Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSpecBox: A Label-Based Transparent Speculation Scheme Against Transient Execution Attacks
63
0
0.0
(
0
)
Added by
Bowen Tang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Speculative execution techniques have been a cornerstone of modern processors to improve instruction-level parallelism. However, recent studies showed that this kind of techniques could be exploited by attackers to leak secret data via transient execution attacks, such as Spectre. Many defenses are proposed to address this problem, but they all face various challenges: (1) Tracking data flow in the instruction pipeline could comprehensively address this problem, but it could cause pipeline stalls and incur high performance overhead; (2) Making side effect of speculative execution imperceptible to attackers, but it often needs additional storage components and complicated data movement operations. In this paper, we propose a label-based transparent speculation scheme called SpecBox. It dynamically partitions the cache system to isolate speculative data and non-speculative data, which can prevent transient execution from being observed by subsequent execution. Moreover, it uses thread ownership semaphores to prevent speculative data from being accessed across cores. In addition, SpecBox also enhances the auxiliary components in the cache system against transient execution attacks, such as hardware prefetcher. Our security analysis shows that SpecBox is secure and the performance evaluation shows that the performance overhead on SPEC CPU 2006 and PARSEC-3.0 benchmarks is small.
rate research
Read More
Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks. We design and evaluate two novel graph attacks against a state-of-the-art network-level, graph-based detection system. Our work highlights areas in adversarial machine learning that have not yet been addressed, specifically: graph-based clustering techniques, and a global feature space where realistic attackers without perfect knowledge must be accounted for (by the defenders) in order to be practical. Even though less informed attackers can evade graph clustering with low cost, we show that some practical defenses are possible.
Trusted Execution Environments (TEEs) are used to protect sensitive data and run secure execution for security-critical applications, by providing an environment isolated from the rest of the system. However, over the last few years, TEEs have been proven weak, as either TEEs built upon security-oriented hardware extensions (e.g., Arm TrustZone) or resorting to dedicated secure elements were exploited multiple times. In this project, we introduce Trusted Execution Environments On-Demand (TEEOD), a novel TEE design that leverages the programmable logic (PL) in the heterogeneous system on chips (SoC) as the secure execution environment. Unlike other TEE designs, TEEOD can provide high-bandwidth connections and physical on-chip isolation. We implemented a proof-of-concept (PoC) implementation targeting an Ultra96-V2 platform. The conducted evaluation demonstrated TEEOD can host up to 6 simultaneous enclaves with a resource usage per enclave of 7.0%, 3.8%, and 15.3% of the total LUTs, FFs, and BRAMS, respectively. To demonstrate the practicability of TEEOD in real-world applications, we successfully run a legacy open-source Bitcoin wallet.
Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access to the victims memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victims confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victims process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, including operating system process separation, static analysis, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing/side-channel attacks. These attacks represent a serious threat to actual systems, since vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. While makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak.
As cross-chain technologies make the interactions among different blockchains (hereinafter chains) possible, multi-chains consensus is becoming more and more important in blockchain networks. However, more attention has been paid to the single-chain consensus schemes. The multi-chains consensus with trusted miners participation has been not considered, thus offering opportunities for malicious users to launch Diverse Miners Behaviors (DMB) attacks on different chains. DMB attackers can be friendly in the consensus process of some chains called mask-chains to enhance trust value, while on other chains called kill-chains they engage in destructive behaviors of network. In this paper, we propose a multi-chains consensus scheme named as Proof-of-DiscTrust (PoDT) to defend against DMB attacks. Distinctive trust idea (DiscTrust) is introduced to evaluate the trust value of each user concerning different chains. A dynamic behaviors prediction scheme is designed to strengthen DiscTrust to prevent intensive DMB attackers who maintain high trust by alternately creating true or false blocks on kill-chains. On this basis, a trusted miners selection algorithm for multi-chains can be achieved at a round of block creation. Experimental results show that PoDT is secure against DMB attacks and more effective than traditional consensus schemes in multi-chains environments.
Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. These studies show that modern cryptographic devices are increasingly threatened by side-channel attacks with the help of deep learning. However, the existing countermeasures are designed to resist classical side-channel attacks, and cannot protect cryptographic devices from deep learning based side-channel attacks. Thus, there arises a strong need for countermeasures against deep learning based side-channel attacks. Although deep learning has the high potential in solving complex problems, it is vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrectly. In this paper, we propose a kind of novel countermeasures based on adversarial attacks that is specifically designed against deep learning based side-channel attacks. We estimate several models commonly used in deep learning based side-channel attacks to evaluate the proposed countermeasures. It shows that our approach can effectively protect cryptographic devices from deep learning based side-channel attacks in practice. In addition, our experiments show that the new countermeasures can also resist classical side-channel attacks.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
