ترغب بنشر مسار تعليمي؟ اضغط هنا

Symbolic Verification of Cache Side-channel Freedom

61   0   0.0 ( 0 )
 نشر من قبل Sudipta Chattopadhyay
 تاريخ النشر 2018
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Cache timing attacks allow third-party observers to retrieve sensitive information from program executions. But, is it possible to automatically check the vulnerability of a program against cache timing attacks and then, automatically shield program executions against these attacks? For a given program, a cache configuration and an attack model, our CACHEFIX framework either verifies the cache side-channel freedom of the program or synthesizes a series of patches to ensure cache side-channel freedom during program execution. At the core of our framework is a novel symbolic verification technique based on automated abstraction refinement of cache semantics. The power of such a framework is to allow symbolic reasoning over counterexample traces and to combine it with runtime monitoring for eliminating cache side channels during program execution. Our evaluation with routines from OpenSSL, libfixedtimefixedpoint, GDK and FourQlib libraries reveals that our CACHEFIX approach (dis)proves cache sidechannel freedom within an average of 75 seconds. Besides, in all except one case, CACHEFIX synthesizes all patches within 20 minutes to ensure cache side-channel freedom of the respective routines during execution.



قيم البحث

اقرأ أيضاً

We propose a data-driven method for synthesizing a static analyzer to detect side-channel information leaks in cryptographic software. Compared to the conventional way of manually crafting such a static analyzer, which can be labor intensive, error p rone and suboptimal, our learning-based technique is not only automated but also provably sound. Our analyzer consists of a set of type-inference rules learned from the training data, i.e., example code snippets annotated with ground truth. Internally, we use syntax-guided synthesis (SyGuS) to generate new features and decision tree learning (DTL) to generate type-inference rules based on these features. We guarantee soundness by formally proving each learned rule via a technique called Datalog query containment checking. We have implemented our technique in the LLVM compiler and used it to detect power side channels in C programs. Our results show that, in addition to being automated and provably sound during synthesis, the learned analyzer also has the same empirical accuracy as two state-of-the-art, manually crafted analyzers while being 300X and 900X faster, respectively.
We demonstrate the feasibility of database reconstruction under a cache side-channel attack on SQLite. Specifically, we present a Flush+Reload attack on SQLite that obtains approximate (or noisy) volumes of range queries made to a private database. W e then present several algorithms that, taken together, reconstruct nearly the exact database in varied experimental conditions, given these approximate volumes. Our reconstruction algorithms employ novel techniques for the approximate/noisy setting, including a noise-tolerant clique-finding algorithm, a Match & Extend algorithm for extrapolating volumes that are omitted from the clique, and a Noise Reduction Step that makes use of a closest vector problem (CVP) solver to improve the overall accuracy of the reconstructed database. The time complexity of our attacks grows quickly with the size of the range of the queried attribute, but scales well to large databases. Experimental results show that we can reconstruct databases of size 100,000 and ranges of size 12 with error percentage of 0.11 % in under 12 hours on a personal laptop.
Lack of security expertise among software practitioners is a problem with many implications. First, there is a deficit of security professionals to meet current needs. Additionally, even practitioners who do not plan to work in security may benefit f rom increased understanding of security. The goal of this paper is to aid software engineering educators in designing a comprehensive software security course by sharing an experience running a software security course for the eleventh time. Through all the eleven years of running the software security course, the course objectives have been comprehensive - ranging from security testing, to secure design and coding, to security requirements to security risk management. For the first time in this eleventh year, a theme of the course assignments was to map vulnerability discovery to the security controls of the Open Web Application Security Project (OWASP) Application Security Verification Standard (ASVS). Based upon student performance on a final exploratory penetration testing project, this mapping may have increased students depth of understanding of a wider range of security topics. The students efficiently detected 191 unique and verified vulnerabilities of 28 different Common Weakness Enumeration (CWE) types during a three-hour period in the OpenMRS project, an electronic health record application in active use.
Recent work has introduced attacks that extract the architecture information of deep neural networks (DNN), as this knowledge enhances an adversarys capability to conduct black-box attacks against the model. This paper presents the first in-depth sec urity analysis of DNN fingerprinting attacks that exploit cache side-channels. First, we define the threat model for these attacks: our adversary does not need the ability to query the victim model; instead, she runs a co-located process on the host machine victims deep learning (DL) system is running and passively monitors the accesses of the target functions in the shared framework. Second, we introduce DeepRecon, an attack that reconstructs the architecture of the victim network by using the internal information extracted via Flush+Reload, a cache side-channel technique. Once the attacker observes function invocations that map directly to architecture attributes of the victim network, the attacker can reconstruct the victims entire network architecture. In our evaluation, we demonstrate that an attacker can accurately reconstruct two complex networks (VGG19 and ResNet50) having observed only one forward propagation. Based on the extracted architecture attributes, we also demonstrate that an attacker can build a meta-model that accurately fingerprints the architecture and family of the pre-trained model in a transfer learning setting. From this meta-model, we evaluate the importance of the observed attributes in the fingerprinting process. Third, we propose and evaluate new framework-level defense techniques that obfuscate our attackers observations. Our empirical security analysis represents a step toward understanding the DNNs vulnerability to cache side-channel attacks.
Side channels represent a broad class of security vulnerabilities that have been demonstrated to exist in many applications. Because completely eliminating side channels often leads to prohibitively high overhead, there is a need for a principled tra de-off between cost and leakage. In this paper, we make a case for the use of maximal leakage to analyze such trade-offs. Maximal leakage is an operationally interpretable leakage metric designed for side channels. We present the most useful theoretical properties of maximal leakage from previous work and demonstrate empirically that conventional metrics such as mutual information and channel capacity underestimate the threat posed by side channels whereas maximal leakage does not. We also study the cost-leakage trade-off as an optimization problem using maximal leakage. We demonstrate that not only can this problem be represented as a linear program, but also that optimal protection can be achieved using a combination of at most two deterministic schemes.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا