Biblio

Software security is an important concern in the world moving towards Information Technology. Detecting software vulnerabilities is a difficult and resource consuming task. Therefore, automatic vulnerability prediction would help development teams to predict vulnerability-prone components and prioritize security inspection efforts. Software source code metrics and data mining techniques have been recently used to predict vulnerability-prone components. Some of previous studies used a set of unit complexity and coupling metrics to predict vulnerabilities. In this study, first, we compare the predictability power of these two groups of metrics in cross-project vulnerability prediction. In cross-project vulnerability prediction we create the prediction model based on datasets of completely different projects and try to detect vulnerabilities in another project. The experimental results show that unit complexity metrics are stronger vulnerability predictors than coupling metrics. Then, we propose a new set of coupling metrics which are called Included Vulnerable Header (IVH) metrics. These new coupling metrics, which consider interaction of application modules with outside of the application, predict vulnerabilities highly better than regular coupling metrics. Furthermore, adding IVH metrics to the set of complexity metrics improves Recall of the best predictor from 60.9% to 87.4% and shows the best set of metrics for cross-project vulnerability prediction.

Most cyber network attacks begin with an adversary gaining a foothold within the network and proceed with lateral movement until a desired goal is achieved. The mechanism by which lateral movement occurs varies but the basic signature of hopping between hosts by exploiting vulnerabilities is the same. Because of the nature of the vulnerabilities typically exploited, lateral movement is very difficult to detect and defend against. In this paper we define a dynamic reachability graph model of the network to discover possible paths that an adversary could take using different vulnerabilities, and how those paths evolve over time. We use this reachability graph to develop dynamic machine-level and network-level impact scores. Lateral movement mitigation strategies which make use of our impact scores are also discussed, and we detail an example using a freely available data set.

Human mobility is one of the key topics to be considered in the networks of the future, both by industrial and research communities that are already focused on multidisciplinary applications and user-centric systems. If the rapid proliferation of networks and high-tech miniature sensors makes this reality possible, the ever-growing complexity of the metrics and parameters governing such systems raises serious issues in terms of privacy, security and computing capability. In this demonstration, we show a new system, able to estimate a user's mobility profile based on anonymized and lightweight smartphone data. In particular, this system is composed of (1) a web analytics platform, able to analyze multimodal sensing traces and improve our understanding of complex mobility patterns, and (2) a smartphone application, able to show a user's profile generated locally in the form of a spider graph. In particular, this application uses anonymized and privacy-friendly data and methods, obtained thanks to the combination of Wi-Fi traces, activity detection and graph theory, made available independent of any personal information. A video showing the different interfaces to be presented is available online.

The Software Assurance Metrics and Tool Evaluation (SAMATE) project at the National Institute of Standards and Technology (NIST) has created the Software Assurance Reference Dataset (SARD) to provide researchers and software security assurance tool developers with a set of known security flaws. As part of an empirical evaluation of a runtime monitoring framework, two test suites were executed and monitored, revealing deficiencies which led to a collaboration with the NIST SAMATE team to provide replacements. Test Suites 45 and 46 are analyzed, discussed, and updated to improve accuracy, consistency, preciseness, and automation. Empirical results show metrics such as recall, precision, and F-Measure are all impacted by invalid base assumptions regarding the test suites.

The Software Assurance Metrics and Tool Evaluation (SAMATE) project at the National Institute of Standards and Technology (NIST) has created the Software Assurance Reference Dataset (SARD) to provide researchers and software security assurance tool developers with a set of known security flaws. As part of an empirical evaluation of a runtime monitoring framework, two test suites were executed and monitored, revealing deficiencies which led to a collaboration with the NIST SAMATE team to provide replacements. Test Suites 45 and 46 are analyzed, discussed, and updated to improve accuracy, consistency, preciseness, and automation. Empirical results show metrics such as recall, precision, and F-Measure are all impacted by invalid base assumptions regarding the test suites.

While attacks on information systems have for most practical purposes binary outcomes (information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we study if attack-detection can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.

In this paper, we propose a new risk analysis framework that enables to supervise risks in complex and distributed systems. Our contribution is twofold. First, we provide the Risk Assessment Graphs (RAGs) as a model of risk analysis. This graph-based model is adaptable to the system changes over the time. We also introduce the potentiality and the accessibility functions which, during each time slot, evaluate respectively the chance of exploiting the RAG's nodes, and the connection time between these nodes. In addition, we provide a worst-case risk evaluation approach, based on the assumption that the intruder threats usually aim at maximising their benefits by inflicting the maximum damage to the target system (i.e. choosing the most likely paths in the RAG). We then introduce three security metrics: the propagated risk, the node risk and the global risk. We illustrate the use of our framework through the simple example of an enterprise email service. Our framework achieves both flexibility and generality requirements, it can be used to assess the external threats as well as the insider ones, and it applies to a wide set of applications.

