The rapid development of Internet of Things (IoT) has triggered more security requirements than ever, especially in detecting vulnerabilities in various IoT devices. The widely used clone-based vulnerability search methods are effective on source code; however, their performance is limited in IoT binary search. In this article, we present IoTSeeker, a function semantic learning based vulnerability search approach for cross-platform IoT binary. First, we construct the function semantic graph to capture both the data flow and control flow information and encode lightweight semantic features of each basic block within the semantic graph as numerical vectors. Then, the embedding vector of the whole binary function is generated by feeding the numerical vectors of basic blocks to our customized semantics aware neural network model. Finally, the cosine distance of two embedding vectors is calculated to determine whether a binary function contains a known vulnerability. The experiments show that IoTSeeker outperforms the state-of-the-art approaches for identifying cross-platform IoT binary vulnerabilities. For example, compared to Gemini, IoTSeeker finds 12.68% more vulnerabilities in the top-50 candidates, and improves the value of AUC for 8.23%.

Clone detection is widely exploited for software vulnerability search. The approaches based on source code analysis cannot be applied to binary clone detection because the same source code can produce significantly different binaries due to different operating systems, microprocessor architectures and compilers. In this paper, we presentBinSeeker, a cross-platform binary seeker that integrates semantic learning and emulation. With the help of the labeled semantic flow graph,BinSeekercan quickly identify$M$candidate functions that are most similar to the vulnerability from the target binary. The value of$M$is relatively large so this semantic learning procedure essentially eliminates those functions that are very unlikely to have the vulnerability. Then, semantic emulation is conducted on these$M$candidates to obtain their dynamic signature sequences. By comparing signature sequences,BinSeekerproduces top-$N$functions that exhibit most similar behavior to that of the vulnerability. With fast filtering of semantic learning and accurate comparison of semantic emulation,BinSeekerseeks vulnerability precisely with little overhead. The experiments on six widely used programs with fifteen known CVE vulnerabilities demonstrate thatBinSeekeroutperforms three state-of-the-art toolsGenius,GeminiandCACompare. Regarding search accuracy,BinSeekerachieves an MRR value of 0.65 in the target programs, whereas the MRR values byGenius,GeminiandCACompareare 0.17, 0.07 and 0.42, respectively. If we consider ranking a function with the targeted vulnerability in the top-5 as accurate,BinSeekerachieves the accuracy of 93.33 percent, while the accuracy of the other three tools is merely 33.33, 13.33 and 53.33 percent, respectively. Such accuracy is achieved with 0.27s on average to determine whether the target binary function contains a known vulnerability, and the time for the other three tools are 1.57s, 0.15s and 0.98s, respectively. Compared to the time used to manually identify the true positive vulnerability from the false positive candidates reported by Gemini, the time overhead ofBinSeekeris negligible. Evidently, the proposedBinSeekerachieves a better balance between accuracy and efficiency.

Embedded systems are increasingly interconnected in the emerging application scenarios. Many of these applications are safety critical, making it a high priority to ensure that the systems are free from malicious attacks. This work aims to detect vulnerabilities, that could be exploited by adversaries to compromise functional correctness, in the embedded firmware, which is challenging especially due to the absence of source code. In particular, we propose EM-Fuzz, a firmware vulnerability detection technique that tightly integrates fuzzing with real-time memory checking. Based on the memory instrumentation, the firmware fuzzing can not only be guided by the traditional branch coverage to generate high-quality seeds to explore hard-to-reach regions but also by the recorded memory sensitive operations to continuously exercise sensitive regions which are prone to being attacked. More importantly, the instrumentation integrates real-time memory checkers to expose memory vulnerabilities, which is not well-supported by existing fuzzers without source code. The experiments on several real-world embedded firmware such as OpenSSL demonstrate that EM-Fuzz significantly improves the performance of state-of-the-art fuzzing tools, such as AFL and AFLFast, with the coverage improvements of 93.98% and 46.89%, respectively. Furthermore, EM-Fuzz exposes a total of 23 vulnerabilities, with an average of about 7-h per vulnerability. AFL and AFLFast together find 10 vulnerabilities, costing about 13 h and 10-h per vulnerability on average, respectively. Out of these 23 vulnerabilities, 16 are previously unknown and have been reported to the upstream product vendors, 7 of which have been assigned with unique CVE identifiers in the U.S. National Vulnerability Database.

