Biblio

The market landscape has undergone dramatic change because of globalization, shifting marketing conditions, cost pressure, increased competition, and volatility. Transforming the operation of businesses has been possible because of the astonishing speed at which technology has witnessed the change. The automotive industry is on the edge of a revolution. The increased customer expectations, changing ownership, self-driving vehicles and much more have led to the transformation of automobiles, applications, and services from artificial intelligence, sensors, RFID to big data analysis. Large automobiles industries have been emphasizing the collection of data to gain insight into customer's expectations, preferences, and budgets alongside competitor's policies. Statistical methods can be applied to historical data, which has been gathered from various authentic sources and can be used to identify the impact of fixed and variable marketing investments and support automakers to come up with a more effective, precise, and efficient approach to target customers. Proper analysis of supply chain data can disclose the weak links in the chain enabling to adopt timely countermeasures to minimize the adverse effects. In order to fully gain benefit from analytics, the collaboration of a detailed set of capabilities responsible for intersecting and integrating with multiple functions and teams across the business is required. The effective role played by big data analysis in the automobile industry has also been expanded in the research paper. The research paper discusses the scope and challenges of big data. The paper also elaborates on the working technology behind the concept of big data. The paper illustrates the working of MapReduce technology that executes in the back end and is responsible for performing data mining.

In today's society, under the comprehensive arrival of the Internet era, the rapid development of technology has facilitated people's production and life, but it is also a “double-edged sword”, making people's personal information and other data subject to a greater threat of abuse. The unique features of big data technology, such as massive storage, parallel computing and efficient query, have created a breakthrough opportunity for the key technologies of large-scale network security situational awareness. On the basis of big data acquisition, preprocessing, distributed computing and mining and analysis, the big data analysis platform provides information security assurance services to the information system. This paper will discuss the security situational awareness in large-scale network environment and the promotion of big data technology in security perception.

The transmission line tower is affected by the surface subsidence in the mined out area of coal mine, which will appear the phenomenon of subsidence, inclination and even tower collapse, threatening the operation safety of the transmission line tower in the mined out area. Therefore, a Safety and Damage Assessment Method of Transmission Line Tower in Goaf Based on Artificial Intelligence is proposed. Firstly, the geometric model of the coal seam in the goaf and the structural reliability model of the transmission line tower are constructed to evaluate the safety. Then, the random forest algorithm in artificial intelligence is used to evaluate the damage of the tower, so as to take protective measures in time. Finally, a finite element simulation model of tower foundation interaction is built, and its safety (force) and damage identification are experimentally analyzed. The results show that the proposed method can ensure high accuracy of damage assessment and reliable judgment of transmission line tower safety within the allowable error.

Proof-of-Work (PoW) is a popular protocol used in Blockchain systems to resolve double-spending problems. However, if an attacker has access to calculation hash power greater than half of the total hash power, this attacker can create a double-spending attack or 51% attack. The cost of creating a 51% attack is surprisingly low if hash power is abundantly available. That posts a great threat to lots of PoW blockchains. We propose a technique to combine history weighted information of miners with the total calculation difficulty to alleviate the 51% attack problem. Analysis indicates that with the new technique, the cost of a traditional attack is increased by two orders of magnitude.

Prior work on mobile app reviews has demonstrated that user reviews contain a wealth of information and are seen as a potential source of requirements. However, most of the studies done in this area mainly focused on mining and analyzing user reviews from the US App Store, leaving reviews of users from other countries unexplored. In this paper, we seek to understand if the perception of the same apps between users from other countries and that from the US differs through analyzing user reviews. We retrieve 300,643 user reviews of the 15 most downloaded iOS apps of 2018, published directly by Apple, from nine English-speaking countries over the course of 5 months. We manually classify 3,358 reviews into several software quality and improvement factors. We leverage a random forest based algorithm to identify factors that can be used to differentiate reviews between the US and other countries. Our preliminary results show that all countries have some factors that are proportionally inconsistent with the US.

