Code Characterization With Graph Convolutions and Capsule Networks

Haridas, P.; Chennupati, Gopinath; Santhi, Nandakishore; Romero, Phillip; Eidenbenz, Stephan

doi:10.1109/access.2020.3011909

Cited by 18 publications

(8 citation statements)

References 26 publications

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“…While in reality, when testing and evaluating software vulnerabilities, there is a big difference between vulnerable and non-vulnerable source code about record counts. Because of these problems, recent studies often focus on combining graph analysis techniques with machine learning or deep learning algorithms [3,4,12,13,14,15,16]. Accordingly, the approaches often extract source code into graphs such as Abstract syntax tree (AST) [17], Control Flow Graph (CFG) [18], and Program Dependence Graph (PDG) [19], and then use machine learning or deep learning techniques to classify vulnerable and nonvulnerable source code.…”

Section: Problemmentioning

confidence: 99%

DrCSE: A New Framework For Software Vulnerability Detection base on An Advanced Computing

Cong,

Xuan

2023

Preprint

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The detection of software vulnerabilities written in C and C++ languages takes a lot of attention and interest today. This paper proposes a new framework called DrCSE to improve software vulnerability detection. It uses an intelligent computation tech-nique based on the combination of two methods: rebalancing data and representation learning to analyze and evaluate the code property graph (CPG) of the source code for detecting abnormal behavior of software vulnerabilities. To do that, DrCSE performs a combination of 3 main processing techniques: i) building the source code feature profiles, ii) rebalancing data, and iii) contrastive learning. In which, the method i) extracts the source code’s features based on the vertices and edges of the CPG. The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset. Finally, contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away. The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabil-ities using the Verum dataset. As a result, the method proposed in the article has brought a pretty good performance in all metrics, especially the Precision and Recall scores of 39.35% and 69.07%, respectively, proving the eﬃciency of the DrCSE Framework. It performs better than other approaches, with a 5% boost in Precision and a 5% boost in Recall. Overall, this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date

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Section: Problemmentioning

confidence: 99%

DrCSE: A New Framework For Software Vulnerability Detection base on An Advanced Computing

Cong,

Xuan

2023

Preprint

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“…Moreover, DL applicability is also found in several areas, such as fraud detection, AD, consensus optimization, and privacy-preserving smart contracts. In summary, the collaboration among researchers from academic and government institutions has resulted in the development of SiCaGCN [235]. In the current BC landscape, scalability poses a significant bottleneck as transaction frequency increases, impeding the overall growth of BC systems.…”

Section: ) Dl/graph Convolutional Neural Network (Gcn) Enabled Tackli...mentioning

confidence: 99%

Qualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond

Pathak,

Pandya,

Bhatia

et al. 2023

IEEE Access

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Utilizing the 0.1 to 10 THz spectrum in the next-generation wireless communication networks holds potential for futuristic applications. However, managing resources to accommodate numerous devices raises privacy and security concerns. Further, technology proliferation entwines devices, infrastructure complexity, and resources. Indeed, the transition from 5G (fifth-generation) to 6G (sixth-generation) signifies a progression towards high-speed data rates, minimal latency, and seamless integration of artificial intelligence, enabling ground-breaking applications and services. However, it complicates network management, privacy, resource allocation, and data processing. Notably, integrating Blockchain Technology (BCT) and Machine Learning (ML) is a promising solution, enhancing security, decentralization, trust in ML decisions, and efficient data sharing. This survey thoroughly reviews the integrated ML and BCT, showcasing their collaborative enhancement of network security, decentralization, trust in ML decisions, immutability, and efficient model sharing. Furthermore, we also delve into various distinctive topics, such as BCT-enabled spectrum refarming, rate splitting multiple access, 6G radar-based communication, reconfigurable intelligent surfaces, visible light communication, and integrated sensing and communication. Moreover, it also explores the integration of ML and BCT in novel 6G communication technologies, including molecular, holographic, and semantic communication. Finally, critical open issues, challenges, solutions, and futuristic scope are identified for forthcoming researchers. INDEX TERMS Terahertz (THz) Communication, Artificial Intelligence (AI), Machine Learning (ML), Blockchain Technology (BCT), and Resource Allocation (RA).

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“…Nowadays, investments in cryptocurrencies are more reliable than before. For example, many researchers are working on the security of cryptocurrencies to prevent cryptojacking [19] [25]. Plenty of research is already carried out to make crypto mining efficient by reducing the mining cost [26].…”

Section: A Preliminariesmentioning

confidence: 99%

“…Also, the researchers, companies, startups, and universities across the globe are working to make this new technology more reliable, mature, and secure. Many researchers are working to make crypto mining more efficient, cheaper [17], [18] and to prevent cryptojacking [19].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

Tanwar¹,

Patel

et al. 2021

IEEE Access

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Blockchain technology is becoming increasingly popular because of its applications in various fields. It gives an edge over the traditional centralized methods as it provides decentralization, immutability, integrity, and anonymity. The most popular application of this technology is cryptocurrencies, which showed a massive rise in their popularity and market capitalization in recent years. Individual investors, big institutions, and corporate firms are investing heavily in it. However, the crypto market is less stable than traditional commodity markets. It can be affected by many technical, sentimental, and legal factors, so it is highly volatile, uncertain, and unpredictable. Plenty of research has been done on various cryptocurrencies to forecast accurate prices, but the majority of these approaches can not be applied in real-time. Motivated from the aforementioned discussion, in this paper, we propose a deep-learning-based hybrid model (includes Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM)) to predict the price of Litecoin and Zcash with inter-dependency of the parent coin. The proposed model can be used in real-time scenarios and it is well trained and evaluated using standard data sets. Results illustrate that the proposed model forecasts the prices with high accuracy compared to existing models.

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Code Characterization With Graph Convolutions and Capsule Networks

Cited by 18 publications

References 26 publications

DrCSE: A New Framework For Software Vulnerability Detection base on An Advanced Computing

DrCSE: A New Framework For Software Vulnerability Detection base on An Advanced Computing

Qualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond

Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

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