The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily basis. This calls for machine learning methods for vulnerability detection. Deep learning is attractive for this purpose because it alleviates the requirement to manually define features. Despite the tremendous success of deep learning in other application domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities in C/C++ programs with source code. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with four software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database. Among these 15 vulnerabilities, seven are unknown and have been reported to the vendors, and the other eight have been “silently” patched by the vendors when releasing newer versions of the pertinent software products.

Blockchain technology is believed by many to be a game changer in many application domains. While the first generation of blockchain technology (i.e., Blockchain 1.0) is almost exclusively used for cryptocurrency, the second generation (i.e., Blockchain 2.0), as represented by Ethereum, is an open and decentralized platform enabling a new paradigm of computing—Decentralized Applications (DApps) running on top of blockchains. The rich applications and semantics of DApps inevitably introduce many security vulnerabilities, which have no counterparts in pure cryptocurrency systems like Bitcoin. Since Ethereum is a new, yet complex, system, it is imperative to have a systematic and comprehensive understanding on its security from a holistic perspective, which was previously unavailable in the literature. To the best of our knowledge, the present survey, which can also be used as a tutorial, fills this void. We systematize three aspects of Ethereum systems security: vulnerabilities, attacks, and defenses. We draw insights into vulnerability root causes, attack consequences, and defense capabilities, which shed light on future research directions.

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed μVulDeePecker. The key insight underlying μVulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of μVulDeePecker. Experimental results show that μVulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities.

Automatically detecting software vulnerabilities is an important problem that has attracted much attention from the academic research community. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and the locating precision that would warrant their adoption for real-world use. In this article, we present a vulnerability detector that can simultaneously achieve a high detection capability and a high locating precision, dubbed Vul nerability Dee p learning-based Locator (VulDeeLocator). In the course of designing VulDeeLocator, we encounter difficulties including how to accommodate semantic relations between the definitions of types as well as macros and their uses across files, how to accommodate accurate control flows and variable define-use relations, and how to achieve high locating precision. We solve these difficulties by using two innovative ideas: (i) leveraging intermediate code to accommodate extra semantic information, and (ii) using the notion of granularity refinement to pin down locations of vulnerabilities. When applied to 200 files randomly selected from three real-world software products, VulDeeLocator detects 18 confirmed vulnerabilities (i.e., true-positives). Among them, 16 vulnerabilities correspond to known vulnerabilities; the other two are not reported in the National Vulnerability Database (NVD) but have been “silently” patched by the vendor of Libav when releasing newer versions.

Security metrics have received significant attention. However, they have not been systematically explored based on the understanding of attack-defense interactions, which are affected by various factors, including the degree of system vulnerabilities, the power of system defense mechanisms, attack (or threat) severity, and situations a system at risk faces. This survey particularly focuses on how a system security state can evolve as an outcome of cyber attack-defense interactions. This survey concerns how to measure system-level security by proposing a security metrics framework based on the following four sub-metrics: (1) metrics of system vulnerabilities , (2) metrics of defense power , (3) metrics of attack or threat severity , and (4) metrics of situations . To investigate the relationships among these four sub-metrics, we propose a hierarchical ontology with four sub-ontologies corresponding to the four sub-metrics and discuss how they are related to each other. Using the four sub-metrics, we discuss the state-of-art existing security metrics and their advantages and disadvantages (or limitations) to obtain lessons and insight in order to achieve an ideal goal in developing security metrics. Finally, we discuss open research questions in the security metrics research domain and we suggest key factors to enhance security metrics from a system security perspective.

Analyzing cyber incident datasets is an important method for deepening our understanding of the evolution of the threat situation. This is a relatively new research topic and many studies remain to be done. In this paper, we report a statistical analysis of a breach incident dataset corresponding to 12 years (2005-2017) of cyber hacking activities that include malware attacks. We show that, in contrast to the findings reported in the literature, both the hacking breach incident inter-arrival times and the breach sizes should be modeled by stochastic processes, rather than by distributions because they exhibit autocorrelations. Then, we propose particular stochastic process models to respectively fit the inter-arrival times and the breach sizes. We also show that these models can predict the inter-arrival times and the breach sizes. In order to get deeper insights into the evolution of hacking breach incidents, we conduct both qualitative and quantitative trend analyses on the dataset. We draw a set of cybersecurity insights, including that the threat of cyber hacks is indeed getting worse in terms of their frequency, but not in terms of the magnitude of their damage.