Biblio

The contemporary struggle that rests upon security risk assessment of Information Systems is its feasibility in the presence of an indeterminate environment when information is insufficient, conflicting, generic or ambiguous. But as pointed out by the security experts, most of the traditional approaches to risk assessment of information systems security are no longer practicable as they fail to deliver viable support on handling uncertainty. Therefore, to address this issue, we have anticipated a comprehensive risk assessment model based on Bayesian Belief Network (BBN) and Fuzzy Inference Scheme (FIS) process to function in an indeterminate environment. The proposed model is demonstrated and further comparisons are made on the test results to validate the reliability of the proposed model.

In the existing situation, most of the business process are running through web applications. This helps the enterprises to grow their business efficiently which creates a good consumer relationship. But the main problem is that they failed to provide a vulnerable free environment. To overcome this issue in web applications, vulnerability assessment should be made periodically. They are many vulnerability assessment methodologies which occur earlier are not much proactive. So, machine learning is needed to provide a combined solution to determine vulnerability occurrence and percentage of vulnerability occurred in logical web pages. We use Decision Tree and Bayesian Belief Network (BBN) as a collective solution to find either vulnerability occur in web applications and the vulnerability occurred percentage on different logical web pages.

Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain.

Ransomwares have become a growing threat since 2012, and the situation continues to worsen until now. The lack of security mechanisms and security awareness are pushing the systems into mire of ransomware attacks. In this paper, a new framework called 2entFOX' is proposed in order to detect high survivable ransomwares (HSR). To our knowledge this framework can be considered as one of the first frameworks in ransomware detection because of little publicly-available research in this field. We analyzed Windows ransomwares' behaviour and we tried to find appropriate features which are particular useful in detecting this type of malwares with high detection accuracy and low false positive rate. After hard experimental analysis we extracted 20 effective features which due to two highly efficient ones we could achieve an appropriate set for HSRs detection. After proposing architecture based on Bayesian belief network, the final evaluation is done on some known ransomware samples and unknown ones based on six different scenarios. The result of this evaluations shows the high accuracy of 2entFox in detection of HSRs.