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2021-04-27
Aktepe, S., Varol, C., Shashidhar, N..  2020.  2020 8th International Symposium on Digital Forensics and Security (ISDFS). 2020 8th International Symposium on Digital Forensics and Security (ISDFS). :1—5.
Cryptocurrencies are the digital currencies designed to replace the regular cash money while taking place in our daily lives especially for the last couple of years. Mining cryptocurrencies are one of the popular ways to have them and make a profit due to unstable values in the market. This attracts attackers to utilize malware on internet users' computer resources, also known as cryptojacking, to mine cryptocurrencies. Cryptojacking started to be a major issue in the internet world. In this case, we developed MiNo, a web browser add-on application to detect these malicious mining activities running without the user's permission or knowledge. This add-on provides security and efficiency for the computer resources of the internet users. MiNo designed and developed with double-layer protection which makes it ahead of its competitors in the market.
2021-02-10
Romano, A., Zheng, Y., Wang, W..  2020.  MinerRay: Semantics-Aware Analysis for Ever-Evolving Cryptojacking Detection. 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE). :1129—1140.
Recent advances in web technology have made in-browser crypto-mining a viable funding model. However, these services have been abused to launch large-scale cryptojacking attacks to secretly mine cryptocurrency in browsers. To detect them, various signature-based or runtime feature-based methods have been proposed. However, they can be imprecise or easily circumvented. To this end, we propose MinerRay, a generic scheme to detect malicious in-browser cryptominers. Instead of leveraging unreliable external patterns, MinerRay infers the essence of cryptomining behaviors that differentiate mining from common browser activities in both WebAssembly and JavaScript contexts. Additionally, to detect stealthy mining activities without user consents, MinerRay checks if the miner can only be instantiated from user actions. MinerRay was evaluated on over 1 million websites. It detected cryptominers on 901 websites, where 885 secretly start mining without user consent. Besides, we compared MinerRay with five state-of-the-art signature-based or behavior-based cryptominer detectors (MineSweeper, CMTracker, Outguard, No Coin, and minerBlock). We observed that emerging miners with new signatures or new services were detected by MinerRay but missed by others. The results show that our proposed technique is effective and robust in detecting evolving cryptominers, yielding more true positives, and fewer errors.
Aktepe, S., Varol, C., Shashidhar, N..  2020.  MiNo: The Chrome Web Browser Add-on Application to Block the Hidden Cryptocurrency Mining Activities. 2020 8th International Symposium on Digital Forensics and Security (ISDFS). :1—5.

Cryptocurrencies are the digital currencies designed to replace the regular cash money while taking place in our daily lives especially for the last couple of years. Mining cryptocurrencies are one of the popular ways to have them and make a profit due to unstable values in the market. This attracts attackers to utilize malware on internet users' computer resources, also known as cryptojacking, to mine cryptocurrencies. Cryptojacking started to be a major issue in the internet world. In this case, we developed MiNo, a web browser add-on application to detect these malicious mining activities running without the user's permission or knowledge. This add-on provides security and efficiency for the computer resources of the internet users. MiNo designed and developed with double-layer protection which makes it ahead of its competitors in the market.

