Survey on extreme learning machines for outlier detection

Kiani, Rasoul; Jin, Wei; Sheng, Victor S.

doi:10.1007/s10994-023-06375-0

Cited by 4 publications

(4 citation statements)

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“…These techniques modify the training distributions to decrease the level of imbalance or reduce noise, e.g., mislabeled samples or anomalies. In their simplest forms, random under-sampling discards random samples from the majority group, while random over-sampling duplicates random samples from the minority group [27].…”

Section: Data-level Methodsmentioning

confidence: 99%

“…Instead, the learning or decision-making process is adjusted in a way that increases the importance of the positive class. Most commonly, algorithms are modified to take a class penalty or weight into consideration, or the decision threshold is shifted in a way that reduces bias towards the negative class [27].…”

Section: Algorithm-level Methodsmentioning

confidence: 99%

“…As a result, instances belonging to the minority group are misclassified more often than those belonging to the majority group. Current solutions for class imbalance can be classified into three categories: data-level methods, algorithm-level methods, and hybrid approaches [27].…”

Section: Class Imbalancementioning

confidence: 99%

“…Data-level and algorithm-level methods have been combined and applied to class imbalance problems. One strategy includes performing data sampling to reduce class noise and imbalance and then applying cost-sensitive learning or thresholding to further reduce the bias towards the majority group [27].…”

Section: Hybrid Approachesmentioning

confidence: 99%

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Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review

Kiani,

Sheng

2024

Electronics

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In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively.

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Section: Data-level Methodsmentioning

confidence: 99%

Section: Algorithm-level Methodsmentioning

confidence: 99%

Section: Class Imbalancementioning

confidence: 99%

Section: Hybrid Approachesmentioning

confidence: 99%

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