IAVS: Intelligent Active Network Vulnerability Scanner

Network security needs to be assured through runtime active evaluating and assessment. However, active vulnerability scanners suffer from serious deficiencies such as heavy scan traffic during the reconnaissance phase, uncertainty in the environment, and heavy reliance on experts. Generating a blind heavy load of attack packets not only causes usage of network resources, but it also increases the probability of detection by target defense systems and causes failure in finding vulnerabilities. Furthermore, environmental uncertainty increases pointless attempts of vulnerability scanners, which wastes time. Utilizing a decision-making method devised for uncertainty conditions, we present Intelligent Active Network Vulnerability Scanner (IAVS). IAVS is implemented as an extension on Hail Mary, the automatic execution mechanism in the Metasploit toolkit. IAVS learns from previous vulnerability exploitation attempts to select exploit codes purposefully. IAVS not only reduces the role of experts in the process of vulnerability testing, but it also decreases the volume of scanning requests during the reconnaissance phase by integrating the reconnaissance and exploitation phases. Our experimental results indicate a successful decrease in failed attempts. It is also demonstrated that improvements in the results of IAVS correspond directly to the rate of similarity among different vulnerabilities in systems of the target network; that is, the higher the similarity, the better the results of IAVS. Our experiments compared the results of IAVS and those of Hail Mary without the IAVS extension; these results show that IAVS improved Hail Marys successful attempts by around 37%.

A Look at the Time Delays in CVSS Vulnerability Scoring

This empirical paper examines the time delays that occur between the publication of Common Vulnerabilities and Exposures (CVEs) in the National Vulnerability Database (NVD) and the Common Vulnerability Scoring System (CVSS) information attached to published CVEs. According to the empirical results based on regularized regression analysis of over eighty thousand archived vulnerabilities, (i) the CVSS content does not statistically influence the time delays, which, however, (ii) are strongly affected by a decreasing annual trend. In addition to these results, the paper contributes to the empirical research tradition of software vulnerabilities by a couple of insights on misuses of statistical methodology.</p

Calculating Common Vulnerability Scoring System’s Environmental Metrics Using Context-Aware Network Graphs

Software vulnerabilities have significant costs associated with them. To aid in the prioritization of vulnerabilities, analysts often utilize Common Vulnerability Scoring System’s Base severity scores. However, the Base scores provided from the National Vulnerability Database are subjective and may incorrectly convey the severity of the vulnerability in an organization\u27s network. This thesis proposes a method to statically analyze context-aware network graphs to increase accuracy of CVSS severity scores. Through experimentation of the proposed methodology, it is determined that context-aware network graphs can capture the required metrics to generate modified severity scores. The proposed approach has some accuracy to it, but leaves room for additional network context to further refine Environmental severity scores

A Relevance Model for Threat-Centric Ranking of Cybersecurity Vulnerabilities

The relentless and often haphazard process of tracking and remediating vulnerabilities is a top concern for cybersecurity professionals. The key challenge they face is trying to identify a remediation scheme specific to in-house, organizational objectives. Without a strategy, the result is a patchwork of fixes applied to a tide of vulnerabilities, any one of which could be the single point of failure in an otherwise formidable defense. This means one of the biggest challenges in vulnerability management relates to prioritization. Given that so few vulnerabilities are a focus of real-world attacks, a practical remediation strategy is to identify vulnerabilities likely to be exploited and focus efforts towards remediating those vulnerabilities first. The goal of this research is to demonstrate that aggregating and synthesizing readily accessible, public data sources to provide personalized, automated recommendations that an organization can use to prioritize its vulnerability management strategy will offer significant improvements over what is currently realized using the Common Vulnerability Scoring System (CVSS). We provide a framework for vulnerability management specifically focused on mitigating threats using adversary criteria derived from MITRE ATT&CK. We identify the data mining steps needed to acquire, standardize, and integrate publicly available cyber intelligence data sets into a robust knowledge graph from which stakeholders can infer business logic related to known threats. We tested our approach by identifying vulnerabilities in academic and common software associated with six universities and four government facilities. Ranking policy performance was measured using the Normalized Discounted Cumulative Gain (nDCG). Our results show an average 71.5% to 91.3% improvement towards the identification of vulnerabilities likely to be targeted and exploited by cyber threat actors. The ROI of patching using our policies resulted in a savings in the range of 23.3% to 25.5% in annualized unit costs. Our results demonstrate the efficiency of creating knowledge graphs to link large data sets to facilitate semantic queries and create data-driven, flexible ranking policies. Additionally, our framework uses only open standards, making implementation and improvement feasible for cyber practitioners and academia

