Biblio
Smart contracts have been widely used on Ethereum to enable business services across various application domains. However, they are prone to different forms of security attacks due to the dynamic and non-deterministic blockchain runtime environment. In this work, we highlighted a general miner-side type of exploit, called concurrency exploit, which attacks smart contracts via generating malicious transaction sequences. Moreover, we designed a systematic algorithm to automatically detect such exploits. In our preliminary evaluation, our approach managed to identify real vulnerabilities that cannot be detected by other tools in the literature.
This paper demonstrates how the Insider Threat Cybersecurity Framework (ITCF) web tool and methodology help provide a more dynamic, defense-in-depth security posture against insider cyber and cyber-physical threats. ITCF includes over 30 cybersecurity best practices to help organizations identify, protect, detect, respond and recover to sophisticated insider threats and vulnerabilities. The paper tests the efficacy of this approach and helps validate and verify ITCF's capabilities and features through various insider attacks use-cases. Two case-studies were explored to determine how organizations can leverage ITCF to increase their overall security posture against insider attacks. The paper also highlights how ITCF facilitates implementation of the goals outlined in two Presidential Executive Orders to improve the security of classified information and help owners and operators secure critical infrastructure. In realization of these goals, ITCF: provides an easy to use rapid assessment tool to perform an insider threat self-assessment; determines the current insider threat cybersecurity posture; defines investment-based goals to achieve a target state; connects the cybersecurity posture with business processes, functions, and continuity; and finally, helps develop plans to answer critical organizational cybersecurity questions. In this paper, the webtool and its core capabilities are tested by performing an extensive comparative assessment over two different high-profile insider threat incidents.
Phishing has increased tremendously over last few years and it has become a serious threat to global security and economy. Existing literature dealing with the problem of phishing is scarce. Phishing is a deception technique that uses a combination of technology and social engineering to acquire sensitive information such as online banking passwords, credit card or bank account details [2]. Phishing can be done through emails and websites to collect confidential information. Phishers design fraudulent websites which look similar to the legitimate websites and lure the user to visit the malicious website. Therefore, the users must be aware of malicious websites to protect their sensitive data [1]. But it is very difficult to distinguish between legitimate and fake website especially for nontechnical users [4]. Moreover, phishing sites are growing rapidly. The aim of this paper is to demonstrate phishing detection using fuzzy logic and interpreting results using different defuzzification methods.
The article considers the approach to static analysis of program code and the general principles of static analyzer operation. The authors identify the most important syntactic and semantic information in the programs, which can be used to find errors in the source code. The general methodology for development of diagnostic rules is proposed, which will improve the efficiency of static code analyzers.