Biblio

The large adoption of cloud services in many business domains dramatically increases the need for effective solutions to improve the security of deployed services. The adoption of Security Service Level Agreements (Security SLAs) represents an effective solution to state formally the security guarantees that a cloud service is able to provide. Even if security policies declared by the service provider are properly implemented before the service is deployed and launched, the actual security level tends to degrade over time, due to the knowledge on the exposed attack surface that the attackers are progressively able to gain. In this paper, we present a Security SLA-driven MTD framework that allows MTD strategies to be applied to a cloud application by automatically switching among different admissible application configurations, in order to confuse the attackers and nullify their reconnaissance effort, while preserving the application Security SLA across reconfigurations.

Recent advances in pervasive computing have caused a rapid growth of the Smart Home market, where a number of otherwise mundane pieces of technology are capable of connecting to the Internet and interacting with other similar devices. However, with the lack of a commonly adopted set of guidelines, several IT companies are producing smart devices with their own proprietary standards, leading to highly heterogeneous Smart Home systems in which the interoperability of the present elements is not always implemented in the most straightforward manner. As such, understanding the cyber risk of these cyber-physical systems beyond the individual devices has become an almost intractable problem. This paper tackles this issue by introducing a Smart Home reference architecture which facilitates security analysis. Being composed by three viewpoints, it gives a high-level description of the various functions and components needed in a domestic IoT device and network. Furthermore, this document demonstrates how the architecture can be used to determine the various attack surfaces of a home automation system from which its key vulnerabilities can be determined.

As 5G transitions from an industrial vision to a tangible, next-generation mobile technology, security remains key business driver. Heterogeneous environment, new networking paradigms and novel use cases makes 5G vulnerable to new security threats. This in turn necessitates a flexible and dependable security mechanism. End-to-End (E2E) data protection provides better security, avoids repeated security operations like encryption/decryption and provides differentiated security based on the services. E2E security deals with authentication, integrity, key management and confidentiality. The attack surface of a 5G system is larger as 5G aims for a heterogeneous networked society. Hence attack resistance needs to be a design consideration when defining new 5G protocols. This framework has been designed for accessing the manifold applications with high security and trust by offering E2E security for various services. The proposed framework is evaluated based on computation complexity, communication complexity, attack resistance rate and security defensive rate. The protocol is also evaluated for correctness, and resistance against passive, active and dictionary attacks using random oracle model and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool.

The growing pervasiveness of Internet of Things (IoT) expands the attack surface by connecting more and more attractive attack targets, i.e. embedded devices, to the Internet. One key component in securing these devices is software integrity checking, which typically attained with Remote Attestation (RA). RA is realized as an interactive protocol, whereby a trusted party, verifier, verifies the software integrity of a potentially compromised remote device, prover. In the vast majority of IoT applications, smart devices operate in swarms, thus triggering the need for efficient swarm attestation schemes.In this paper, we present WISE, the first intelligent swarm attestation protocol that aims to minimize the communication overhead while preserving an adequate level of security. WISE depends on a resource-efficient smart broadcast authentication scheme where devices are organized in fine-grained multi-clusters, and whenever needed, the most likely compromised devices are attested. The candidate devices are selected intelligently taking into account the attestation history and the diverse characteristics (and constraints) of each device in the swarm. We show that WISE is very suitable for resource-constrained embedded devices, highly efficient and scalable in heterogenous IoT networks, and offers an adjustable level of security.

Modern software development and deployment practices encourage complexity and bloat while unintentionally sacrificing efficiency and security. A major driver in this is the overwhelming emphasis on programmers' productivity. The constant demands to speed up development while reducing costs have forced a series of individual decisions and approaches throughout software engineering history that have led to this point. The current state-of-the-practice in the field is a patchwork of architectures and frameworks, packed full of features in order to appeal to: the greatest number of people, obscure use cases, maximal code reuse, and minimal developer effort. The Office of Naval Research (ONR) Total Platform Cyber Protection (TPCP) program seeks to de-bloat software binaries late in the life-cycle with little or no access to the source code or the development process.

