shell, used for secure remote server management, file transfer, and tunnelling inter alia, MemDecrypt experiments rapidly yield AES-encrypted details for a live secure file transfer including remote user credentials, transmitted file name and file contents. Thus, MemDecrypt discovers cryptographic artefacts and quickly decrypts live SSH malicious communications including the detection and interception of data exfiltration of confidential data. [ABSTRACT FROM AUTHOR]

Smart phones often leave behind a wealth of information that can be used as an evidence during an investigation. There are thus many smart phone applications that employ encryption to store and/or transmit data, and this can add a layer of complexity for an investigator. IMO is a popular application which employs encryption for both call and chat activities. This paper explores important artifacts from both the device and from the network traffic. This was generated for both Android and iOS platforms. The novel aspect of the work is the extensive analysis of encrypted network traffic generated by IMO. Along with this the paper defines a new method of using a firewall to explore the obscured options of connectivity, and in a way which is independent of the protocol used by the IMO client and server. Our results outline that we can correctly detect IMO traffic flows and classify different events of its chat and call related activities. We have also compared IMO network traffic of Android and iOS platforms to report the subtle differences. The results are valid for IMO 9.8.00 on Android and 7.0.55 on iOS. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate possible threats with unofficial Android marketplaces, and geo-localize the malware distribution over three main regions: China; Europe; and Russia. It provides a comprehensive review of existing academic literature about security in Android focusing especially on malware detection systems and existing malware databases. Through the implementation of a methodology for identification of malicious applications it has been collected data revealing a 5% of them as malicious in an overall analysis. Furthermore, the analysis shown that Russia and Europe have a preponderance of generic detections and adware, while China is found to be targeted mainly by riskware and malware. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges of performing a forensic investigation against a multi-node Hadoop cluster and proposes a methodology for examiners to use in such situations. The procedure's aim of minimising disruption to the data centre during the acquisition process is achieved through the use of RAM forensics. This affords initial cluster reconnaissance which in turn facilitates targeted data acquisition on the identified DataNodes. To evaluate the methodology's feasibility, a small Hadoop Distributed File System (HDFS) was configured and forensic artefacts simulated upon it by deleting data originally stored in the cluster. RAM acquisition and analysis was then performed on the NameNode in order to test the validity of the suggested methodology. The results are cautiously positive in establishing that RAM analysis of the NameNode can be used to pinpoint the data blocks affected by the attack, allowing a targeted approach to the acquisition of data from the DataNodes, provided that the physical locations can be determined. A full forensic analysis of the DataNodes was beyond the scope of this project. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose novel approaches to the problem of classifying high entropy file fragments. Although classification of file fragments is central to the science of Digital Forensics, high entropy types have been regarded as a problem. Roussev and Garfinkel (2009) argue that existing methods will not work on high entropy fragments because they have no discernible patterns to exploit. We propose two methods that do not rely on such patterns. The NIST statistical test suite is used to detect randomness in 4 KiB fragments. These test results were analysed using an Artificial Neural Network (ANN). Optimum results were 91% and 82% correct classification rates for encrypted and compressed fragments respectively. We also use the compressibility of a fragment as a measure of its randomness. Correct classification was 76% and 70% for encrypted and compressed fragments respectively. We show that newer more efficient compression formats are more difficult to classify. We have used subsets of the publicly available ‘GovDocs1 Million File Corpus’ so that any future research may make valid comparisons with the results obtained here. [Copyright &y& Elsevier]

• Merging of value chain and supply chain into a single transparent blockchain-based solution. • The use of blockchain for e-commerce with the start from product development to customer acquisition. • Lattice-based cryptography usage in blockchain signcryption process. • Introduction of proof-of-accomplishment as an extension of smart contract in heterogeneous stakeholder environment. A distributed and transparent ledger system is considered for various e-commerce products including health medicines, electronics, security appliances, food products and many more to ensure technological and e-commerce sustainability. This solution, named as 'PRODCHAIN', is a generic blockchain framework with lattice-based cryptographic processes for reducing the complexity for tracing the e-commerce products. Moreover, we have introduced a rating based consensus process called Proof of Accomplishment (PoA). The solution has been analyzed and experimental studies are performed on Ethereum network. The results are discussed in terms of latency and throughput which prove the efficiency of PRODCHAIN in e-commerce products and services. The presented solution is beneficial for improving the traceability of the products ensuring the social and financial sustainability. This work will help the researchers to gain knowledge about the blockchain implications for supply chain possibilities in future developments for society. [ABSTRACT FROM AUTHOR]

Ransomware continues to grow in both scale, cost, complexity and impact since its initial discovery nearly 30 years ago. Security practitioners are engaged in a continual "arms race" with the ransomware developers attempting to defend their digital infrastructure against such attacks. Recent manifestations of ransomware have started to employ a hybrid combination of symmetric and asymmetric encryption to encode user's files. This paper describes an investigation that tried to determine if the techniques currently employed in the field of digital forensics could be leveraged to discover the encryption keys used by these types of malicious software thus mitigating the effects of a ransomware attack. Memory was captured from a system infected by ransomware and its contents was examined using live forensic tools, with the intent of identifying the symmetric encryption keys being used. NotPetya, Bad Rabbit and Phobos hybrid ransomware samples were tested during the investigation. If keys were discovered, the following two steps were also performed. Firstly, a timeline was manually created by combining data from multiple sources to illustrate the ransomware's behaviour as well as showing when the encryption keys were present in memory and how long they remained there. Secondly, an attempt was made to decrypt the files encrypted by the ransomware using the found keys. In all cases, the investigation was able to confirm that it was possible to identify the encryption keys used. A description of how these found keys were then used to successfully decrypt files that had been encrypted during the execution of the ransomware is also given. The resulting generated timelines provided a excellent way to visualise the behaviour of the ransomware and the encryption key management practices it employed, and from a forensic investigation and possible mitigation point of view, when the encryption keys are in memory. • Successfully recovered encryption keys from volatile memory used by ransomware. • Applying live forensic techniques to ransomware mitigation. • Graphical representation of ransomware behaviour and encryption key life cycle. • Recovery of files encrypted by NotPetya, Bad Rabbit and Phobos ransomware. [ABSTRACT FROM AUTHOR]

In recent years the capacity of digital storage devices has been increasing at a rate that has left digital forensic services struggling to cope. There is an acknowledgement that current forensic tools have failed to keep up. The workload is such that a form of ‘administrative triage’ takes place in many labs where perceived low priority jobs are delayed or dropped without reference to the data itself. In this paper we investigate the feasibility of first responders performing a fast initial scan of a device by sampling on the device itself. A Bloom filter is used to store the block hashes of large collections of contraband data. We show that by sampling disk clusters, we can achieve 99.9% accuracy scanning for contraband data in minutes. Even under the constraints imposed by low specification legacy equipment, it is possible to scan a device for contraband with a known and controllable margin of error in a reasonable time. We conclude that in this type of case it is feasible to boot the device into a forensically sound environment and do a pre-imaging scan to prioritise the device for further detailed investigation. [ABSTRACT FROM AUTHOR]