In this paper, we analyze the performance and cost trade-off from selecting two representations of nodes when implementing the Aho-Corasick algorithm. This algorithm can be used for pattern matching in network-based intrusion detection systems such as Snort. Our analysis uses the Snort 2.9.7 rules set, which contains almost 26k patterns. Our methodology consists of code profiling and analysis, followed by the selection of a parameter to maximize a metric that combines clock cycles count and memory usage. The parameter determines which of two types of nodes is selected for each trie node. We show that it is possible to select the parameter to optimize the metric, which results in an improvement by up to 12× compared with the single node-type case.

Security requirements around software systems have become more stringent as society becomes more interconnected via the Internet. New ways of prioritizing security efforts are needed so security professionals can use their time effectively to find security vulnerabilities or prevent them from occurring in the first place. The goal of this work is to help software development teams prioritize security efforts by approximating the attack surface of a software system via stack trace analysis. Automated attack surface approximation is a technique that uses crash dump stack traces to predict what code may contain exploitable vulnerabilities. If a code entity (a binary, file or function) appears on stack traces, then Attack Surface Approximation (ASA) considers that code entity is on the attack surface of the software system. We also explore whether number of appearances of code on stack traces correlates with where security vulnerabilities are found. To date, feasibility studies of ASA have been performed on Windows 8 and 8.1, and Mozilla Firefox. The results from these studies indicate that ASA may be useful for practitioners trying to secure their software systems. We are now working towards establishing the ground truth of what the attack surface of software systems is, along with looking at how ASA could change over time, among other metrics.

Security requirements around software systems have become more stringent as society becomes more interconnected via the Internet. New ways of prioritizing security efforts are needed so security professionals can use their time effectively to find security vulnerabilities or prevent them from occurring in the first place. The goal of this work is to help software development teams prioritize security efforts by approximating the attack surface of a software system via stack trace analysis. Automated attack surface approximation is a technique that uses crash dump stack traces to predict what code may contain exploitable vulnerabilities. If a code entity (a binary, file or function) appears on stack traces, then Attack Surface Approximation (ASA) considers that code entity is on the attack surface of the software system. We also explore whether number of appearances of code on stack traces correlates with where security vulnerabilities are found. To date, feasibility studies of ASA have been performed on Windows 8 and 8.1, and Mozilla Firefox. The results from these studies indicate that ASA may be useful for practitioners trying to secure their software systems. We are now working towards establishing the ground truth of what the attack surface of software systems is, along with looking at how ASA could change over time, among other metrics.

The premise of this year's SafeConfig Workshop is existing tools and methods for security assessments are necessary but insufficient for scientifically rigorous testing and evaluation of resilient and active cyber systems. The objective for this workshop is the exploration and discussion of scientifically sound testing regimen(s) that will continuously and dynamically probe, attack, and "test" the various resilient and active technologies. This adaptation and change in focus necessitates at the very least modification, and potentially, wholesale new developments to ensure that resilient- and agile-aware security testing is available to the research community. All testing, validation and experimentation must also be repeatable, reproducible, subject to scientific scrutiny, measurable and meaningful to both researchers and practitioners.

While cloud computing has become an attractive platform for supporting data intensive applications, a major obstacle to the adoption of cloud computing in sectors such as health and defense is the privacy risk associated with releasing datasets to third-parties in the cloud for analysis. A widely-adopted technique for data privacy preservation is to anonymize data via local recoding. However, most existing local-recoding techniques are either serial or distributed without directly optimizing scalability, thus rendering them unsuitable for big data applications. In this paper, we propose a highly scalable approach to local-recoding anonymization in cloud computing, based on Locality Sensitive Hashing (LSH). Specifically, a novel semantic distance metric is presented for use with LSH to measure the similarity between two data records. Then, LSH with the MinHash function family can be employed to divide datasets into multiple partitions for use with MapReduce to parallelize computation while preserving similarity. By using our efficient LSH-based scheme, we can anonymize each partition through the use of a recursive agglomerative \$k\$-member clustering algorithm. Extensive experiments on real-life datasets show that our approach significantly improves the scalability and time-efficiency of local-recoding anonymization by orders of magnitude over existing approaches.

