DiLeNA: Distributed Ledger Network Analyzer

This paper describes the Distributed Ledger Network Analyzer (DiLeNA), a new software tool for the analysis of the transactions network recorded in Distributed Ledger Technologies (DLTs). The set of transactions in a DLT forms a complex network. Studying its characteristics and peculiarities is of paramount importance, in order to understand how users interact in the distributed ledger system. The tool design and implementation is introduced and some results are provided. In particular, the Bitcoin and Ethereum blockchains, i.e. the most famous and used DLTs at the time of writing, have been analyzed and compared.Comment: Proceeding of the 3rd Workshop on Cryptocurrencies and Blockchains for Distributed Systems (CryBlock 2020

Development of a cryptocurrency bot

As an emerging market and research direction, cryptocurrencies and cryptocurrency trading have seen considerable progress and a notable upturn in interest and activity, even entering the market people without experience or sufficient knowledge. In addition, the tremendous volatility and the fact that this market never closes make the human factor affect crypto asset trading too much. Hence, in this project a cryptocurrency trading bot is developed. To be exact, the algorithm consists of two distinguishable parts: the bot itself and the backtesting. Notwithstanding that both parts departs from the analysis of financial markets in general, and cryptocurrencies in particular, both present clear differences in terms of code and pretext. On the one hand, the bot’s algorithm is used to trade in reality, specifically through the Binance exchange. Here the user plays risks their monetary capital. On the other hand, backtesting consists of verifying the trading strategy based on historical data. Backtesting serves, then, as validation of the strategy to be followed by the bot. Thus, all the necessary fundamentals to understand both the general cryptocurrency context and technical analysis relevant concepts are presented, along with a detailed explanation of the implemented algorithm and a proper analysis of the obtained results. Finally, further code improvements and new ideas to develop in the future are suggested, apart from presenting the code developed

Scalable, Data- intensive Network Computation

To enable groups of collaborating researchers at different locations to effectively share large datasets and investigate their spontaneous hypotheses on the fly, we are interested in de- veloping a distributed system that can be easily leveraged by a variety of data intensive applications. The system is composed of (i) a number of best effort logistical depots to en- able large-scale data sharing and in-network data processing, (ii) a set of end-to-end tools to effectively aggregate, manage and schedule a large number of network computations with attendant data movements, and (iii) a Distributed Hash Table (DHT) on top of the generic depot services for scalable data management. The logistical depot is extended by following the end-to-end principles and is modeled with a closed queuing network model. Its performance characteristics are studied by solving the steady state distributions of the model using local balance equations. The modeling results confirm that the wide area network is the performance bottleneck and running concurrent jobs can increase resource utilization and system throughput. As a novel contribution, techniques to effectively support resource demanding data- intensive applications using the ¯ne-grained depot services are developed. These techniques include instruction level scheduling of operations, dynamic co-scheduling of computation and replication, and adaptive workload control. Experiments in volume visualization have proved the effectiveness of these techniques. Due to the unique characteristic of data- intensive applications and our co-scheduling algorithm, a DHT is implemented on top of the basic storage and computation services. It demonstrates the potential of the Logistical Networking infrastructure to serve as a service creation platform

Domestic electricity use: a high-resolution energy demand model

The pattern of electricity use in an individual domestic dwelling is highly dependent upon the activities of the occupants and their associated use of electrical appliances. This paper presents a high-resolution model of domestic electricity use, that is based upon a combination of patterns of active occupancy (i.e. when people are at home and awake), and daily activity profiles that characterise how people spend their time performing certain activities. One-minute resolution synthetic electricity demand data is created through the simulation of appliance use; the model covers all major appliances commonly found in the domestic environment. In order to validate the model, electricity demand was recorded over the period of a year within 22 dwellings in the East Midlands, UK. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models

User Review Analysis for Requirement Elicitation: Thesis and the framework prototype's source code

Online reviews are an important channel for requirement elicitation. However, requirement engineers face challenges when analysing online user reviews, such as data volumes, technical supports, existing techniques, and legal barriers. Juan Wang proposes a framework solving user review analysis problems for the purpose of requirement elicitation that sets up a channel from downloading user reviews to structured analysis data. The main contributions of her work are: (1) the thesis proposed a framework to solve the user review analysis problem for requirement elicitation; (2) the prototype of this framework proves its feasibility; (3) the experiments prove the effectiveness and efficiency of this framework. This resource here is the latest version of Juan Wang's PhD thesis "User Review Analysis for Requirement Elicitation" and all the source code of the prototype for the framework as the results of her thesis

DAWG: A Defense Against Cache Timing Attacks in Speculative Execution Processors

Software side channel attacks have become a serious concern with the recent rash of attacks on speculative processor architectures. Most attacks that have been demonstrated exploit the cache tag state as their exfiltration channel. While many existing defense mechanisms that can be implemented solely in software have been proposed, these mechanisms appear to patch specific attacks, and can be circumvented. In this paper, we propose minimal modifications to hardware to defend against a broad class of attacks, including those based on speculation, with the goal of eliminating the entire attack surface associated with the cache state covert channel. We propose DAWG, Dynamically Allocated Way Guard, a generic mechanism for secure way partitioning of set associative structures including memory caches. DAWG endows a set associative structure with a notion of protection domains to provide strong isolation. When applied to a cache, unlike existing quality of service mechanisms such as Intel\u27s Cache Allocation Technology (CAT), DAWG isolates hits and metadata updates across protection domains. We describe how DAWG can be implemented on a processor with minimal modifications to modern operating systems. We argue a non-interference property that is orthogonal to speculative execution and therefore argue that existing attacks such as Spectre Variant 1 and 2 will not work on a system equipped with DAWG. Finally, we evaluate the performance impact of DAWG on the cache subsystem