Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Pham, Long H."'
Autor:
Pham, Long H., Sun, Jun
On the one hand, there has been considerable progress on neural network verification in recent years, which makes certifying neural networks a possibility. On the other hand, neural networks in practice are often re-trained over time to cope with new
Externí odkaz:
http://arxiv.org/abs/2407.06697
The application of deep neural network models in various security-critical applications has raised significant security concerns, particularly the risk of backdoor attacks. Neural backdoors pose a serious security threat as they allow attackers to ma
Externí odkaz:
http://arxiv.org/abs/2405.14781
Fuzzing has emerged as a powerful technique for finding security bugs in complicated real-world applications. American fuzzy lop (AFL), a leading fuzzing tool, has demonstrated its powerful bug finding ability through a vast number of reported CVEs.
Externí odkaz:
http://arxiv.org/abs/2307.02289
Heap-manipulating programs are known to be challenging to reason about. We present a novel verifier for heap-manipulating programs called S2TD, which encodes programs systematically in the form of Constrained Horn Clauses (CHC) using a novel extensio
Externí odkaz:
http://arxiv.org/abs/2209.09327
Autor:
Pham, Long H., Sun, Jun
Neural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is
Externí odkaz:
http://arxiv.org/abs/2205.06992
Smart contracts are distributed, self-enforcing programs executing on top of blockchain networks. They have the potential to revolutionize many industries such as financial institutes and supply chains. However, smart contracts are subject to code-ba
Externí odkaz:
http://arxiv.org/abs/2101.01917
Studies show that neural networks, not unlike traditional programs, are subject to bugs, e.g., adversarial samples that cause classification errors and discriminatory instances that demonstrate the lack of fairness. Given that neural networks are inc
Externí odkaz:
http://arxiv.org/abs/2007.11206
Smart contracts are Turing-complete programs that execute on the infrastructure of the blockchain, which often manage valuable digital assets. Solidity is one of the most popular programming languages for writing smart contracts on the Ethereum platf
Externí odkaz:
http://arxiv.org/abs/2004.08563
Analyzing and verifying heap-manipulating programs automatically is challenging. A key for fighting the complexity is to develop compositional methods. For instance, many existing verifiers for heap-manipulating programs require user-provided specifi
Externí odkaz:
http://arxiv.org/abs/1908.10051
Concolic testing is a test generation technique which works effectively by integrating random testing generation and symbolic execution. Existing concolic testing engines focus on numeric programs. Heap-manipulating programs make extensive use of com
Externí odkaz:
http://arxiv.org/abs/1907.05637