Publications by authors named "ShengZheng Liu"

Article Synopsis
  • The text discusses the importance of anomaly detection in the Industrial Internet of Things (IIoT) due to increasing data and security challenges.
  • It introduces a new deep learning model called MIX_LSTM designed to improve anomaly detection by optimizing feature selection and loss functions that address data imbalances.
  • The model was tested on two datasets (UNSW-NB15 and NSL-KDD), achieving impressive metrics like low False Alarm Rates and high AUC-ROC and AUC-PR values, demonstrating its effectiveness in detecting anomalies.
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The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions.

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The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues.

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MicroRNAs (miRNAs) are small and non-coding RNA molecules which have multiple important regulatory roles within cells. With the deepening research on miRNAs, more and more researches show that the abnormal expression of miRNAs is closely related to various diseases. The relationship between miRNAs and diseases is crucial for discovering the pathogenesis of diseases and exploring new treatment methods.

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