Biblio
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.
In the development process of critical systems, one of the main challenges is to provide early system validation and verification against vulnerabilities in order to reduce cost caused by late error detection. We propose in this paper an approach that, firstly allows formally describe system security specifications, thanks to our suggested extended attack tree. Secondly, static and dynamic system modeling by using a SysML connectivity profile to model error propagation is introduced. Finally, a model checker has been used in order to validate system specifications.
The paper suggests several techniques for computer network risk assessment based on Common Vulnerability Scoring System (CVSS) and attack modeling. Techniques use a set of integrated security metrics and consider input data from security information and event management (SIEM) systems. Risk assessment techniques differ according to the used input data. They allow to get risk assessment considering requirements to the accuracy and efficiency. Input data includes network characteristics, attacks, attacker characteristics, security events and countermeasures. The tool that implements these techniques is presented. Experiments demonstrate operation of the techniques for different security situations.
Attack graphs are a powerful modeling technique with which to explore the attack surface of a system. However, they can be difficult to generate due to the exponential growth of the state space, often times making exhaustive search impractical. This paper discusses an approach for generating large attack graphs with an emphasis on scalable generation over a distributed system. First, a serial algorithm is presented, highlighting bottlenecks and opportunities to exploit inherent concurrency in the generation process. Then a strategy to parallelize this process is presented. Finally, we discuss plans for future work to implement the parallel algorithm using a hybrid distributed/shared memory programming model on a heterogeneous compute node cluster.