Gomes, G., Dias, L., Correia, M..  2020.  CryingJackpot: Network Flows and Performance Counters against Cryptojacking. 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). :1—10.
Cryptojacking, the appropriation of users' computational resources without their knowledge or consent to obtain cryp-tocurrencies, is a widespread attack, relatively easy to implement and hard to detect. Either browser-based or binary, cryptojacking lacks robust and reliable detection solutions. This paper presents a hybrid approach to detect cryptojacking where no previous knowledge about the attacks or training data is needed. Our Cryp-tojacking Intrusion Detection Approach, Cryingjackpot, extracts and combines flow and performance counter-based features, aggregating hosts with similar behavior by using unsupervised machine learning algorithms. We evaluate Cryingjackpot experimentally with both an artificial and a hybrid dataset, achieving F1-scores up to 97%.
Tanana, D., Tanana, G..  2020.  Advanced Behavior-Based Technique for Cryptojacking Malware Detection. 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS). :1—4.
With rising value and popularity of cryptocurrencies, they inevitably attract cybercriminals seeking illicit profits within blockchain ecosystem. Two of the most popular methods are ransomware and cryptojacking. Ransomware, being the first and more obvious threat has been extensively studied in the past. Unlike that, scientists have often neglected cryptojacking, because it’s less obvious and less harmful than ransomware. In this paper, we’d like to propose enhanced detection program to combat cryptojacking, additionally briefly touching history of cryptojacking, also known as malicious mining and reviewing most notable previous attempts to detect and combat cryptojacking. The review would include out previous work on malicious mining detection and our current detection program is based on its previous iteration, which mostly used CPU usage heuristics to detect cryptojacking. However, we will include additional metrics for malicious mining detection, such as network usage and calls to cryptographic libraries, which result in a 93% detection rate against the selected number of cryptojacking samples, compared to 81% rate achieved in previous work. Finally, we’ll discuss generalization of proposed detection technique to include GPU cryptojackers.
Varlioglu, S., Gonen, B., Ozer, M., Bastug, M..  2020.  Is Cryptojacking Dead After Coinhive Shutdown? 2020 3rd International Conference on Information and Computer Technologies (ICICT). :385—389.
Cryptojacking is the exploitation of victims' computer resources to mine for cryptocurrency using malicious scripts. It had become popular after 2017 when attackers started to exploit legal mining scripts, especially Coinhive scripts. Coinhive was actually a legal mining service that provided scripts and servers for in-browser mining activities. Nevertheless, over 10 million web users had been victims every month before the Coinhive shutdown that happened in Mar 2019. This paper explores the new era of the cryptojacking world after Coinhive discontinued its service. We aimed to see whether and how attackers continue cryptojacking, generate new malicious scripts, and developed new methods. We used a capable cryptojacking detector named CMTracker that proposed by Hong et al. in 2018. We automatically and manually examined 2770 websites that had been detected by CMTracker before the Coinhive shutdown. The results revealed that 99% of sites no longer continue cryptojacking. 1% of websites still run 8 unique mining scripts. By tracking these mining scripts, we detected 632 unique cryptojacking websites. Moreover, open-source investigations (OSINT) demonstrated that attackers still use the same methods. Therefore, we listed the typical patterns of cryptojacking. We concluded that cryptojacking is not dead after the Coinhive shutdown. It is still alive, but not as attractive as it used to be.
Tizio, G. Di, Ngo, C. Nam.  2020.  Are You a Favorite Target For Cryptojacking? A Case-Control Study On The Cryptojacking Ecosystem 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :515—520.
Illicitly hijacking visitors' computational resources for mining cryptocurrency via compromised websites is a consolidated activity.Previous works mainly focused on large-scale analysis of the cryptojacking ecosystem, technical means to detect browser-based mining as well as economic incentives of cryptojacking. So far, no one has studied if certain technical characteristics of a website can increase (decrease) the likelihood of being compromised for cryptojacking campaigns.In this paper, we propose to address this unanswered question by conducting a case-control study with cryptojacking websites obtained crawling the web using Minesweeper. Our preliminary analysis shows some association for certain website characteristics, however, the results obtained are not statistically significant. Thus, more data must be collected and further analysis must be conducted to obtain a better insight into the impact of these relations.
Tanana, D..  2020.  Behavior-Based Detection of Cryptojacking Malware. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :0543—0545.
With rise of cryptocurrency popularity and value, more and more cybercriminals seek to profit using that new technology. Most common ways to obtain illegitimate profit using cryptocurrencies are ransomware and cryptojacking also known as malicious mining. And while ransomware is well-known and well-studied threat which is obvious by design, cryptojacking is often neglected because it's less harmful and much harder to detect. This article considers question of cryptojacking detection. Brief history and definition of cryptojacking are described as well as reasons for designing custom detection technique. We also propose complex detection technique based on CPU load by an application, which can be applied to both browser-based and executable-type cryptojacking samples. Prototype detection program based on our technique was designed using decision tree algorithm. The program was tested in a controlled virtual machine environment and achieved 82% success rate against selected number of cryptojacking samples. Finally, we'll discuss generalization of proposed technique for future work.
2021-01-22
Mani, G., Pasumarti, V., Bhargava, B., Vora, F. T., MacDonald, J., King, J., Kobes, J..  2020.  DeCrypto Pro: Deep Learning Based Cryptomining Malware Detection Using Performance Counters. 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). :109—118.
Autonomy in cybersystems depends on their ability to be self-aware by understanding the intent of services and applications that are running on those systems. In case of mission-critical cybersystems that are deployed in dynamic and unpredictable environments, the newly integrated unknown applications or services can either be benign and essential for the mission or they can be cyberattacks. In some cases, these cyberattacks are evasive Advanced Persistent Threats (APTs) where the attackers remain undetected for reconnaissance in order to ascertain system features for an attack e.g. Trojan Laziok. In other cases, the attackers can use the system only for computing e.g. cryptomining malware. APTs such as cryptomining malware neither disrupt normal system functionalities nor trigger any warning signs because they simply perform bitwise and cryptographic operations as any other benign compression or encoding application. Thus, it is difficult for defense mechanisms such as antivirus applications to detect these attacks. In this paper, we propose an Operating Context profiling system based on deep neural networks-Long Short-Term Memory (LSTM) networks-using Windows Performance Counters data for detecting these evasive cryptomining applications. In addition, we propose Deep Cryptomining Profiler (DeCrypto Pro), a detection system with a novel model selection framework containing a utility function that can select a classification model for behavior profiling from both the light-weight machine learning models (Random Forest and k-Nearest Neighbors) and a deep learning model (LSTM), depending on available computing resources. Given data from performance counters, we show that individual models perform with high accuracy and can be trained with limited training data. We also show that the DeCrypto Profiler framework reduces the use of computational resources and accurately detects cryptomining applications by selecting an appropriate model, given the constraints such as data sample size and system configuration.
2020-07-10
Javed Butt, Usman, Abbod, Maysam, Lors, Anzor, Jahankhani, Hamid, Jamal, Arshad, Kumar, Arvind.  2019.  Ransomware Threat and its Impact on SCADA. 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3). :205—212.
Modern cybercrimes have exponentially grown over the last one decade. Ransomware is one of the types of malware which is the result of sophisticated attempt to compromise the modern computer systems. The governments and large corporations are investing heavily to combat this cyber threat against their critical infrastructure. It has been observed that over the last few years that Industrial Control Systems (ICS) have become the main target of Ransomware due to the sensitive operations involved in the day to day processes of these industries. As the technology is evolving, more and more traditional industrial systems are replaced with advanced industry methods involving advanced technologies such as Internet of Things (IoT). These technology shift help improve business productivity and keep the company's global competitive in an overflowing competitive market. However, the systems involved need secure measures to protect integrity and availability which will help avoid any malfunctioning to their operations due to the cyber-attacks. There have been several cyber-attack incidents on healthcare, pharmaceutical, water cleaning and energy sector. These ICS' s are operated by remote control facilities and variety of other devices such as programmable logic controllers (PLC) and sensors to make a network. Cyber criminals are exploring vulnerabilities in the design of these ICS's to take the command and control of these systems and disrupt daily operations until ransomware is paid. This paper will provide critical analysis of the impact of Ransomware threat on SCADA systems.
Muñoz, Jordi Zayuelas i, Suárez-Varela, José, Barlet-Ros, Pere.  2019.  Detecting cryptocurrency miners with NetFlow/IPFIX network measurements. 2019 IEEE International Symposium on Measurements Networking (M N). :1—6.