Golang (short for Go programming language) is a fast and compiled language, which has been increasingly used in industry due to its excellent performance on concurrent programming. Golang redefines concurrent programming grammar, making it a challenge for traditional clone detection tools and techniques. However, there exist few tools for detecting duplicates or copy-paste related bugs in Golang. Therefore, an effective and efficient code clone detector on Golang is especially needed.

Industrial Control System (ICS) protocols are widely used to build communications among system components. Compared with common internet protocols, ICS protocols have more control over remote devices by carrying a specific field called “function code”, which assigns what the receive end should do. Therefore, it is of vital importance to ensure their correctness. However, traditional vulnerability detection techniques such as fuzz testing are challenged by the increasing complexity of these diverse ICS protocols. In this paper, we present a function code aware fuzzing framework — Polar, which automatically extracts semantic information from the ICS protocol and utilizes this information to accelerate security vulnerability detection. Based on static analysis and dynamic taint analysis, Polar initiates the values of the function code field and identifies some vulnerable operations. Then, novel semantic aware mutation and selection strategies are designed to optimize the fuzzing procedure. For evaluation, we implement Polar on top of two popular fuzzers — AFL and AFLFast, and conduct experiments on several widely used ICS protocols such as Modbus, IEC104, and IEC 61850. Results show that, compared with AFL and AFLFast, Polar achieves the same code coverage and bug detection numbers at the speed of 1.5X-12X. It also gains increase with 0%--91% more paths within 24 hours. Furthermore, Polar has exposed 10 previously unknown vulnerabilities in those protocols, 6 of which have been assigned unique CVE identifiers in the US National Vulnerability Database.

As a result of the increasing number of concurrent programs, the researchers put forward a number of tools with different implementation strategies to detect data race. However, confirming data races from the collection of true and false positives reported by race detectors is extremely the time-consuming process during the evaluation period.

Code reuse improves software development efficiency, however, vulnerabilities can be introduced inadvertently. Many existing works compute the code similarity based on CFGs to determine whether a binary function contains a known vulnerability. Unfortunately, their performance in cross-platform binary search is challenged.

Race detection is increasingly popular, both in the academic research and in industrial practice. However, there is no specialized and comprehensive dataset of the data race, making it difficult to achieve the purpose of effectively evaluating race detectors or developing efficient race detection algorithms.

Learning-based clone detection is widely exploited for binary vulnerability search. Although they solve the problem of high time overhead of traditional dynamic and static search approaches to some extent, their accuracy is limited, and need to manually identify the true positive cases among the top-M search results during the industrial practice. This paper presents VulSeeker-Pro, an enhanced binary vulnerability seeker that integrates function semantic emulation at the back end of semantic learning, to release the engineers from the manual identification work. It first uses the semantic learning based predictor to quickly predict the top-M candidate functions which are the most similar to the vulnerability from the target binary. Then the top-M candidates are fed to the emulation engine to resort, and more accurate top-N candidate functions are obtained. With fast filtering of semantic learning and dynamic trace generation of function semantic emulation, VulSeeker-Pro can achieve higher search accuracy with little time overhead. The experimental results on 15 known CVE vulnerabilities involving 6 industry widely used programs show that VulSeeker-Pro significantly outperforms the state-of-the-art approaches in terms of accuracy. In a total of 45 searches, VulSeeker-Pro finds 40 and 43 real vulnerabilities in the top-1 and top-5 candidate functions, which are 12.33× and 2.58× more than the most recent and related work Gemini. In terms of efficiency, it takes 0.22 seconds on average to determine whether the target binary function contains a known vulnerability or not.

Building language models for source code enables a large set of improvements on traditional software engineering tasks. One promising application is automatic code completion. State-of-the-art techniques capture code regularities at token level with lexical information. Such language models are more suitable for predicting short token sequences, but become less effective with respect to long statement level predictions. In this paper, we have proposed PCC to optimize the token-level based language modeling. Specifically, PCC introduced an intermediate representation (IR) for source code, which puts tokens into groups using lexeme and variable relative order. In this way, PCC is able to handle long token sequences, i.e., group sequences, to suggest a complete statement with the precise synthesizer. Further more, PCC employed a fuzzy matching technique which combined genetic and longest common subsequence algorithms to make the prediction more accurate. We have implemented a code completion plugin for Eclipse and evaluated it on open-source Java projects. The results have demonstrated the potential of PCC in generating precise long statement level predictions. In 30%-60% of the cases, it can correctly suggest the complete statement with only six candidates, and 40%-90% of the cases with ten candidates.