No abstract

Little or no research has been directed to analysis and researching forensic analysis of the Bitcoin mixing or 'tumbling' service themselves. This work is intended to examine effective tooling and methodology for recovering forensic artifacts from two privacy focused mixing services namely Obscuro which uses the secure enclave on intel chips to provide enhanced confidentiality and Wasabi wallet which uses CoinJoin to mix and obfuscate crypto currencies. These wallets were set up on VMs and then several forensic tools used to examine these VM images for relevant forensic artifacts. These forensic tools were able to recover a broad range of forensic artifacts and found both network forensics and logging files to be a useful source of artifacts to deanonymize these mixing services.

There is an increasing drive to provide improved levels of trust within an Internet-of-Things (IoTs) environments, but the devices and sensors used tend to be limited in their capabilities for dealing with traditional cryptography methods. Resource constraints and security are often the two major concerns of IIoT (Industrial IoT applications and big data generation at the present time. The strict security measures are often not significantly resource-managed and therefore, negotiation normally takes place between these. Following this, various light-weight versions of generic security primitives have been developed for IIoT and other resource-constrained sustainability. In this paper, we address the authentication concerns for resource-constrained environments by designing an efficient authentication protocol. Our authentication scheme is based on LiSP (light-weight Signcryption Protocol); however, some further customization has been performed on it to make it more suitable for IIoT-like resource-constrained environments. We use Keccack as the hash function in the process and Elli for light-weight public-key cryptography. We name our authentication scheme: Extended light-weight Signcryption Protocol with Keccack (LiSP-XK). The paper outlines a comparative analysis on our new design of authentication against a range of state-of-the-art schemes. We find the suitability of LiSP-XK for IIoT like environments due to its lesser complexity and less energy consumption. Moreover, the signcryption process is also beneficial in enhancing security. Overall the paper shows that LiSP-XK is overall 35% better in efficiency as compared to the other signcryption approaches.

The pandemic/epidemic of COVID-19 has affected people worldwide. A huge number of lives succumbed to death due to the sudden outbreak of this corona virus infection. The specified symptoms of COVID-19 detection are very common like as normal flu; asymptomatic version of COVID-19 has become a critical issue. Therefore, as a precautionary measurement oxygen level needs to be monitored by every individual if no other critical condition is found. It is not the only parameter for COVID-19 detection but, as per the suggestions by different medical organizations such as WHO it is better to use oximeter to monitor the oxygen level in probable patients as a precaution. People are using the oximeters personally; however, not having any clue or guidance regarding the measurements obtained. Therefore, in this paper, we have shown a framework of oxygen level monitoring and severity calculation and probabilistic decision of being a COVID-19 patient. This framework is also able to maintain the privacy of patient information and uses probabilistic classification to measure the severity. Results are measured based on latency of blockchain creation and overall response, throughput, detection and severity accuracy. The analysis finds the solution efficient and significant in the IoT framework for the present health hazard in our world.