Emerging blockchain and cryptocurrency-based technologies are redefining the way we conduct business in cyberspace. Today, a myriad of blockchain and cryp-tocurrency systems, applications, and technologies are widely available to companies, end-users, and even malicious actors who want to exploit the computational resources of regular users through cryptojacking malware. Especially with ready-to-use mining scripts easily provided by service providers (e.g., Coinhive) and untraceable cryptocurrencies (e.g., Monero), cryptojacking malware has become an indispensable tool for attackers. Indeed, the banking industry, major commercial websites, government and military servers (e.g., US Dept. of Defense), online video sharing platforms (e.g., Youtube), gaming platforms (e.g., Nintendo), critical infrastructure resources (e.g., routers), and even recently widely popular remote video conferencing/meeting programs (e.g., Zoom during the Covid-19 pandemic) have all been the victims of powerful cryptojacking malware campaigns. Nonetheless, existing detection methods such as browser extensions that protect users with blacklist methods or antivirus programs with different analysis methods can only provide a partial panacea to this emerging crypto-jacking issue as the attackers can easily bypass them by using obfuscation techniques or changing their domains or scripts frequently. Therefore, many studies in the literature proposed cryptojacking malware detection methods using various dynamic/behavioral features. However, the literature lacks a systemic study with a deep understanding of the emerging cryptojacking malware and a comprehensive review of studies in the literature. To fill this gap in the literature, in this SoK paper, we present a systematic overview of cryptojacking malware based on the information obtained from the combination of academic research papers, two large cryptojacking datasets of samples, and 45 major attack instances. Finally, we also present lessons learned and new research directions to help the research community in this emerging area.

Mixed integer linear programming (MILP) models are applied extensively in the field of cryptanalysis. Finding the minimum number of active S-boxes and the best differential characteristic in a differential attack are two main problems examined using the MILP approach. In this study, KLEIN and PRINCE block ciphers are modeled with MILP to search for an exact solution to these problems. Both ciphers contain matrix multiplication operations, which can be calculated using multiple xor operations. The standard MILP model for multiple xors increases the number of variables significantly, which extends the solution time. In this work, an alternative xor model is proposed using fewer variables than the standard xor model. The new model is much more efficient in terms of the number of variables involved and the execution time. Using the new model, we analyze the differential properties of KLEIN and PRINCE. We obtain the exact minimum number of active S-boxes of these ciphers with full rounds and also discover the best differential characteristics for various numbers of rounds. For KLEIN and PRINCE ciphers we achieve the best single differential characteristic of probability 2(-56). These results improve the best single-key differential attacks on these ciphers in the literature.

Secure socket layer/transport layer security (TLS) is the de facto protocol for providing secure communications over the Internet. It relies on the web PKImodel for authentication and secure key exchange. Despite its relatively successful past, the number of web PKI incidents observed have increased recently. These incidents revealed the risks of forged certificates issued by certificate authorities without the consent of the domain owners. Several solutions have been proposed to solve this problem but no solution has yet received widespread adoption due to complexity and deployability issues. In this study, the authors proposean effective solution for this problem that allows a TLS server to detect a certificate substitution attack against its domain across the Internet. The proposed solution is practical and allows a smooth and gradual transition. They also give a triangulation algorithm enabling the server to find out the origin of the attack. They conducted simulation experiments using real‐world BGPdata and showed that their proposal can be effective for detecting and locating attacks using relatively few vantage points over the Internet.

Abstract For commitments on secrets, statistical hiding is a must when we are dealing with a long‐term secret or when the secret domain is small enough for a brute‐force attack by a powerful adversary. Unfortunately, all the Chinese Remainder Theorem‐based verifiable secret sharing schemes in the literature are either insecure or suffer from the vulnerability of computationally hiding commitments. To the best of our knowledge, there exist five such studies where two of them were already proven to be insecure. In this work, we first show that two of the remaining schemes are also insecure, that is, the schemes reveal information on the secret even when the adversary is passive. In addition, the remaining one is only secure against a computationally bounded adversary which can be a problem for secret sharing schemes requiring long‐term secret obscurity or using small secret domain. We propose a modification for the latter scheme and prove that the modified scheme is a secure verifiable secret sharing scheme against an unbounded adversary. Lastly, as an application, we show how to use the new scheme for joint random secret sharing and analyze the practicality and efficiency of the proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.

SSL/TLS is the most widely used cryptographic protocol on the Internet today. Despite having a reasonably good design and a practical commercial model behind it, there are several points where SSL fails in practice. In this talk, we will discuss the security of the SSL protocol in real life.

The open and anonymous nature of a P2P network makes it an ideal medium for attackers to spread malicious content. In this paper we describe a reputation-based trust management protocol for P2P networks where users rate the reliability of parties they deal with, and share this information with their peers. The protocol helps establish trust among good peers as well as identifying the malicious ones. Results of various simulation experiments show that the proposed system can be highly effective in preventing the spread of malicious content in P2P networks.