VulSeeker-pro: enhanced semantic learning based binary vulnerability seeker with emulation

Gao, Jian; Yang, Xin; Fu, Ying; Jiang, Yu; Shi, Heyuan; Sun, Jun

doi:10.1145/3236024.3275524

Cited by 32 publications

(25 citation statements)

References 12 publications

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“…It extracts three semantic features, including function, inter-function, and inter-module features, to detect based on the Deep Neural Networks (DNN) model. Gao et al also proposed VulSeeker [95] and VulSeeker-Pro [96], those vulnerability search methods combined with a deep learning model to improve the accuracy of vulnerability detection. These two methods were verified to be more accurate than existing methods such as Gemini [93].…”

Section: Technical Requirementsmentioning

confidence: 99%

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A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices

Zhuge

Cao

et al. 2020

Future Internet

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With the prosperity of the Internet of Things (IoT) industry environment, the variety and quantity of IoT devices have grown rapidly. IoT devices have been widely used in smart homes, smart wear, smart manufacturing, smart cars, smart medical care, and many other life-related fields. With it, security vulnerabilities of IoT devices are emerging endlessly. The proliferation of security vulnerabilities will bring severe risks to users’ privacy and property. This paper first describes the research background, including IoT architecture, device components, and attack surfaces. We review state-of-the-art research on IoT device vulnerability discovery, detection, mitigation, and other related works. Then, we point out the current challenges and opportunities by evaluation. Finally, we forecast and discuss the research directions on vulnerability analysis techniques of IoT devices.

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Section: Technical Requirementsmentioning

confidence: 99%

“…We find that arm-based Linux devices such as routers are selected as research targets mostly at this stage. The research on similarity detection expands cross-architecture scenarios [88][89][90][91][93][94][95][96]; others do not challenge this issue.…”

Section: Challengesmentioning

confidence: 99%

A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices

Zhuge

Cao

et al. 2020

Future Internet

View full text Add to dashboard Cite

show abstract

“…Such applications are of pa interest to hackers, because finding vulnerabilities in them does not require special embedded systems background. There are many different file systems for embedded devices including SquashFS 26 , UBIFS 27 , YAFFS2 28 , and JFFS2 29 .…”

Section: Firmware Extractionmentioning

confidence: 99%

“…blocks of IR instructions [24] and conditional formulas [25]. Recently, machine learning algorithms have also been leveraged in order to quickly find code similar to a known vulnerable component [26], [27], [28].…”

Section: Finding Potentially Vulnerable Componentsmentioning

confidence: 99%

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IoT Hacking – A Primer

Papp¹,

Tamás²,

Buttyán³

2019

Infocommunications journal

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The Internet of Things (IoT) enables many new and exciting applications, but it also creates a number of new risks related to information security. Several recent attacks on IoT devices and systems illustrate that they are notoriously insecure. It has also been shown that a major part of the attacks resulted in full adversarial control over IoT devices, and the reason for this is that IoT devices themselves are weakly protected and they often cannot resist even the most basic attacks. Penetration testing or ethical hacking of IoT devices can help discovering and fixing their vulnerabilities that, if exploited, can result in highly undesirable conditions, including damage of expensive physical equipment or even loss of human life. In this paper, we give a basic introduction into hacking IoT devices. We give an overview on the methods and tools for hardware hacking, firmware extraction and unpacking, and performing basic firmware analysis. We also provide a survey on recent research on more advanced firmware analysis methods, including static and dynamic analysis of binaries, taint analysis, fuzzing, and symbolic execution techniques. By giving an overview on both practical methods and readily available tools as well as current scientific research efforts, our work can be useful for both practitioners and academic researchers.

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Focus : Function clone identification on cross‐platform

Liu

et al. 2021

Int J of Intelligent Sys

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Automatic identification of function clones on crossplatform aims at determining whether two functions are identical or not without access to the source code, which is a fundamental challenge in vulnerability search, code plagiarism detection, and malware classification. With the rapid development of deep neural network in pro-K E Y W O R D S attention mechanism, binary similarity, graph neural network, program analysis | INTRODUCTIONBinary code similarity detection is a key foundation in maintaining software security, which can be generally applied in searching critical vulnerability 1 and detecting malware, 2 and so forth. Today, with the booming development of billions of Internet of Things (IoT) devices, the similarity analysis of binary code has become even more important than ever, because (1) code reuse is quite common in IoT which spreads a single vulnerability at source code level across thousands or more embedded devices that have diverse hardware architectures and software platforms 3 and (2) a known vulnerability that has been exploited in the past can also cause unprecedented damage and significant revenue losses to our daily life. 4 Worryingly, accurate and efficient analysis of stripped binary code is difficult because debug information such as symbol names, types, and location of commercial off-the-shelf (COTS) binary is unavailable. Besides, compared to binary code similarity detection on single-platform, function identification on cross-platform needs to deal with the binary code differences caused by various architectures, optimization levels, and compilers, which is even more challenging.Traditional binary code similarity detection approaches 5 adopt symbolic execution, constraint solving, or theorem prover to directly check binary code equivalence. However, these methods are inefficient especially when the number of function pairs is large, making them difficult to be utilized in large-scale applications. Graph matching-based methods use program flow diagrams to represent functions and detect similar functions. [6][7][8][9][10][11] These methods are usually inaccurate or their detection performance is limited by the efficiency of graph matching algorithms. 1,12 For instance, Genius 10 takes more than 1 week to construct their proposed codebook for only three software packages and the time complexity is quadratic in the number of training samples and linear in the cost of the bipartite graph matching algorithm.Recently, with the rapid development of deep learning and graph neural network (GNN), 13,14 graph matching and classification problems such as social network graph analytics 15 and chemical formula matching 13 can be solved well by transforming a representative graph to an embedding.

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VulSeeker-pro: enhanced semantic learning based binary vulnerability seeker with emulation

Cited by 32 publications

References 12 publications

A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices

A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices

IoT Hacking – A Primer

Focus : Function clone identification on cross‐platform

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