With growing popularity of Android, it's attack surface has also increased. Prevalence of third party android marketplaces gives attackers an opportunity to plant their malicious apps in the mobile eco-system. To evade signature based detection, attackers often transform their malware, for instance, by introducing code level changes. In this paper we propose a lightweight static Permission Flow Graph (PFG) based approach to detect malware even when they have been transformed (obfuscated). A number of techniques based on behavioral analysis have also been proposed in the past; how-ever our interest lies in leveraging the permission framework alone to detect malware variants and transformations without considering behavioral aspects of a malware. Our proposed approach constructs Permission Flow Graph (PFG) for an Android App. Transformations performed at code level, often result in changing control flow, however, most of the time, the permission flow remains invariant. As a consequences, PFGs of transformed malware and non-transformed malware remain structurally similar as shown in this paper using state-of-the-art graph similarity algorithm. Furthermore, we propose graph based similarity metrics at both edge level and vertex level in order to bring forth the structural similarity of the two PFGs being compared. We validate our proposed methodology through machine learning algorithms. Results prove that our approach is successfully able to group together Android malware and its variants (transformations) together in the same cluster. Further, we demonstrate that our proposed approach is able to detect transformed malware with a detection accuracy of 98.26%, thereby ensuring that malicious Apps can be detected even after transformations.

In order to support large volume of transactions and number of users, as estimated by the load demand modeling, a system needs to scale in order to continue to satisfy required quality attributes. In particular, for systems exposed to the Internet, scaling up may increase the attack surface susceptible to malicious intrusions. The new proactive approach based on the concept of Moving Target Defense (MTD) should be considered as a complement to current cybersecurity protection. In this paper, we analyze the scalability of the Self Cleansing Intrusion Tolerance (SCIT) MTD approach using Cloud infrastructure services. By applying the model of MTD with continuous rotation and diversity to a multi-node or multi-instance system, we argue that the effectiveness of the approach is dependent on the share-nothing architecture pattern of the large system. Furthermore, adding more resources to the MTD mechanism can compensate to achieve the desired level of secure availability.

Proactive security reviews and test efforts are a necessary component of the software development lifecycle. Resource limitations often preclude reviewing the entire code base. Making informed decisions on what code to review can improve a team's ability to find and remove vulnerabilities. Risk-based attack surface approximation (RASA) is a technique that uses crash dump stack traces to predict what code may contain exploitable vulnerabilities. The goal of this research is to help software development teams prioritize security efforts by the efficient development of a risk-based attack surface approximation. We explore the use of RASA using Mozilla Firefox and Microsoft Windows stack traces from crash dumps. We create RASA at the file level for Firefox, in which the 15.8% of the files that were part of the approximation contained 73.6% of the vulnerabilities seen for the product. We also explore the effect of random sampling of crashes on the approximation, as it may be impractical for organizations to store and process every crash received. We find that 10-fold random sampling of crashes at a rate of 10% resulted in 3% less vulnerabilities identified than using the entire set of stack traces for Mozilla Firefox. Sampling crashes in Windows 8.1 at a rate of 40% resulted in insignificant differences in vulnerability and file coverage as compared to a rate of 100%.

Cyber-Physical Systems (CPS) such as Unmanned Aerial Systems (UAS) sense and actuate their environment in pursuit of a mission. The attack surface of these remotely located, sensing and communicating devices is both large, and exposed to adversarial actors, making mission assurance a challenging problem. While best-practice security policies should be followed, they are rarely enough to guarantee mission success as not all components in the system may be trusted and the properties of the environment (e.g., the RF environment) may be under the control of the attacker. CPS must thus be built with a high degree of resilience to mitigate threats that security cannot alleviate. In this paper, we describe the Agile and Resilient Embedded Systems (ARES) methodology and metric set. The ARES methodology pursues cyber security and resilience (CSR) as high level system properties to be developed in the context of the mission. An analytic process guides system developers in defining mission objectives, examining principal issues, applying CSR technologies, and understanding their interactions.