The threat of DDOS and other cyberattacks has increased during the last decade. In addition to the radical increase in the number of attacks, they are also becoming more sophisticated with the targets ranging from ordinary users to service providers and even critical infrastructure. According to some resources, the sophistication of attacks is increasing faster than the mitigating actions against them. For example determining the location of the attack origin is becoming impossible as cyber attackers employ specific means to evade detection of the attack origin by default, such as using proxy services and source address spoofing. The purpose of this paper is to initiate discussion about effective Internet Protocol traceback mechanisms that are needed to overcome this problem. We propose an approach for traceback that is based on extensive use of security metrics before (proactive) and during (reactive) the attacks.

This paper introduces combined data integrity and availability attacks to expand the attack scenarios against power system state estimation. The goal of the adversary, who uses the combined attack, is to perturb the state estimates while remaining hidden from the observer. We propose security metrics that quantify vulnerability of power grids to combined data attacks under single and multi-path routing communication models. In order to evaluate the proposed security metrics, we formulate them as mixed integer linear programming (MILP) problems. The relation between the security metrics of combined data attacks and pure data integrity attacks is analyzed, based on which we show that, when data availability and data integrity attacks have the same cost, the two metrics coincide. When data availability attacks have a lower cost than data integrity attacks, we show that a combined data attack could be executed with less attack resources compared to pure data integrity attacks. Furthermore, it is shown that combined data attacks would bypass integrity-focused mitigation schemes. These conclusions are supported by the results obtained on a power system model with and without a communication model with single or multi-path routing.

The success or failure of a mobile application (`app') is largely determined by user ratings. Users frequently make their app choices based on the ratings of apps in comparison with similar, often competing apps. Users also expect apps to continually provide new features while maintaining quality, or the ratings drop. At the same time apps must also be secure, but is there a historical trade-off between security and ratings? Or are app store ratings a more all-encompassing measure of product maturity? We used static analysis tools to collect security-related metrics in 38,466 Android apps from the Google Play store. We compared the rate of an app's permission misuse, number of requested permissions, and Androrisk score, against its user rating. We found that high-rated apps have statistically significantly higher security risk metrics than low-rated apps. However, the correlations are weak. This result supports the conventional wisdom that users are not factoring security risks into their ratings in a meaningful way. This could be due to several reasons including users not placing much emphasis on security, or that the typical user is unable to gauge the security risk level of the apps they use everyday.

This paper presents a comprehensive review of state-of-the-art research works in knowledge-based user authentication, covering the security and usability aspects of the most prominent user authentication schemes; text-, pin- and graphical-based. From the security perspective, we analyze current threats from a user and service provider perspective. Furthermore, based on current practices in authentication policies, we summarize and discuss their security strengths based on widely applied security metrics. From the usability point of view, we present and discuss the usability of each authentication scheme in regards with task performance and user experience. The analysis reveals that although a plethora of alternative user authentication schemes have been proposed in the literature and users interact differently with the various alternatives, online service providers do not yet adopt alternatives to text-based solutions. We further discuss and identify areas for further research and improved methodology with the aim to drive this research towards the design of sustainable, secure and usable authentication approaches.

Security in cloud environments is always considered an issue, due to the lack of control over leased resources. In this paper, we present a solution that offers security-as-a-service by relying on Security Service Level Agreements (Security SLAs) as a means to represent the security features to be granted. In particular, we focus on a security mechanism that is automatically configured and activated in an as-a-service fashion in order to protect cloud resources against DoS attacks. The activities reported in this paper are part of a wider work carried out in the FP7-ICT programme project SPECS, which aims at building a framework offering Security-as-a-Service using an SLA-based approach. The proposed approach founds on the adoption of SPECS Services to negotiate, to enforce and to monitor suitable security metrics, chosen by cloud customers, negotiated with the provider and included in a signed Security SLA.

Information and communication technologies have augmented interoperability and rapidly advanced varying industries, with vast complex interconnected networks being formed in areas such as safety-critical systems, which can be further categorised as critical infrastructures. What also must be considered is the paradigm of the Internet of Things which is rapidly gaining prevalence within the field of wireless communications, being incorporated into areas such as e-health and automation for industrial manufacturing. As critical infrastructures and the Internet of Things begin to integrate into much wider networks, their reliance upon communication assets by third parties to ensure collaboration and control of their systems will significantly increase, along with system complexity and the requirement for improved security metrics. We present a critical analysis of the risk assessment methods developed for generating attack graphs. The failings of these existing schemas include the inability to accurately identify the relationships and interdependencies between the risks and the reduction of attack graph size and generation complexity. Many existing methods also fail due to the heavy reliance upon the input, identification of vulnerabilities, and analysis of results by human intervention. Conveying our work, we outline our approach to modelling interdependencies within large heterogeneous collaborative infrastructures, proposing a distributed schema which utilises network modelling and attack graph generation methods, to provide a means for vulnerabilities, exploits and conditions to be represented within a unified model.

