Some smart contracts violate decentralization principles by defining privileged accounts that manage other users' assets without permission, introducing centralization risks that have caused financial losses. Existing methods, however, face challenges in accurately detecting diverse centralization risks due to their dependence on predefined behavior patterns. In this paper, we propose JANUS, an automated analyzer for Solidity smart contracts that detects financial centralization risks independently of their specific behaviors. JANUS identifies differences between states reached by privileged and ordinary accounts, and analyzes whether these differences are finance-related. Focusing on the impact of risks rather than behaviors, JANUS achieves improved accuracy compared to existing tools and can uncover centralization risks with unknown patterns. To evaluate JANUS's performance, we compare it with other tools using a dataset of 540 contracts. Our evaluation demonstrates that JANUS outperforms representative tools in terms of detection accuracy for financial centralization risks . Additionally, we evaluate JANUS on a real-world dataset of 33,151 contracts, successfully identifying two types of risks that other tools fail to detect. We also prove that the state traversal method and variable summaries, which are used in JANUS to reduce the number of states to be compared, do not introduce false alarms or omissions in detection.

Behavioral analytics is quickly becoming the cornerstone of most every Infosec technology. However, it takes a lot more than simply analyzing user activity with rules and statistics, it takes applying ML (Machine Learning) to access and activity data, as well as employing AI (Artificial Intelligence) to reduce false positives and accurately risk score. Two critical capabilities that a multitude of security vendors have yet to address in their products to enable automated risk response. Those lacking machine-based cognitive abilities have come to rely on static pattern definitions, signatures and policies for a legacy world of known good and bad. Today, we must assume compromise and assess risk, even more importantly for privileged accounts with the access keys to IT environments.

In an unassuming building on the outskirts of Budapest engineers working for small Hungarian security firm BalaBit have spent the last three years working on technology its makers are convinced can contain one of cybersecurity's most intractable woes. In 2014 the relatively unknown firm launched a system called Blindspotter which, as its name suggests, gives its customers mostly in finance and telco sector buyers the ability to see things most networks barely acknowledge as existing let alone attempt to look for. Blindspotter is designed to watch what network users are doing in a lot of detail, a boon for organisations that worry about user credentials being abused, either deliberately from within by attackers who've somehow pilfered them. When used in conjunction with the firm's network proxy appliance, Shell Control Box (SCB), organisations suddenly have the ability to monitor their whole infrastructure using measurements of user behaviour rather than packets, ports and protocols. The system's real intrigue isn't what it does – cybersecurity is already chock full of network monitoring in different forms – so much as how it does it.