Before Toasters Rise Up: A View into the Emerging IoT Threat Landscape

Vervier, Pierre-Antoine; Shen, Yun

doi:10.1007/978-3-030-00470-5_26

Cited by 33 publications

(27 citation statements)

References 7 publications

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“…In this paper, Vervier and Shen [120] studied the behavior of cybercriminals by operating low-and high-interaction IoT honeypots. They found that both the diversity and complexity of IoT botnets are increasing, while the Mirai malware family is still dominant.…”

Section: Botnetmentioning

confidence: 99%

A Survey on Recent Advanced Research of CPS Security

et al. 2021

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Cyber-physical systems (CPSs) are next-generation intelligent systems that integrate computing, communication, and control. Malicious attacks on CPSs can lead to both property damage and casualties. Therefore, it is worth surveying CPS security by reviewing and analyzing the latest high-quality related works. In this paper, we provide an overview of the CPS security studies from the last five years and select 142 related works from A- or B-level conferences/journals recommended by the China Computer Federation (CCF). First, we review the main contents of the selected papers and classify them into 24 topics. Then, we analyze hotspots and trends of CPS security technologies in three dimensions: (1) architecture layers (perception, network, and application); (2) application scenarios (smart grids, health care, smart transportation, smart homes, and general grids); and (3) MADC (Measure, Attack, Defense, and Control) types. Finally, we also perform a statistical analysis in terms of paper publication times, author institutes, countries, and sponsors to show the current worldwide CPS security research situation.

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Section: Botnetmentioning

confidence: 99%

A Survey on Recent Advanced Research of CPS Security

et al. 2021

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“…In this work, we have used the source code of 20 IoT malware programs, which we compiled using various compiler options. We have focused on malware for this project because many recent research efforts [12,21,5,19,10] use binary disassemblers like IDA Pro to analyze IoT malware. We believe that our work will give malware researchers a better idea about the level of accuracy that they can expect from IDA Pro.…”

Section: Limitations and Future Workmentioning

confidence: 99%

“…IoT malware analysis. There have been several studies analyzing the behavior of IoT malware [5,19,17,10]. The studies focus mostly on the behavior, and the spread patterns of IoT malware and using static and dynamic analysis.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, IoT malware developers release the source code to the public as it has been the case for Mirai [4], and Lightaidra [3]. Black hat hackers make use of such source code to create malware that targets IoT devices to engage in malicious activities [19]. Further, the software eco-system for IoT devices is quite heterogeneous and not as mature with respect to security [10].…”

Section: Introductionmentioning

confidence: 99%

“…The closest related work by Andriesse et al [2] evaluates the performance of 9 disassemblers for the x86 architecture (PC-based) on benign binaries. Other related work can be grouped into the following categories: (a) static analysis of malware [7,14]; (b) disassemblers in malware analysis: using disassemblers in analyzing the malware structure [15,17]; (c) IoT binary analysis: analyzing code similarities of IoT firmware to identify known vulnerabilities [12,21]; and (d) IoT malware analysis: analyzing the behavior of IoT malware [5,19,10]. We discuss related work further in Section 6.…”

Section: Introductionmentioning

confidence: 99%

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2019 International Conference on Cyber Security for Emerging Technologies (CSET)

2019

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Defending against the threat of IoT malware will require new techniques and tools. An important security capability, that precedes a number of security analyses, is the ability to reverse engineer IoT malware binaries effectively. A key question is whether PC-oriented disassemblers can be effective on IoT malware, given the difference in the malware programs and the processors that support them. In this paper, we develop a systematic approach and a tool for evaluating the effectiveness of disassemblers on IoT malware binaries. The key components of the approach are: (a) we find the source code for 20 real-world malware programs, (b) we compile them to form a test set of 240 binaries using various compiler optimization options, device architectures, and considering both stripped and unstripped versions of the binaries, and (c) we establish the ground-truth for all these binaries for six disassembly accuracy metrics, such as the percentage of correctly disassembled instructions, and the accuracy of the control flow graph. Overall, we find that IDA Pro performs well for unstripped binaries with a precision and recall accuracy of over 85% for all the metrics. However, IDA Pro's performance deteriorates significantly with stripped binaries, mainly because the recall accuracy of identifying the start of functions drops to around 60% for both platforms. The results for the stripped ARM and MIPS binaries are similar to stripped x86 binaries in [2]. Interestingly, we find that most compiler optimization options, except the -O3 option for the MIPS architecture, do not cause any noticeable effect in the accuracy. We view our approach as an important capability for assessing and improving reverse engineering tools focusing on IoT malware.

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IoT malware detection using static and dynamic analysis techniques: A systematic literature review

Kumar,

Ahlawat,

Sahni

2024

Security and Privacy

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The Internet of Things (IoT) is reshaping the world with its potential to support new and evolving applications in areas, such as healthcare, automation, remote monitoring, and so on. This rapid popularity and growth of IoT‐based applications coincides with a significant surge in threats and malware attacks on IoT devices. Furthermore, the widespread usage of Linux‐based systems in IoT devices makes malware detection a challenging task. Researchers and practitioners have proposed a variety of techniques to address these threats in the IoT ecosystem. Both researchers and practitioners have proposed a range of techniques to counter these threats within the IoT ecosystem. However, despite the multitude of proposed techniques, there remains a notable absence of a comprehensive and systematic review assessing the efficacy of static and dynamic analysis methods in detecting IoT malware. This research work is a systematic literature review (SLR) that aims to offer a concise summary of the latest advancements in the field of IoT malware detection, specifically focusing on the utilization of static and dynamic analytic techniques. The SLR focuses on examining the present status of research, methodology, and trends in the area of IoT malware detection. It accomplishes this by synthesizing the findings from a wide range of scholarly works that have been published in well‐regarded academic journals and conferences. Additionally, the SLR highlights the significance of the empirical process that includes the role of selecting datasets, accurate feature selection and the utilization of machine learning algorithms in enhancing the detection accuracy. The study also evaluates the capability of different analysis techniques to detect malware and compares the performance of various models for IoT malware detection. Furthermore, the review concluded by addressing several open issues and challenges that the research community as a whole must address.

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Before Toasters Rise Up: A View into the Emerging IoT Threat Landscape

Cited by 33 publications

References 7 publications

A Survey on Recent Advanced Research of CPS Security

A Survey on Recent Advanced Research of CPS Security

2019 International Conference on Cyber Security for Emerging Technologies (CSET)

IoT malware detection using static and dynamic analysis techniques: A systematic literature review

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