Vulnerability modelling and mitigation strategies for hybrid networks

Hybrid networks nowadays consist of traditional IT components, Internet of Things (IoT) and industrial control systems (ICS) nodes with varying characteristics, making them genuinely heterogeneous in nature. Historically evolving from traditional internet-enabled IT servers, hybrid networks allow organisations to strengthen cybersecurity, increase flexibility, improve efficiency, enhance reliability, boost remote connectivity and easy management. Though hybrid networks offer significant benefits from business and operational perspectives, this integration has increased the complexity and security challenges to all connected nodes. The IT servers of these hybrid networks are high-budget devices with tremendous processing power and significant storage capacity. In contrast, IoT nodes are low-cost devices with limited processing power and capacity. In addition, the ICS nodes are programmed for dedicated functions with the least interference. The available cybersecurity solutions for hybrid networks are either for specific node types or address particular weaknesses. Due to these distinct characteristics, these solutions may place other nodes in vulnerable positions. This study addresses this gap by proposing a comprehensive vulnerability modelling and mitigation strategy. This proposed solution equally applies to each node type of hybrid network while considering their unique characteristics. For this purpose, the industry-wide adoption of the Common Vulnerability Scoring System (CVSS) has been extended to embed the distinct characteristics of each node type in a hybrid network. To embed IoT features, the ‘attack vectors’ and ‘attack complexity vectors’ are modified and another metric “human safety index”, is integrated in the ‘Base metric group’ of CVSS. In addition, the ICS related characteristics are included in the ‘Environmental metric group’ of CVSS. This metric group is further enhanced to reflect the node resilience capabilities when evaluating the vulnerability score. The resilience of a node is evaluated by analysing the complex relationship of numerous contributing cyber security factors and practices. The evolved CVSSR-IoT-ICS framework proposed in the thesis measures the given vulnerabilities by adopting the unique dynamics of each node. These vulnerability scores are then mapped in the attack tree to reveal the critical nodes and shortest path to the target node. The mitigating strategy framework suggests the most efficient mitigation strategy to counter vulnerabilities by examining the node’s functionality, its locality, centrality, criticality, cascading impacts, available resources, and performance thresholds. Various case studies were conducted to analyse and evaluate our proposed vulnerability modelling and mitigation strategies on realistic supply chain systems. These analyses and evaluations confirm that the proposed solutions are highly effective for modelling the vulnerabilities while the mitigation strategies reduce the risks in dynamic and resource-constrained environments. The unified vulnerability modelling of hybrid networks minimises ambiguities, reduces complexities and identifies hidden deficiencies. It also improves system reliability and performance of heterogeneous networks while at the same time gaining acceptance for a universal vulnerability modelling framework across the cyber industry. The contributions have been published in reputable journals and conferences.Doctor of Philosoph

Quantitative Evaluation and Reevaluation of Security in Services

Services are software components or systems designed to support interoperable machine or application-oriented interaction over a network. The popularity of services grows because they are easily accessible, very flexible, provide reach functionality, and can constitute more complex services. During the service selection, the user considers not only functional requirements to a service but also security requirements. The user would like to be aware that security of the service satisfies security requirements before starting the exploitation of the service, i.e., before the service is granted to access assets of the user. Moreover, the user wants to be sure that security of the service satisfies security requirements during the exploitation which may last for a long period. Pursuing these two goals require security of the service to be evaluated before the exploitation and continuously reevaluated during the exploitation. This thesis aims at a framework consisting of several quantitative methods for evaluation and continuous reevaluation of security in services. The methods should help a user to select a service and to control the service security level during the exploitation. The thesis starts with the formal model for general quantitative security metrics and for risk that may be used for the evaluation of security in services. Next, we adjust the computation of security metrics with a refined model of an attacker. Then, the thesis proposes a general method for the evaluation of security of a complex service composed from several simple services using different security metrics. The method helps to select the most secure design of the complex service. In addition, the thesis describes an approach based on the Usage Control (UCON) model for continuous reevaluation of security in services. Finally, the thesis discusses several strategies for a cost-effective decision making in the UCON unde

CVSS attack graphs

International audienceAttack models and attack graphs are efficient tools to describe and analyse attack scenarios aimed at computer networks. More precisely, attack graphs give all possible scenarios for an attacker to reach a certain goal, exploiting vulnerabilities of the targeted network. Nevertheless they give no information about the damages induced by these attacks, nor about the probability of exploitation of these scenarios. In this paper, we propose to combine attack graphs and CVSS framework, in order to add damage and exploitability probability information. Then, we define a notion of risk for each attack scenario, which is based on quantitative information added to attack graphs