Selfish mining is a well-known mining attack strategy discovered by Eyal and Sirer in 2014. After that, the attackers' strategy has been further discussed by many other works, which analyze the strategy and behavior of a single attacker. The extension of the strategy research is greatly restricted by the assumption that there is only one attacker in the blockchain network, since, in many cases, a proof of work blockchain has multiple attackers. The attackers can be independent of others instead of sharing information and attacking the blockchain as a whole. In this paper, we will establish a new model to analyze the miners' behavior in a proof of work blockchain with multiple attackers. Based on our model, we extend the attackers' strategy by proposing a new strategy set publish-n. Meanwhile, we will also review other attacking strategies such as selfish mining and stubborn mining in our model to explore whether these strategies work or not when there are multiple attackers. The performances of different strategies are compared using relative stale block rate of the attackers. In a proof of work blockchain model with two attackers, strategy publish-n can beat selfish mining by up to 26.3%.

Mining is the foundation of blockchain-based cryptocurrencies such as Bitcoin rewarding the miner for finding blocks for new transactions. The Monero currency enables mining with standard hardware in contrast to special hardware (ASICs) as often used in Bitcoin, paving the way for in-browser mining as a new revenue model for website operators. In this work, we study the prevalence of this new phenomenon. We identify and classify mining websites in 138M domains and present a new fingerprinting method which finds up to a factor of 5.7 more miners than publicly available block lists. Our work identifies and dissects Coinhive as the major browser-mining stakeholder. Further, we present a new method to associate mined blocks in the Monero blockchain to mining pools and uncover that Coinhive currently contributes 1.18% of mined blocks having turned over 1293 Moneros in June 2018.

Consider the following set-up for the plot of a possible future episode of the TV series Black Mirror: human brains can be connected directly to the net and MiningMind Inc. has developed a technology that merges a reward system with a cryptojacking engine that uses the human brain to mine cryptocurrency (or to carry out some other mining activity). Part of our brain will be committed to cryptographic calculations (mining), leaving the remaining part untouched for everyday operations, i.e., for our brain's normal daily activity. In this short paper, we briefly argue why this set-up might not be so far fetched after all, and explore the impact that such a technology could have on our lives and our society. This article is summarized in: the morning paper an interesting/influential/important paper from the world of CS every weekday morning, as selected by Adrian Colyer

A wave of alternative coins that can be effectively mined without specialized hardware, and a surge in cryptocurrencies' market value has led to the development of cryptocurrency mining ( cryptomining ) services, such as Coinhive, which can be easily integrated into websites to monetize the computational power of their visitors. While legitimate website operators are exploring these services as an alternative to advertisements, they have also drawn the attention of cybercriminals: drive-by mining (also known as cryptojacking ) is a new web-based attack, in which an infected website secretly executes JavaScript code and/or a WebAssembly module in the user's browser to mine cryptocurrencies without her consent. In this paper, we perform a comprehensive analysis on Alexa's Top 1 Million websites to shed light on the prevalence and profitability of this attack. We study the websites affected by drive-by mining to understand the techniques being used to evade detection, and the latest web technologies being exploited to efficiently mine cryptocurrency. As a result of our study, which covers 28 Coinhive-like services that are widely being used by drive-by mining websites, we identified 20 active cryptomining campaigns. Motivated by our findings, we investigate possible countermeasures against this type of attack. We discuss how current blacklisting approaches and heuristics based on CPU usage are insufficient, and present MineSweeper, a novel detection technique that is based on the intrinsic characteristics of cryptomining code, and, thus, is resilient to obfuscation. Our approach could be integrated into browsers to warn users about silent cryptomining when visiting websites that do not ask for their consent.

