Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection

Wang, Huanting; Ye, Guixin; Tang, Zhanyong; Tan, Shin Hwei; Huang, Songfang; Fang, Dingyi; Feng, Yansong; Bian, Lizhong; Wang, Zheng

doi:10.1109/tifs.2020.3044773

Cited by 164 publications

(86 citation statements)

References 51 publications

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“…Six of the research works developed framework to automate vulnerability detection in software product (Wang et al 2015;Bagri & Gupta, 2019;Min et al 2019;Wang et al 2020;Zarakovitis et al 2021). Min et al (2019) designed an Android software vulnerability mining framework based on dynamic taint analysis technology.…”

Section: Analysis and Resultsmentioning

confidence: 99%

A systematic literature review of software vulnerability detection

2022

EJCSIT

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This study provided a systematic literature review of software vulnerability detection (SVD) by searching ACM and IEEE databases for related literatures. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart, a total of 55 studies published in the selected journals and conference proceeding of IEEE and ACM from 2015 to 2021 were reviewed. The objective is to identify, select and critically evaluate research works carried out on software vulnerability detection. The selected articles were grouped into 7 categories across various vulnerability detection evaluation criteria such as neural network – 5 papers, machine learning – 11 papers, static and dynamic analysis – 8 papers, code clone – 3 papers, classification – 4 papers, models – 3 papers, and frameworks – 6 papers. There are 15 articles that could not fall into any of these 7 categories, thus, they were place in others row that used different criteria to implement vulnerability detection. The result showed that many researchers used machine learning strategy to detect vulnerability in software since large volume of data can be reviewed easily with machine learning. Although many systems have been developed for detecting software vulnerability, none is able to show the type of vulnerability detected.

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Section: Analysis and Resultsmentioning

confidence: 99%

A systematic literature review of software vulnerability detection

2022

EJCSIT

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“…Predictive modeling. Machine learning has been used to model power consumption [34], task scheduling [74,93,105] of mobile systems, program tuning and modeling in general [12,15,18,19,58,65,97,100,102,104,106,107,110,111,117]. Our work is the first to employ machine learning for 4K MAR offloading.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Adaptive Computation Offloading for Mobile Augmented Reality

Ren¹,

Gao²,

Wang³

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Augmented reality (AR) underpins many emerging mobile applications, but it increasingly requires more computation power for better machine understanding and user experience. While computation offloading promises a solution for high-quality and interactive mobile AR, existing methods work best for high-definition videos but cannot meet the real-time requirement for emerging 4K videos due to the long uploading latency. We introduce ACTOR, a novel computation-offloading framework for 4K mobile AR. To reduce the uploading latency, ACTOR dynamically and judiciously downscales the mobile video feed to be sent to the remote server. On the server-side, it leverages image super-resolution technology to scale back the received video so that high-quality object detection, tracking and rendering can be performed on the full 4K resolution. ACTOR employs machine learning to predict which of the downscaling resolutions and super-resolution configurations should be used, by taking into account the video content, server processing delay, and user expected latency. We evaluate ACTOR by applying it to over 2,000 4K video clips across two typical WiFi network settings. Extensive experimental results show that ACTOR consistently and significantly outperforms competitive methods for simultaneously meeting the latency and user-perceived video quality requirements.

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“…], [23], [24], [25], [33], [34], [35], [36], [37], our RL-based solution has the benefit of not requiring labelled a large number of training samples to train the model. Obtaining sufficient and representative training samples to cover a diverse set of workloads seen in deployment have been shown to be difficult [38], [39], [40], [41]. Without a large and sufficient training dataset, a supervised learning method often delivers poor performance during real-life uses as the target program behavior can be significantly different from those seen at the training phase.…”

Section: Introductionmentioning

confidence: 99%

Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach

Wang

Zhang

Hao

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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Power and energy is the first-class design constraint for multi-core processors and is a limiting factor for future-generation supercomputers. While modern processor design provides a wide range of mechanisms for power and energy optimization, it remains unclear how software can make the best use of them. This paper presents a novel approach for runtime power optimization on modern multi-core systems. Our policy combines power capping and uncore frequency scaling to match the hardware power profile to the dynamically changing program behavior at runtime. We achieve this by employing reinforcement learning (RL) to automatically explore the energy-performance optimization space from training programs, learning the subtle relationships between the hardware power profile, the program characteristics, power consumption and program running times. Our RL framework then uses the learned knowledge to adapt the chip's power budget and uncore frequency to match the changing program phases for any new, previously unseen program. We evaluate our approach on two computing clusters by applying our techniques to 11 parallel programs that were not seen by our RL framework at the training stage. Experimental results show that our approach can reduce the system-level energy consumption by 12%, on average, with less than 3% of slowdown on the application performance. By lowering the uncore frequency to leave more energy budget to allow the processor cores to run at a higher frequency, our approach can reduce the energy consumption by up to 17% while improving the application performance by 5% for specific workloads.

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Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection

Cited by 164 publications

References 51 publications

A systematic literature review of software vulnerability detection

A systematic literature review of software vulnerability detection

Adaptive Computation Offloading for Mobile Augmented Reality

Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach

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