Biblio

Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.

Large scale cloud networks consist of distributed networking and computing elements that process critical information and thus security is a key requirement for any environment. Unfortunately, assessing the security state of such networks is a challenging task and the tools used in the past by security experts such as packet filtering, firewall, Intrusion Detection Systems (IDS) etc., provide a reactive security mechanism. In this paper, we introduce a Moving Target Defense (MTD) based proactive security framework for monitoring attacks which lets us identify and reason about multi-stage attacks that target software vulnerabilities present in a cloud network. We formulate the multi-stage attack scenario as a two-player zero-sum Markov Game (between the attacker and the network administrator) on attack graphs. The rewards and transition probabilities are obtained by leveraging the expert knowledge present in the Common Vulnerability Scoring System (CVSS). Our framework identifies an attacker's optimal policy and places countermeasures to ensure that this attack policy is always detected, thus forcing the attacker to use a sub-optimal policy with higher cost.

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.

Business or military missions are supported by hardware and software systems. Unanticipated cyber activities occurring in supporting systems can impact such missions. In order to quantify such impact, we describe a layered graphical model as an extension of forensic investigation. Our model has three layers: the upper layer models operational tasks that constitute the mission and their inter-dependencies. The middle layer reconstructs attack scenarios from available evidence to reconstruct their inter-relationships. In cases where not all evidence is available, the lower level reconstructs potentially missing attack steps. Using the three levels of graphs constructed in these steps, we present a method to compute the impacts of attack activities on missions. We use NIST National Vulnerability Database's (NVD)-Common Vulnerability Scoring System (CVSS) scores or forensic investigators' estimates in our impact computations. We present a case study to show the utility of our model.