Low-latency anonymity systems such as Tor rely on intermediate relays to forward user traffic; these relays, however, are often unreliable, resulting in a degraded user experience. Worse yet, malicious relays may introduce deliberate failures in a strategic manner in order to increase their chance of compromising anonymity. In this paper we propose using a reputation metric that can profile the reliability of relays in an anonymity system based on users' past experience. The two main challenges in building a reputation-based system for an anonymity system are: first, malicious participants can strategically oscillate between good and malicious nature to evade detection, and second, an observed failure in an anonymous communication cannot be uniquely attributed to a single relay. Our proposed framework addresses the former challenge by using a proportional-integral-derivative (PID) controller-based reputation metric that ensures malicious relays adopting time-varying strategic behavior obtain low reputation scores over time, and the latter by introducing a filtering scheme based on the evaluated reputation score to effectively discard relays mounting attacks. We collect data from the live Tor network and perform simulations to validate the proposed reputation-based filtering scheme. We show that an attacker does not gain any significant benefit by performing deliberate failures in the presence of the proposed reputation framework.

In highly configurable systems the configuration space is too big for (re-)certifying every configuration in isolation. In this project, we combine software analysis with network analysis to detect which configuration options interact and which have local effects. Instead of analyzing a system as Linux and SELinux for every combination of configuration settings one by one (>102000 even considering compile-time configurations only), we analyze the effect of each configuration option once for the entire configuration space. The analysis will guide us to designs separating interacting configuration options in a core system and isolating orthogonal and less trusted configuration options from this core.

Modeling and analyzing security of networked systems is an important problem in the emerging Science of Security and has been under active investigation. In this paper, we propose a new approach towards tackling the problem. Our approach is inspired by the shock model and random environment techniques in the Theory of Reliability, while accommodating security ingredients. To the best of our knowledge, our model is the first that can accommodate a certain degree of adaptiveness of attacks, which substantially weakens the often-made independence and exponential attack inter-arrival time assumptions. The approach leads to a stochastic process model with two security metrics, and we attain some analytic results in terms of the security metrics.

In highly configurable systems the configuration space is too big for (re-)certifying every configuration in isolation. In this project, we combine software analysis with network analysis to detect which configuration options interact and which have local effects. Instead of analyzing a system as Linux and SELinux for every combination of configuration settings one by one (>102000 even considering compile-time configurations only), we analyze the effect of each configuration option once for the entire configuration space. The analysis will guide us to designs separating interacting configuration options in a core system and isolating orthogonal and less trusted configuration options from this core. 

In highly configurable systems the configuration space is too big for (re-)certifying every configuration in isolation. In this project, we combine software analysis with network analysis to detect which configuration options interact and which have local effects. Instead of analyzing a system as Linux and SELinux for every combination of configuration settings one by one (>102000 even considering compile-time configurations only), we analyze the effect of each configuration option once for the entire configuration space. The analysis will guide us to designs separating interacting configuration options in a core system and isolating orthogonal and less trusted configuration options from this core. 

In highly configurable systems the configuration space is too big for (re-)certifying every configuration in isolation. In this project, we combine software analysis with network analysis to detect which configuration options interact and which have local effects. Instead of analyzing a system as Linux and SELinux for every combination of configuration settings one by one (>102000 even considering compile-time configurations only), we analyze the effect of each configuration option once for the entire configuration space. The analysis will guide us to designs separating interacting configuration options in a core system and isolating orthogonal and less trusted configuration options from this core.

By enabling a direct comparison of different security solutions with respect to their relative effectiveness, a network security metric may provide quantifiable evidences to assist security practitioners in securing computer networks. However, research on security metrics has been hindered by difficulties in handling zero-day attacks exploiting unknown vulnerabilities. In fact, the security risk of unknown vulnerabilities has been considered as something unmeasurable due to the less predictable nature of software flaws. This causes a major difficulty to security metrics, because a more secure configuration would be of little value if it were equally susceptible to zero-day attacks. In this paper, we propose a novel security metric, k-zero day safety, to address this issue. Instead of attempting to rank unknown vulnerabilities, our metric counts how many such vulnerabilities would be required for compromising network assets; a larger count implies more security because the likelihood of having more unknown vulnerabilities available, applicable, and exploitable all at the same time will be significantly lower. We formally define the metric, analyze the complexity of computing the metric, devise heuristic algorithms for intractable cases, and finally demonstrate through case studies that applying the metric to existing network security practices may generate actionable knowledge.