In Android, communications between apps and system services are supported by a transaction-based Inter-Process Communication (IPC) mechanism. Binder, as the cornerstone of this IPC mechanism, separates two communicating parties as client and server. As with any client-server model, the server should not make any assumption on the validity (sanity) of client-side transaction. To our surprise, we find this principle has frequently been overlooked in the implementation of Android system services. In this paper, we try to answer why developers keep making this seemingly simple mistake by studying more than 100 vulnerabilities on this attack surface. We analyzed these vulnerabilities to find that most of them are rooted at a common confusion of where the actual security boundary is among system developers. We thus highlight the deficiency of testing only on client-side public APIs and argue for the necessity of testing and protection on the Binder interface — the actual security boundary. Specifically, we design and implement BinderCracker, an automatic testing framework that supports context-aware fuzzing and actively manages the dependency between transactions. It does not require the source codes of the component under test, is compatible with services in different layers, and performs much more effectively than simple black-box fuzzing. We also call attention to the attack attribution problem for IPC-based attacks. The lack of OS-level support makes it very difficult to identify the culprit apps even for developers with adb access. We address this issue by providing an informative runtime diagnostic tool that tracks the origin, schema, content, and parsing details of each failed transaction. This brings transparency into the IPC process and provides an essential step for other in-depth analysis or forensics.

We present the Chained Attacks approach, an automated model-based approach to test the security of web applications that does not require a background in formal methods. Starting from a set of HTTP conversations and a configuration file providing the testing surface and purpose, a model of the System Under Test (SUT) is generated and input, along with the web attacker model we defined, to a model checker acting as test oracle. The HTTP conversations, payload libraries, and a mapping created while generating the model aid the concretization of the test cases, allowing for their execution on the SUT's implementation. We applied our approach to a real-life case study and we were able to find a combination of different attacks representing the concrete chained attack performed by a bug bounty hunter.

When reasoning about software security, researchers and practitioners use the phrase ``attack surface'' as a metaphor for risk. Enumerate and minimize the ways attackers can break in then risk is reduced and the system is better protected, the metaphor says. But software systems are much more complicated than their surfaces. We propose function- and file-level attack surface metrics–-proximity and risky walk–-that enable fine-grained risk assessment. Our risky walk metric is highly configurable: we use PageRank on a probability-weighted call graph to simulate attacker behavior of finding or exploiting a vulnerability. We provide evidence-based guidance for deploying these metrics, including an extensive parameter tuning study. We conducted an empirical study on two large open source projects, FFmpeg and Wireshark, to investigate the potential correlation between our metrics and historical post-release vulnerabilities. We found our metrics to be statistically significantly associated with vulnerable functions/files with a small-to-large Cohen's d effect size. Our prediction model achieved an increase of 36% (in FFmpeg) and 27% (in Wireshark) in the average value of F-measure over a base model built with SLOC and coupling metrics. Our prediction model outperformed comparable models from prior literature with notable improvements: 58% reduction in false negative rate, 81% reduction in false positive rate, and 548% increase in F-measure. These metrics advance vulnerability prevention by [(a)] being flexible in terms of granularity, performing better than vulnerability prediction literature, and being tunable so that practitioners can tailor the metrics to their products and better assess security risk.

Smart grid aims to improve control and monitoring routines to ensure reliable and efficient supply of electricity. The rapid advancements in information and communication technologies of Supervisory Control And Data Acquisition (SCADA) networks, however, have resulted in complex cyber physical systems. This added complexity has broadened the attack surface of power-related applications, amplifying their susceptibility to cyber threats. A particular class of system integrity attacks against the smart grid is False Data Injection (FDI). In a successful FDI attack, an adversary compromises the readings of grid sensors in such a way that errors introduced into estimates of state variables remain undetected. This paper presents an end-to-end case study of how to instantiate real FDI attacks to the Alternating Current (AC) –nonlinear– State Estimation (SE) process. The attack is realized through firmware modifications of the microprocessor-based remote terminal systems, falsifying the data transmitted to the SE routine, and proceeds regardless of perfect or imperfect knowledge of the current system state. The case study concludes with an investigation of an attack on the IEEE 14 bus system using load data from the New York Independent System Operator (NYISO).

Cloud computing has been a great enabler for both the Internet of Things and Big Data. However, as with all new computing developments, development of the technology is usually much faster than consideration for, and development of, solutions for security and privacy. In a previous paper, we proposed that a unikernel solution could be used to improve security and privacy in a cloud scenario. In this paper, we outline how we might apply this approach to the Internet of Things, which can demonstrate an improvement over existing approaches.

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.

Proactive security review and test efforts are a necessary component of the software development lifecycle. Since resource limitations often preclude reviewing, testing and fortifying the entire code base, prioritizing what code to review/test can improve a team's ability to find and remove more vulnerabilities that are reachable by an attacker. One way that professionals perform this prioritization is the identification of the attack surface of software systems. However, identifying the attack surface of a software system is non-trivial. The goal of this poster is to present the concept of a risk-based attack surface approximation based on crash dump stack traces for the prioritization of security code rework efforts. For this poster, we will present results from previous efforts in the attack surface approximation space, including studies on its effectiveness in approximating security relevant code for Windows and Firefox. We will also discuss future research directions for attack surface approximation, including discovery of additional metrics from stack traces and determining how many stack traces are required for a good approximation.

