Machine Learning for Detecting Security Attacks on Blockchain using Software Defined Networking

Gaba, Shivani; Budhiraja, Ishan; Makkar, Aaisha; Garg, Deepak

doi:10.1109/iccworkshops53468.2022.9814656

Cited by 17 publications

(4 citation statements)

References 9 publications

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“…Paper [99] predicted decentralized blockchain security by using the Long Short-Term Memory (LSTM) network, specifically testing whether LSTMbased neural networks can generate beneficial transaction signals for different blockchains. Paper [100] detected blockchain security attacks through machine learning and software-defined networking methods. It discusses anomaly identification methods centered on encoder-decoder prototypes, trained with collective information obtained by observing blockchain behavior.…”

Section: Research On Safety Assessment Methodsmentioning

confidence: 99%

Blockchain Security Threats and Collaborative Defense: A Literature Review

Li,

Cheng,

Shi

et al. 2023

Computers, Materials &Amp; Continua

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As a distributed database, the system security of the blockchain is of great significance to prevent tampering, protect privacy, prevent double spending, and improve credibility. Due to the decentralized and trustless nature of blockchain, the security defense of the blockchain system has become one of the most important measures. This paper comprehensively reviews the research progress of blockchain security threats and collaborative defense, and we first introduce the overview, classification, and threat assessment process of blockchain security threats. Then, we investigate the research status of single-node defense technology and multi-node collaborative defense technology and summarize the blockchain security evaluation indicators and evaluation methods. Finally, we discuss the challenges of blockchain security and future research directions, such as parallel detection and federated learning. This paper aims to stimulate further research and discussion on blockchain security, providing more reliable security guarantees for the use and development of blockchain technology to face changing threats and challenges through continuous updating and improvement of defense technologies.

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Section: Research On Safety Assessment Methodsmentioning

confidence: 99%

Blockchain Security Threats and Collaborative Defense: A Literature Review

Li,

Cheng,

Shi

et al. 2023

Computers, Materials &Amp; Continua

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“…A large amount of collected data can be utilized for diagnosis or diagnosis model development with the aid of numerous devices. However, the potential disclosure of patient data will potentially raise privacy and security [49] concerns during the contact period. To address these challenges, we offer DEEP-FEL, a decentralized, efficient, and privacy-enhanced federated edge learning system that enables medical devices from various institutions to jointly train a global model without exchanging raw data [50] .…”

Section: Sars-cov-2 Variantsmentioning

confidence: 99%

A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues

Deepanshi

Budhiraja

Garg

et al. 2023

Computer Communications

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“…An unsupervised encoder decoder prototype developed along with the benefits of SDN (Software defined networking) is presented and experimented on Ethereum classic dataset. 62 In contradiction to the existing supervised ML methods, the proposed technique uses an unsupervised model built upon irregular anomaly detection. Moreover, the experiment focuses on sensing Zero-Day attacks, which are highly unpredictable.…”

Section: Blockchain With Machine Learningmentioning

confidence: 99%

“…Another security breach for the Blockchain enabled application is from DAO attack. An unsupervised encoder decoder prototype developed along with the benefits of SDN (Software defined networking) is presented and experimented on Ethereum classic dataset 62 . In contradiction to the existing supervised ML methods, the proposed technique uses an unsupervised model built upon irregular anomaly detection.…”

Section: Anomaly Detection Using Integration Of Artificial Intelligen...mentioning

confidence: 99%

An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions – A review

Chithanuru

Ramaiah

2023

Concurrency and Computation

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SummaryRecently, Blockchain cryptographic distributed transaction ledger technology finds its usage in many applications. The application's ledgers implemented through Blockchain, ensures tamper‐proof transactions, and in turn the applications became robust enough against cyber‐attack But still adversaries put forward their efforts in detecting the vulnerabilities in the infrastructure to execute their ill intent. In the literature, many counter measures techniques are presented to address the security breaches on the Blockchain. Detecting as well mitigating from the possible anomalies against on blockchain infrastructure through AI techniques is the greatest attempt of this article, and which is much needed now. Hence, this review article enlightens the readers with the essence of cyber security, the security aspects of Blockchain, its infrastructure vulnerabilities, various Blockchain‐enabled use cases along with the their challenges. Primarily, anomaly detection on Blockchain infrastructure through Artificial Intelligence Techniques is focused. A detailed analysis of Artificial Intelligence Techniques in detecting the anomalies with the help of Blockchain and also how these two technologies complement each other was demonstrated with the help of suitable use cases. The merits, challenges along with the possible future directions, while integrating Blockchain with Artificial Intelligence Techniques are presented for the benefit of research community.

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Machine Learning for Detecting Security Attacks on Blockchain using Software Defined Networking

Cited by 17 publications

References 9 publications

Blockchain Security Threats and Collaborative Defense: A Literature Review

Blockchain Security Threats and Collaborative Defense: A Literature Review

A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues

An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions – A review

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