In the last few years, cryptocurrency mining has become more and more important on the Internet activity and nowadays is even having a noticeable impact on the global economy. This has motivated the emergence of a new malicious activity called cryptojacking, which consists of compromising other machines connected to the Internet and leverage their resources to mine cryptocurrencies. In this context, it is of particular interest for network administrators to detect possible cryptocurrency miners using network resources without permission. Currently, it is possible to detect them using IP address lists from known mining pools, processing information from DNS traffic, or directly performing Deep Packet Inspection (DPI) over all the traffic. However, all these methods are still ineffective to detect miners using unknown mining servers or result too expensive to be deployed in real-world networks with large traffic volume. In this paper, we present a machine learning-based method able to detect cryptocurrency miners using NetFlow/IPFIX network measurements. Our method does not require to inspect the packets' payload; as a result, it achieves cost-efficient miner detection with similar accuracy than DPI-based techniques.

Radhakrishnan, Kiran, Menon, Rajeev R, Nath, Hiran V.  2019.  A survey of zero-day malware attacks and its detection methodology. TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). :533—539.

The recent malware outbreaks have shown that the existing end-point security solutions are not robust enough to secure the systems from getting compromised. The techniques, like code obfuscation along with one or more zero-days, are used by malware developers for evading the security systems. These malwares are used for large-scale attacks involving Advanced Persistent Threats(APT), Botnets, Cryptojacking, etc. Cryptojacking poses a severe threat to various organizations and individuals. We are summarising multiple methods available for the detection of malware.

Nahmias, Daniel, Cohen, Aviad, Nissim, Nir, Elovici, Yuval.  2019.  TrustSign: Trusted Malware Signature Generation in Private Clouds Using Deep Feature Transfer Learning. 2019 International Joint Conference on Neural Networks (IJCNN). :1—8.