Moving target defense (MTD) is an emerging paradigm in which system defenses dynamically mutate in order to decrease the overall system attack surface. Though the concept is promising, implementations have not been widely adopted. The field has been actively researched for over ten years, and has only produced a small amount of extensively adopted defenses, most notably, address space layout randomization (ASLR). This is despite the fact that there currently exist a variety of moving target implementations and proofs-of-concept. We suspect that this results from the moving target controls breaking critical system dependencies from the perspectives of users and administrators, as well as making things more difficult for attackers. As a result, the impact of the controls on overall system security is not sufficient to overcome the inconvenience imposed on legitimate system users. In this paper, we analyze a successful MTD approach. We study the control's dependency graphs, showing how we use graph theoretic and network properties to predict the effectiveness of the selected control.

While software systems are being developed and released to consumers more rapidly than ever, security remains an important issue for developers. Shorter development cycles means less time for these critical security testing and review efforts. The attack surface of a system is the sum of all paths for untrusted data into and out of a system. Code that lies on the attack surface therefore contains code with actual exploitable vulnerabilities. However, identifying code that lies on the attack surface requires the same contested security resources from the secure testing efforts themselves. My research proposes an automated technique to approximate attack surfaces through the analysis of stack traces. We hypothesize that stack traces user crashes represent activity that puts the system under stress, and is therefore indicative of potential security vulnerabilities. The goal of this research is to aid software engineers in prioritizing security efforts by approximating the attack surface of a system via stack trace analysis. In a trial on Mozilla Firefox, the attack surface approximation selected 8.4% of files and contained 72.1% of known vulnerabilities. A similar trial was performed on the Windows 8 product.

This research focuses on hyper visor security from holistic perspective. It centers on hyper visor architecture - the organization of the various subsystems which collectively compromise a virtualization platform. It holds that the path to a secure hyper visor begins with a big-picture focus on architecture. Unfortunately, little research has been conducted with this perspective. This study investigates the impact of monolithic and micro kernel hyper visor architectures on the size and scope of the attack surface. Six architectural features are compared: management API, monitoring interface, hyper calls, interrupts, networking, and I/O. These subsystems are core hyper visor components which could be used as attack vectors. Specific examples and three leading hyper visor platforms are referenced (ESXi for monolithic architecture; Xen and Hyper-V for micro architecture). The results describe the relative strengths and vulnerabilities of both types of architectures. It is concluded that neither design is more secure, since both incorporate security tradeoffs in core processes.

One of the primary challenges when developing or implementing a security framework for any particular environment is determining the efficacy of the implementation. Does the implementation address all of the potential vulnerabilities in the environment, or are there still unaddressed issues? Further, if there is a choice between two frameworks, what objective measure can be used to compare the frameworks? To address these questions, we propose utilizing a technique of attack graph analysis to map the attack surface of the environment and identify the most likely avenues of attack. We show that with this technique we can quantify the baseline state of an application and compare that to the attack surface after implementation of a security framework, while simultaneously allowing for comparison between frameworks in the same environment or a single framework across multiple applications.

Recent years, HTML5 is widely adopted in popular browsers. Unfortunately, as a new Web standard, HTML5 may expand the Cross Site Scripting (XSS) attack surface as well as improve the interactivity of the page. In this paper, we identified 14 XSS attack vectors related to HTML5 by a systematic analysis about new tags and attributes. Based on these vectors, a XSS test vector repository is constructed and a dynamic XSS vulnerability detection tool focusing on Webmail systems is implemented. By applying the tool to some popular Webmail systems, seven exploitable XSS vulnerabilities are found. The evaluation result shows that our tool can efficiently detect XSS vulnerabilities introduced by HTML5.

In this report we show how to adapt the notion of “attack surface” to formally evaluate security properties at the architectural level of design and to identify vulnerabilities in architectural designs. Further we explore the application of this metric in the context of architecture-based transformations to improve security by reducing the attack surface. These transformations are described in detail and validated with a simple experiment.