Central nodes are critical in establishing structural connectivity in a complex network. Attacking such nodes can create real havoc in a complex system. We propose attack strategies based on four types of centers, namely betweenness center, degree center, median, and center. We study the vulnerability of synthetic as well as real-world networks in these center-based attacks. These attacks are node-removal attacks which involve identifying the central node set and removing them from the network. We observed that the attacks based on recalculated network information are more efficient than ones based on initial network information. This work shows that the median-based attack, which is a novel strategy proposed in this work, is highly destructive in real as well as synthetic networks.
Attack vulnerability is also an indication of many structural and dynamic features of networks. @cite_16 , in 2004, studied the hierarchy of complex networks based on its vulnerability meaures. Thus the quantification of vulnerability measures turn out to be of relevance in network analysis. , in 2007, proposed measures to quantify the multiscale evaluation of vulnerability @cite_17 . Mishkovski et. al. @cite_10 , in 2011, proposed a metric for vulnerability measurement. This metric was based on average edge betweenness.
@cite_16 @cite_10 @cite_17
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We present a novel approach to quantify the vulnerability of a complex network, i.e., the capacity of a graph to maintain its functional performance under random damages or malicious attacks. The proposed measure represents a multiscale evaluation of vulnerability, and makes use of combined powers of the links’ betweenness. We show that the proposed approach is able to properly describe some cases for which earlier measures of vulnerability fail. The relevant applications of our method for technological network design are outlined. An unpatched vulnerability can lead to security breaches. When a new vulnerability is discovered, it needs to be assessed so that it can be prioritized. A major challenge in software security is the assessment of the potential risk due to vulnerability exploitability. CVSS metrics have become a de facto standard that is commonly used to assess the severity of a vulnerability. The CVSS Base Score measures severity based on exploitability and impact measures. CVSS exploitability is measured based on three metrics: Access Vector, Authentication, and Access Complexity. However, CVSS exploitability measures assign subjective numbers based on the views of experts. Two of its factors, Access Vector and Authentication, are the same for almost all vulnerabilities. CVSS does not specify how the third factor, Access Complexity, is measured, and hence we do not know if it considers software properties as a factor. In this paper, we propose an approach that assesses the risk of vulnerability exploitability based on two software properties - attack surface entry points and reach ability analysis. A vulnerability is reachable if it is located in one of the entry points or is located in a function that is called either directly or indirectly by the entry points. The likelihood of an entry point being used in an attack can be assessed by using damage potential-effort ratio in the attack surface metric and the presence of system calls deemed dangerous. To illustrate the proposed method, five reported vulnerabilities of Apache HTTP server 1.3.0 have been examined at the source code level. The results show that the proposed approach, which uses more detailed information, can yield a risk assessment that can be different from the CVSS Base Score. Measuring the impact of network attack is an important issue in network science. In this paper, we study the impact of maximal vertex coverage (MVC) attack in large complex networks, where the attacker aims at deleting as many edges of the network as possible by attacking a small fraction of nodes. First, we present two metrics to measure the impact of MVC attack. To compute these metrics, we propose an efficient randomized greedy algorithm with near-optimal performance guarantee. Second, we generalize the MVC attack into an uncertain setting, in which a node is deleted by the attacker with a prior probability. We refer to the MVC attack under such uncertain environment as the probabilistic MVC attack. Based on the probabilistic MVC attack, we propose two adaptive metrics, and then present an adaptive greedy algorithm for calculating such metrics accurately and efficiently. Finally, we conduct extensive experiments on 20 real datasets. The results show that P2P and co-authorship networks are extremely robust under the MVC attack while both the online social networks and the Email communication networks exhibit vulnerability under the MVC attack. In addition, the results demonstrate the efficiency and effectiveness of the proposed algorithms for computing the proposed metrics. Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. Existing security metrics have generally focused on measuring individual vulnerabilities without considering their combined effects. Our previous work tackle this issue by exploring the causal relationships between vulnerabilities encoded in an attack graph. However, the evolving nature of vulnerabilities and networks has largely been ignored. In this paper, we propose a Dynamic Bayesian Networks (DBNs)-based model to incorporate temporal factors, such as the availability of exploit codes or patches. Starting from the model, we study two concrete cases to demonstrate the potential applications. This novel model provides a theoretical foundation and a practical framework for continuously measuring network security in a dynamic environment.