This paper presents TrustSign, a novel, trusted automatic malware signature generation method based on high-level deep features transferred from a VGG-19 neural network model pre-trained on the ImageNet dataset. While traditional automatic malware signature generation techniques rely on static or dynamic analysis of the malware's executable, our method overcomes the limitations associated with these techniques by producing signatures based on the presence of the malicious process in the volatile memory. Signatures generated using TrustSign well represent the real malware behavior during runtime. By leveraging the cloud's virtualization technology, TrustSign analyzes the malicious process in a trusted manner, since the malware is unaware and cannot interfere with the inspection procedure. Additionally, by removing the dependency on the malware's executable, our method is capable of signing fileless malware. Thus, we focus our research on in-browser cryptojacking attacks, which current antivirus solutions have difficulty to detect. However, TrustSign is not limited to cryptojacking attacks, as our evaluation included various ransomware samples. TrustSign's signature generation process does not require feature engineering or any additional model training, and it is done in a completely unsupervised manner, obviating the need for a human expert. Therefore, our method has the advantage of dramatically reducing signature generation and distribution time. The results of our experimental evaluation demonstrate TrustSign's ability to generate signatures invariant to the process state over time. By using the signatures generated by TrustSign as input for various supervised classifiers, we achieved 99.5% classification accuracy.

Yulianto, Arief Dwi, Sukarno, Parman, Warrdana, Aulia Arif, Makky, Muhammad Al.  2019.  Mitigation of Cryptojacking Attacks Using Taint Analysis. 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). :234—238.

Cryptojacking (also called malicious cryptocurrency mining or cryptomining) is a new threat model using CPU resources covertly “mining” a cryptocurrency in the browser. The impact is a surge in CPU Usage and slows the system performance. In this research, in-browsercryptojacking mitigation has been built as an extension in Google Chrome using Taint analysis method. The method used in this research is attack modeling with abuse case using the Man-In-The-Middle (MITM) attack as a testing for mitigation. The proposed model is designed so that users will be notified if a cryptojacking attack occurs. Hence, the user is able to check the script characteristics that run on the website background. The results of this research show that the taint analysis is a promising method to mitigate cryptojacking attacks. From 100 random sample websites, the taint analysis method can detect 19 websites that are infcted by cryptojacking.

Tahir, Rashid, Durrani, Sultan, Ahmed, Faizan, Saeed, Hammas, Zaffar, Fareed, Ilyas, Saqib.  2019.  The Browsers Strike Back: Countering Cryptojacking and Parasitic Miners on the Web. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :703—711.

With the recent boom in the cryptocurrency market, hackers have been on the lookout to find novel ways of commandeering users' machine for covert and stealthy mining operations. In an attempt to expose such under-the-hood practices, this paper explores the issue of browser cryptojacking, whereby miners are secretly deployed inside browser code without the knowledge of the user. To this end, we analyze the top 50k websites from Alexa and find a noticeable percentage of sites that are indulging in this exploitative exercise often using heavily obfuscated code. Furthermore, mining prevention plug-ins, such as NoMiner, fail to flag such cleverly concealed instances. Hence, we propose a machine learning solution based on hardware-assisted profiling of browser code in real-time. A fine-grained micro-architectural footprint allows us to classify mining applications with \textbackslashtextgreater99% accuracy and even flags them if the mining code has been heavily obfuscated or encrypted. We build our own browser extension and show that it outperforms other plug-ins. The proposed design has negligible overhead on the user's machine and works for all standard off-the-shelf CPUs.

Saad, Muhammad, Khormali, Aminollah, Mohaisen, Aziz.  2019.  Dine and Dash: Static, Dynamic, and Economic Analysis of In-Browser Cryptojacking. 2019 APWG Symposium on Electronic Crime Research (eCrime). :1—12.

Cryptojacking is the permissionless use of a target device to covertly mine cryptocurrencies. With cryptojacking attackers use malicious JavaScript codes to force web browsers into solving proof-of-work puzzles, thus making money by exploiting resources of the website visitors. To understand and counter such attacks, we systematically analyze the static, dynamic, and economic aspects of in-browser cryptojacking. For static analysis, we perform content-, currency-, and code-based categorization of cryptojacking samples to 1) measure their distribution across websites, 2) highlight their platform affinities, and 3) study their code complexities. We apply unsupervised learning to distinguish cryptojacking scripts from benign and other malicious JavaScript samples with 96.4% accuracy. For dynamic analysis, we analyze the effect of cryptojacking on critical system resources, such as CPU and battery usage. Additionally, we perform web browser fingerprinting to analyze the information exchange between the victim node and the dropzone cryptojacking server. We also build an analytical model to empirically evaluate the feasibility of cryptojacking as an alternative to online advertisement. Our results show a large negative profit and loss gap, indicating that the model is economically impractical. Finally, by leveraging insights from our analyses, we build countermeasures for in-browser cryptojacking that improve upon the existing remedies.

