Decompilation is a process of converting a low-level machine code snippet back into a high-level programming language such as C. It serves as a basis to aid reverse engineers in comprehending the contextual semantics of the code. In this respect, commercial decompilers like Hex-Rays have made significant strides in improving the readability of decompiled code over time. While previous work has proposed the metrics for assessing the readability of source code, including identifiers, variable names, function names, and comments, those metrics are unsuitable for measuring the readability of decompiled code primarily due to i) the lack of rich semantic information in the source and ii) the presence of erroneous syntax or inappropriate expressions. In response, to the best of our knowledge, this work first introduces R2I, the Relative Readability Index, a specialized metric tailored to evaluate decompiled code in a relative context quantitatively. In essence, R2I can be computed by i) taking code snippets across different decompilers as input and ii) extracting pre-defined features from an abstract syntax tree. For the robustness of R2I, we thoroughly investigate the enhancement efforts made by existing decompilers and academic research to promote code readability, identifying 31 features to yield a reliable index collectively. Besides, we conducted a user survey to capture subjective factors such as one’s coding styles and preferences. Our empirical experiments demonstrate that R2I is a versatile metric capable of representing the relative quality of decompiled code (e.g., obfuscation, decompiler updates) and being well aligned with human perception in our survey.

Binary reverse engineering is crucial to gain insights into the inner workings of a stripped binary. Yet, it is challenging to read the original semantics from a binary code snippet because of the unavailability of high-level information in the source, such as function names, variable names, and types. Recent advancements in deep learning show the possibility of recovering such vanished information with a well-trained model from a pre-defined dataset. Albeit a static model’s notable performance, it can hardly cope with ever-increasing data stream (e.g., compiled binaries) by nature. The two viable approaches for ceaseless learning are retraining the whole dataset from scratch and fine-tuning a pre-trained model; however, retraining suffers from large computational overheads and fine-tuning from performance degradation (i.e., catastrophic forgetting). Lately, continual learning (CL) tackles the problem of handling incremental data in security domains (e.g., network intrusion detection, malware detection) using reasonable resources while maintaining performance in practice. In this paper, we focus on how CL assists the improvement of a generative model that predicts a function symbol name from a series of machine instructions. To this end, we introduce BinAdapter, a system that can infer function names from an incremental dataset without performance degradation from an original dataset by leveraging CL techniques. Our major finding shows that incremental tokens in the source (i.e., machine instructions) or the target (i.e., function names) largely affect the overall performance of a CL-enabled model. Accordingly, BinAdapter adopts three built-in approaches: i) inserting adapters in case of no incremental tokens in both the source and target, ii) harnessing multilingual neural machine translation (M-NMT) and fine-tuning the source embeddings with i) in case of incremental tokens in the source, and iii) fine-tuning target embeddings with ii) in case of incremental tokens in both. To demonstrate the effectiveness of BinAdapter, we evaluate the above three scenarios using incremental datasets with or without a set of new tokens (e.g., unseen machine instructions or function names), spanning across different architectures and optimization levels. Our empirical results show that BinAdapter outperforms the state-of-the-art CL techniques for an F1 of up to 24.3% or a Rouge-l of 21.5% in performance.

Fuzzing has demonstrated great success in bug discovery and plays a crucial role in software testing today. Despite the increasing popularity of fuzzing, automated root cause analysis (RCA) has drawn less attention. One of the recent advances in RCA is crash-based statistical debugging, which leverages the behavioral differences in program execution between crash-triggered and non-crashing inputs. Hence, obtaining non-crashing behaviors close to the original crash is crucial but challenging with previous approaches (e.g., fuzzing). In this paper, we present BENZENE, a practical end-to-end RCA system that facilitates a fully automated crash diagnosis. To this end, we introduce a novel technique, called under-constrained state mutation, that generates both crashing and non-crashing behaviors for effective and efficient RCA. We design and implement the BENZENE prototype, and evaluate it with 60 vulnerabilities in the wild. Our empirical results demonstrate that BENZENE not only surpasses in performance (i.e., root cause ranking), but also achieves superior results in both speed (4.6 times faster) and memory footprint (31.4 times less) on average than prior approaches.

A smart contract is a self-executing program on a blockchain to ensure an immutable and transparent agreement without the involvement of intermediaries. Despite its growing popularity for many blockchain platforms like Ethereum, no technical means is available even when a smart contract requires to be protected from being copied. One of promising directions to claim a software ownership is software watermarking. However, applying existing software watermarking techniques is challenging because of the unique properties of a smart contract, such as a code size constraint, non-free execution cost, and no support for dynamic allocation under a virtual machine environment. This paper introduces a novel software watermarking scheme, dubbed SMARTMARK, aiming to protect the ownership of a smart contract against a pirate activity. SMARTMARK builds the control flow graph of a target contract runtime bytecode, and locates a collection of bytes that are randomly elected for representing a watermark. We implement a full-fledged prototype for Ethereum, applying SMARTMARK to 27,824 unique smart contract bytecodes. Our empirical results demonstrate that SMARTMARK can effectively embed a watermark into a smart contract and verify its presence, meeting the requirements of credibility and imperceptibility while incurring an acceptable performance degradation. Besides, our security analysis shows that SMARTMARK is resilient against viable watermarking corruption attacks; e.g., a large number of dummy opcodes are needed to disable a watermark effectively, resulting in producing an illegitimate smart contract clone that is not economical.

Reverse engineering of a stripped binary has a wide range of applications, yet it is challenging mainly due to the lack of contextually useful information within. Once debugging symbols (e.g., variablenames, types, function names) are discarded, recovering such informationis not technically viable with traditional approacheslike static or dynamic binary analysis. We focus on a functionsymbol name recovery, which allows a reverse engineer to gaina quick overview of an unseen binary. The key insight is that awell-developed program labels a meaningful function name thatdescribes its underlying semantics well. In this paper, we presentAsmDepictor, the Transformer-based framework that generates afunction symbol name from a set of assembly codes (i.e., machine instructions),which consists of three major components: binary coderefinement, model training, and inference. To this end, we conductsystematic experiments on the effectiveness of code refinement thatcan enhance an overall performance. We introduce the per-layerpositional embedding and Unique-softmax for AsmDepictor sothat both can aid to capture a better relationship between tokens.Lastly, we devise a novel evaluation metric tailored for a short descriptionlength, the Jaccard* score. Our empirical evaluation showsthat the performance of AsmDepictor by far surpasses that of thestate-of-the-art models up to around 400%. The best AsmDepictormodel achieves an F1 of 71.5 and Jaccard* of 75.4.

Binary code similarity detection serves as a basis for a wide spectrum of applications, including software plagiarism, malware classification, and known vulnerability discovery. However, the inference of contextual meanings of a binary is challenging due to the absence of semantic information available in source codes. Recent advances leverage the benefits of a deep learning architecture into a better understanding of underlying code semantics and the advantages of the Siamese architecture into better code similarity detection. In this paper, we propose BinShot, a BERT-based similarity learning architecture that is highly transferable for effective binary code similarity detection. We tackle the problem of detecting code similarity with one-shot learning (a special case of few-shot learning). To this end, we adopt a weighted distance vector with a binary cross entropy as a loss function on top of BERT. With the prototype implementation of BinShot, our experimental results demonstrate the effectiveness, transferability, and practicality of BinShot, which is robust to detecting the similarity of previously unseen functions.We show that BinShot outperforms the previous state-of-the-art approaches for binary code similarity detection.