Applicability of Intrusion Detection System on Ethereum Attacks: A Comprehensive Review

Kabla, Arkan Hammoodi Hasan; Anbar, Mohammed; Manickam, Selvakumar; Al-Amiedy, Taief Alaa; Cruspe, Peterson Bernabe; Al-Ani, Ahmed; Karuppayah, Shankar

doi:10.1109/access.2022.3188637

Cited by 13 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These particles then update their positions and velocities based on historical data, aiming to converge toward the optimal position. This process is demonstrated by Equation ( 9) and Equation (10), which illustrate the updating of particle positions and velocities, respectively [47].…”

Section: ) Feature Selection Based Psomentioning

confidence: 99%

“…The importance of an IDS in Ethereum lies in its ability to provide an additional layer of security to protect against attacks and malicious activity. By continuously monitoring network traffic and alerting on suspicious activity, an IDS can help to identify and prevent potential threats before they can cause harm [10]. Generative Adversarial Networks (GANs) comprise a pair of neural networks, namely the generator and the discriminator, which collaborate in the identification and classification of network data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Abnormal Transactions Detection in the Ethereum Network Using Semi-Supervised Generative Adversarial Networks

Sanjalawe,

Al-E’mari

2023

IEEE Access

View full text Add to dashboard Cite

Numerous abnormal transactions have been exposed as a result of targeted attacks on Ethereum, such as the Ethereum Decentralized Autonomous Organization attack. Exploiting vulnerabilities in smart contracts, malicious users can pursue their own illicit objectives through abnormal transactions. Consequently, identifying these malevolent users, implicated in fraudulent activities and their attribution, becomes exceedingly complex. Cryptocurrency transactions used for malicious purposes, employing pseudo-anonymous accounts to send and receive ransom payments and accumulating funds under various identities, further highlight the need to control and detect these abnormal transactions for maintaining a high level of security within the Ethereum network. Although existing Intrusion Detection Systems (IDSs) help mitigate abnormal transaction occurrences, their performance necessitates improvement. To address this issue, this study presents a novel approach, named Abnormal Transactions Detection Using a Semi-Supervised Generative Adversarial Network (ATD-SGAN), which efficiently detects abnormal attacks within the Ethereum network. ATD-SGAN leverages a semi-supervised generative adversarial network for this purpose. The results demonstrate that ATD-SGAN significantly enhances the performance of state-ofthe-art IDSs. It achieves an increase in detection accuracy from 3.78% to 11.05% and reduces the false alarm rate from 42.29% to 0.15%. Moreover, ATD-SGAN notably improves the F1-measure, ranging from 10.39% to 3.79%, compared to the current IDSs.

show abstract

Section: ) Feature Selection Based Psomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Abnormal Transactions Detection in the Ethereum Network Using Semi-Supervised Generative Adversarial Networks

Sanjalawe,

Al-E’mari

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…An example of a common detection approach is the Intrusion Detection System (IDS) [26]. IDS is software that can identify attacks by distinguishing their intrusions as abnormal traffic [7,27]. Examples of common monitoring approaches are Sensors, Honeypots, or Crawlers, which will be discussed in the following subsections.…”

Section: P2p Botnet Monitoringmentioning

confidence: 99%

Monitoring Peer-to-Peer Botnets: Requirements, Challenges, and Future Works

Kabla¹,

Anbar²,

Manickam³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

The cyber-criminal compromises end-hosts (bots) to configure a network of bots (botnet). The cyber-criminals are also looking for an evolved architecture that makes their techniques more resilient and stealthier such as Peer-to-Peer (P2P) networks. The P2P botnets leverage the privileges of the decentralized nature of P2P networks. Consequently, the P2P botnets exploit the resilience of this architecture to be arduous against take-down procedures. Some P2P botnets are smarter to be stealthy in their Commandand-Control mechanisms (C2) and elude the standard discovery mechanisms. Therefore, the other side of this cyberwar is the monitor. The P2P botnet monitoring is an exacting mission because the monitoring must care about many aspects simultaneously. Some aspects pertain to the existing monitoring approaches, some pertain to the nature of P2P networks, and some to counter the botnets, i.e., the anti-monitoring mechanisms. All these challenges should be considered in P2P botnet monitoring. To begin with, this paper provides an anatomy of P2P botnets. Thereafter, this paper exhaustively reviews the existing monitoring approaches of P2P botnets and thoroughly discusses each to reveal its advantages and disadvantages. In addition, this paper groups the monitoring approaches into three groups: passive, active, and hybrid monitoring approaches. Furthermore, this paper also discusses the functional and non-functional requirements of advanced monitoring. In conclusion, this paper ends by epitomizing the challenges of various aspects and gives future avenues for better monitoring of P2P botnets.

show abstract

“…Their work was devoted to exploring various verification techniques rather than being limited to a single technology (such as formal verification), thus offering a comprehensive overview of various smart-contract security verification methods. Kabla et al [21] conducted a comprehensive investigation on the applicability of Intrusion Detection Systems (IDS) for Ethereum attacks. This study examined the effectiveness of various IDS technologies in addressing security issues and detecting potential threats on the Ethereum blockchain.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Smart-Contract Security through Machine Learning: A Survey of Approaches and Techniques

et al. 2023

View full text Add to dashboard Cite

As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain.

show abstract

Applicability of Intrusion Detection System on Ethereum Attacks: A Comprehensive Review

Cited by 13 publications

References 57 publications

Abnormal Transactions Detection in the Ethereum Network Using Semi-Supervised Generative Adversarial Networks

Abnormal Transactions Detection in the Ethereum Network Using Semi-Supervised Generative Adversarial Networks

Monitoring Peer-to-Peer Botnets: Requirements, Challenges, and Future Works

Enhancing Smart-Contract Security through Machine Learning: A Survey of Approaches and Techniques

Contact Info

Product

Resources

About