2020-04-17
Burgess, Jonah, Carlin, Domhnall, O'Kane, Philip, Sezer, Sakir.  2019.  MANiC: Multi-step Assessment for Crypto-miners. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1—8.

Modern Browsers have become sophisticated applications, providing a portal to the web. Browsers host a complex mix of interpreters such as HTML and JavaScript, allowing not only useful functionality but also malicious activities, known as browser-hijacking. These attacks can be particularly difficult to detect, as they usually operate within the scope of normal browser behaviour. CryptoJacking is a form of browser-hijacking that has emerged as a result of the increased popularity and profitability of cryptocurrencies, and the introduction of new cryptocurrencies that promote CPU-based mining. This paper proposes MANiC (Multi-step AssessmeNt for Crypto-miners), a system to detect CryptoJacking websites. It uses regular expressions that are compiled in accordance with the API structure of different miner families. This allows the detection of crypto-mining scripts and the extraction of parameters that could be used to detect suspicious behaviour associated with CryptoJacking. When MANiC was used to analyse the Alexa top 1m websites, it detected 887 malicious URLs containing miners from 11 different families and demonstrated favourable results when compared to related CryptoJacking research. We demonstrate that MANiC can be used to provide insights into this new threat, to identify new potential features of interest and to establish a ground-truth dataset, assisting future research.

2019-01-16
Rodriguez, Juan D. Parra, Posegga, Joachim.  2018.  RAPID: Resource and API-Based Detection Against In-Browser Miners. Proceedings of the 34th Annual Computer Security Applications Conference. :313–326.

Direct access to the system's resources such as the GPU, persistent storage and networking has enabled in-browser crypto-mining. Thus, there has been a massive response by rogue actors who abuse browsers for mining without the user's consent. This trend has grown steadily for the last months until this practice, i.e., CryptoJacking, has been acknowledged as the number one security threat by several antivirus companies. Considering this, and the fact that these attacks do not behave as JavaScript malware or other Web attacks, we propose and evaluate several approaches to detect in-browser mining. To this end, we collect information from the top 330.500 Alexa sites. Mainly, we used real-life browsers to visit sites while monitoring resourcerelated API calls and the browser's resource consumption, e.g., CPU. Our detection mechanisms are based on dynamic monitoring, so they are resistant to JavaScript obfuscation. Furthermore, our detection techniques can generalize well and classify previously unseen samples with up to 99.99% precision and recall for the benign class and up to 96% precision and recall for the mining class. These results demonstrate the applicability of detection mechanisms as a server-side approach, e.g., to support the enhancement of existing blacklists. Last but not least, we evaluated the feasibility of deploying prototypical implementations of some detection mechanisms directly on the browser. Specifically, we measured the impact of in-browser API monitoring on page-loading time and performed micro-benchmarks for the execution of some classifiers directly within the browser. In this regard, we ascertain that, even though there are engineering challenges to overcome, it is feasible and bene!cial for users to bring the mining detection to the browser.

2018-11-19
Rauchberger, Julian, Schrittwieser, Sebastian, Dam, Tobias, Luh, Robert, Buhov, Damjan, Pötzelsberger, Gerhard, Kim, Hyoungshick.  2018.  The Other Side of the Coin: A Framework for Detecting and Analyzing Web-Based Cryptocurrency Mining Campaigns. Proceedings of the 13th International Conference on Availability, Reliability and Security. :18:1–18:10.

Mining for crypto currencies is usually performed on high-performance single purpose hardware or GPUs. However, mining can be easily parallelized and distributed over many less powerful systems. Cryptojacking is a new threat on the Internet and describes code included in websites that uses a visitor's CPU to mine for crypto currencies without the their consent. This paper introduces MiningHunter, a novel web crawling framework which is able to detect mining scripts even if they obfuscate their malicious activities. We scanned the Alexa Top 1 million websites for cryptojacking, collected more than 13,400,000 unique JavaScript files with a total size of 246 GB and found that 3,178 websites perform cryptocurrency mining without their visitors' consent. Furthermore, MiningHunter can be used to provide an in-depth analysis of cryptojacking campaigns. To show the feasibility of the proposed framework, three of such campaigns are examined in detail. Our results provide the most comprehensive analysis to date of the spread of cryptojacking on the Internet.

