Project Achilles: A Prototype Tool for Static MethodLevel Vulnerability Detection of Java Source Code Using a Recurrent Neural Network Software has become an essential component of modern life, but when software vulnerabilities threaten the security of users, new ways of analyzing for software security must be explored. Using the National Institute of Standards and Technologyu0027s Juliet Java Suite, containing thousands of examples of defective Java methods for a variety of vulnerabilities, a prototype tool was developed implementing an array of LongShort Term Memory Recurrent Neural Networks to detect vulnerabilities within source code. The tool employs various data preparation methods to be independent of coding style and to automate the process of extracting methods, labeling data, and partitioning the dataset. The result is a prototype commandline utility that generates an ndimensional vulnerability prediction vector. The experimental evaluation using 44,495 test cases indicates that the tool can achieve an accuracy higher than 90% for 24 out of 29 different types of CWE vulnerabilities.

RENN: Efficient Reverse Execution with NeuralNetworkAssisted Alias Analysis Reverse execution and coredump analysis have long been used to diagnose the root cause of software crashes. Each of these techniques, however, face inherent challenges, such as insufficient capability when handling memory aliases. Recent works have used hypothesis testing to address this drawback, albeit with high computational complexity, making them impractical for real world applications. To address this issue, we propose a new deep neural architecture, which could significantly improve memory alias resolution. At the high level, our approach employs a recurrent neural network (RNN) to learn the binary code pattern pertaining to memory accesses. It then infers the memory region accessed by memory references. Since memory references to different regions naturally indicate a nonalias relationship, our neural architecture can greatly reduce the burden of doing hypothesis testing to track down nonalias relation in binary code. Different from previous researches that have utilized deep learning for other binary analysis tasks, the neural network proposed in this work is fundamentally novel. Instead of simply using offtheshelf neural networks, we designed a new recurrent neural architecture that could capture the data dependency between machine code segments. To demonstrate the utility of our deep neural architecture, we implement it as RENN, a neural networkassisted reverse execution system. We utilize this tool to analyze software crashes corresponding to 40 memory corruption vulnerabilities from the real world. Our experiments show that RENN can significantly improve the efficiency of locating the root cause for the crashes. Compared to a stateoftheart technique, RENN has 36.25% faster execution time on average, detects an average of 21.35% more nonalias pairs, and successfully identified the root cause of 12.5% more cases.