In this paper, we examine the recent trend to- wards in-browser mining of cryptocurrencies; in particular, the mining of Monero through Coinhive and similar code- bases. In this model, a user visiting a website will download a JavaScript code that executes client-side in her browser, mines a cryptocurrency - typically without her consent or knowledge - and pays out the seigniorage to the website. Websites may consciously employ this as an alternative or to supplement advertisement revenue, may offer premium content in exchange for mining, or may be unwittingly serving the code as a result of a breach (in which case the seigniorage is collected by the attacker). The cryptocurrency Monero is preferred seemingly for its unfriendliness to large-scale ASIC mining that would drive browser-based efforts out of the market, as well as for its purported privacy features. In this paper, we survey this landscape, conduct some measurements to establish its prevalence and profitability, outline an ethical framework for considering whether it should be classified as an attack or business opportunity, and make suggestions for the detection, mitigation and/or prevention of browser-based mining for non- consenting users.

Botnets have become one of the most significant cyber threats over the last decade. The diffusion of the "Internet of Things" and its for-profit exploitation, contributed to botnets spread and sophistication, thus providing real, efficient and profitable criminal cyber-services. Recent research on botnet detection focuses on traffic pattern-based detection, and on analyzing the network traffic generated by the infected hosts, in order to find behavioral patterns independent from the specific payloads, architectures and protocols. In this paper we address the periodic behavioral patterns of infected hosts communicating with their Command-and-Control servers. The main novelty introduced is related to the traffic analysis in the frequency domain without using the well-known Fast Fourier Transform. Moreover, the mentioned analysis is performed through the exploitation of the proxy logs, easily deployable on almost every real-world scenario, from enterprise networks to mobile devices.

Earth provides natural resources, such as fossil fuels and minerals, that are vital for Europe's economy. As the global demand grows, especially for strategic metals, commodity prices rapidly rise and there is an identifiable risk of an increasing supply shortage of some metals, including those identified as critical to Europe's high technology sector. Hence a major element in any economy's long-term strategy must be to respond to the increasing pressure on natural resources to ensure security of supply for these strategic metals. In today's rapidly changing global economic landscape, mining in the deep sea, specifically at extinct hydrothermal vents and the vast areas covered by polymetallic nodules, has gone from a distant possibility to a likely reality within just a decade. The extremely hostile conditions found on the deep-ocean floor pose specific challenges, both technically and environmentally, which are demanding and entirely different from land-based mining. At present, European offshore industries and marine research institutions have significant experience and technology and are well positioned to develop engineering and knowledge-based solutions to resource exploitation in these challenging and sensitive environments. However, to keep this position there is a need to initiate pilot studies to develop breakthrough methodologies for the exploration, assessment and extraction of deep-sea minerals, as well as investigate the implications for economic and environmental sustainability. The Blue Mining project will address all aspects of the entire value chain in this field, from resource discovery to resource assessment, from exploitation technologies to the legal and regulatory framework.

Botnets are considered one of the most dangerous species of network-based attack today because they involve the use of very large coordinated groups of hosts simultaneously. The behavioral analysis of computer networks is at the basis of the modern botnet detection methods, in order to intercept traffic generated by malwares for which signatures do not exist yet. Defining a pattern of features to be placed at the basis of behavioral analysis, puts the emphasis on the quantity and quality of information to be caught and used to mark data streams as normal or abnormal. The problem is even more evident if we consider extensive computer networks or clouds. With the present paper we intend to show how heuristics applied to large-scale proxy logs, considering a typical phase of the life cycle of botnets such as the search for C&C Servers through AGDs (Algorithmically Generated Domains), may provide effective and extremely rapid results. The present work will introduce some novel paradigms. The first is that some of the elements of the supply chain of botnets could be completed without any interaction with the Internet, mostly in presence of wide computer networks and/or clouds. The second is that behind a large number of workstations there are usually "human beings" and it is unlikely that their behaviors will cause marked changes in the interaction with the Internet in a fairly narrow time frame. Finally, AGDs can highlight, at the moment, common lexical features, detectable quickly and without using any black/white list.