Rüth, Jan, Zimmermann, Torsten, Wolsing, Konrad, Hohlfeld, Oliver.  2018.  Digging into Browser-Based Crypto Mining. Proceedings of the Internet Measurement Conference 2018. :70–76.

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.

Sempreboni, Diego, Viganò, Luca.  2018.  MMM: May I Mine Your Mind. Companion Proceedings of the The Web Conference 2018. :1573–1576.

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

Konoth, Radhesh Krishnan, Vineti, Emanuele, Moonsamy, Veelasha, Lindorfer, Martina, Kruegel, Christopher, Bos, Herbert, Vigna, Giovanni.  2018.  MineSweeper: An In-Depth Look into Drive-by Cryptocurrency Mining and Its Defense. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :1714–1730.

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.

Hong, Geng, Yang, Zhemin, Yang, Sen, Zhang, Lei, Nan, Yuhong, Zhang, Zhibo, Yang, Min, Zhang, Yuan, Qian, Zhiyun, Duan, Haixin.  2018.  How You Get Shot in the Back: A Systematical Study About Cryptojacking in the Real World. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :1701–1713.

As a new mechanism to monetize web content, cryptocurrency mining is becoming increasingly popular. The idea is simple: a webpage delivers extra workload (JavaScript) that consumes computational resources on the client machine to solve cryptographic puzzles, typically without notifying users or having explicit user consent. This new mechanism, often heavily abused and thus considered a threat termed "cryptojacking", is estimated to affect over 10 million web users every month; however, only a few anecdotal reports exist so far and little is known about its severeness, infrastructure, and technical characteristics behind the scene. This is likely due to the lack of effective approaches to detect cryptojacking at a large-scale (e.g., VirusTotal). In this paper, we take a first step towards an in-depth study over cryptojacking. By leveraging a set of inherent characteristics of cryptojacking scripts, we build CMTracker, a behavior-based detector with two runtime profilers for automatically tracking Cryptocurrency Mining scripts and their related domains. Surprisingly, our approach successfully discovered 2,770 unique cryptojacking samples from 853,936 popular web pages, including 868 among top 100K in Alexa list. Leveraging these samples, we gain a more comprehensive picture of the cryptojacking attacks, including their impact, distribution mechanisms, obfuscation, and attempts to evade detection. For instance, a diverse set of organizations benefit from cryptojacking based on the unique wallet ids. In addition, to stay under the radar, they frequently update their attack domains (fastflux) on the order of days. Many attackers also apply evasion techniques, including limiting the CPU usage, obfuscating the code, etc.

Carlin, D., O'Kane, P., Sezer, S., Burgess, J..  2018.  Detecting Cryptomining Using Dynamic Analysis. 2018 16th Annual Conference on Privacy, Security and Trust (PST). :1–6.

With the rise in worth and popularity of cryptocurrencies, a new opportunity for criminal gain is being exploited and with little currently offered in the way of defence. The cost of mining (i.e., earning cryptocurrency through CPU-intensive calculations that underpin the blockchain technology) can be prohibitively expensive, with hardware costs and electrical overheads previously offering a loss compared to the cryptocurrency gained. Off-loading these costs along a distributed network of machines via malware offers an instantly profitable scenario, though standard Anti-virus (AV) products offer some defences against file-based threats. However, newer fileless malicious attacks, occurring through the browser on seemingly legitimate websites, can easily evade detection and surreptitiously engage the victim machine in computationally-expensive cryptomining (cryptojacking). With no current academic literature on the dynamic opcode analysis of cryptomining, to the best of our knowledge, we present the first such experimental study. Indeed, this is the first such work presenting opcode analysis on non-executable files. Our results show that browser-based cryptomining within our dataset can be detected by dynamic opcode analysis, with accuracies of up to 100%. Further to this, our model can distinguish between cryptomining sites, weaponized benign sites, de-weaponized cryptomining sites and real world benign sites. As it is process-based, our technique offers an opportunity to rapidly detect, prevent and mitigate such attacks, a novel contribution which should encourage further future work.

Eskandari, S., Leoutsarakos, A., Mursch, T., Clark, J..  2018.  A First Look at Browser-Based Cryptojacking. 2018 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :58–66.

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.