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Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder

BRIEFINGS IN BIOINFORMATICS 2022年第3期23卷 bbac104-bbac104页

作者： Liu, Wei Lin, Hui Huang, Li Peng, Li Tang, Ting Zhao, Qi Yang, Li Xiangtan Univ Sch Comp Sci Sch Cyberspace Sci Xiangtan Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Peoples R China Tsinghua Univ Acad Arts & Design Beijing Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China Xiangtan Univ Sch Publ Adm Xiangtan Peoples R China

Increasing evidences show that the occurrence of human complex diseases is closely related to microRNA (miRNA) variation and imbalance. For this reason, predicting disease-related miRNAs is essential for the diagnosis and treatment of complex human diseases. Although some current computational methods can effectively predict potential disease-related miRNAs, the accuracy of prediction should be further improved. In our study, a new computational method via deep forest ensemble learning based on autoencoder (DFELMDA) is proposed to predict miRNA-disease associations. Specifically, a new feature representation strategy is proposed to obtain different types of feature representations (from miRNA and disease) for each miRNA-disease association. Then, two types of low-dimensional feature representations are extracted by two deep autoencoders for predicting miRNA-disease associations. Finally, two prediction scores of the miRNA-disease associations are obtained by the deep random forest and combined to determine the final results. DFELMDA is compared with several classical methods on the The Human microRNA Disease Database (HMDD) dataset. Results reveal that the performance of this method is superior. The area under receiver operating characteristic curve (AUC) values obtained by DFELMDA through 5-fold and 10-fold cross-validation are 0.9552 and 0.9560, respectively. In addition, case studies on colon, breast and lung tumors of different disease types further demonstrate the excellent ability of DFELMDA to predict disease-associated miRNA-disease. Performance analysis shows that DFELMDA can be used as an effective computational tool for predicting miRNA-disease associations.

关键词： miRNA-disease association prediction deep forest ensemble learning deep autoencoder feature representation

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A deep Nonnegative Matrix Factorization Approach via autoencoder for Nonlinear Fault Detection

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第8期16卷 5042-5052页

作者： Ren, Zelin Zhang, Wensheng Zhang, Zhizhong Chinese Acad Sci Inst Automat Res Ctr Precis Sensing & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China

In the era of big data, data-driven fault detection is vital for modern industrial systems. This article considers the potential complexity of fault detection and proposes a novel nonlinear method based on nonnegative matrix factorization (NMF). Motivated by an autoencoder, in this article we first utilize the input data to learn an appropriate nonlinear mapping function, which transforms the original space into a high-dimensional feature space. Then, according to the decomposition rule of NMF, we divide the learned feature space into two subspaces, and two statistics in these subspaces are designed appropriately for nonlinear fault detection. The established method, i.e., deep nonnegative matrix factorization (DNMF), is implemented by three parts: an encoder module, an NMF module, and a decoder module. Unlike conventional NMF-based nonlinear methods using implicit and predetermined kernels, DNMF provides a new nonlinear scheme applied to NMF via a deep autoencoder framework and realizes nonlinear mapping for input data automatically. Our proposed nonlinear framework can be further generalized to other linear methods. Besides, DNMF greatly expands the NMF application scope by breaking through the limitation of nonnegative input. The Tennessee Eastman process as an industrial benchmark is employed to verify the effectiveness of the proposed method.

关键词： Kernel Fault detection Feature extraction Matrix decomposition Neural networks Informatics Data-driven fault detection deep autoencoder nonlinear industrial process nonnegative matrix factorization (NMF)

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Blade Rub-Impact Fault Identification Using autoencoder-Based Nonlinear Function Approximation and a deep Neural Network

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SENSORS 2020年第21期20卷 6265.-6265.页

作者： Prosvirin, Alexander E. Piltan, Farzin Kim, Jong-Myon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea

A blade rub-impact fault is one of the complex and frequently appearing faults in turbines. Due to their nonlinear and nonstationary nature, complex signal analysis techniques, which are expensive in terms of computation time, are required to extract valuable fault information from the vibration signals collected from rotor systems. In this work, a novel method for diagnosing the blade rub-impact faults of different severity levels is proposed. Specifically, the deep undercomplete denoising autoencoder is first used for estimating the nonlinear function of the system under normal operating conditions. Next, the residual signals obtained as the difference between the original signals and their estimates by the autoencoder are computed. Finally, these residual signals are used as inputs to a deep neural network to determine the current state of the rotor system. The experimental results demonstrate that the amplitudes of the residual signals reflect the changes in states of the rotor system and the fault severity levels. Furthermore, these residual signals in combination with the deep neural network demonstrated promising fault identification results when applied to a complex nonlinear fault, such as a blade-rubbing fault. To test the effectiveness of the proposed nonlinear-based fault diagnosis algorithm, this technique is compared with the autoregressive with external input Laguerre proportional-integral observer that is a linear-based fault diagnosis observation technique.

关键词： data-driven fault diagnosis deep autoencoder deep neural network deep learning-based function approximation fault diagnosis rotating machinery rub-impact fault nonlinear-based fault diagnosis

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DISPARITY ANALYSIS BETWEEN THE ASSEMBLY AND BYTE MALWARE SAMPLES WITH deep autoencoderS 19

DISPARITY ANALYSIS BETWEEN THE ASSEMBLY AND BYTE MALWARE SAM...

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19th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Abdullah, Muhammed Amin Yu, Yongbin Cai, Jingye Imrana, Yakubu Tettey, Nartey Obed Addo, Daniel Sarpong, Kwabena Agbley, Bless Lord Y. Appiah, Benjamin Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Ho Tech Univ Dept Comp Sci Ho Ghana

ISBN: (纸本)9781665493895

Malware attacks in the cyber world continue to increase despite the efforts of Malware analysts to combat this problem. Recently, Malware samples have been presented as binary sequences and assembly codes. However, most researchers focus only on the raw Malware sequence in their proposed solutions, ignoring that the assembly codes may contain important details that enable rapid Malware detection. In this work, we leveraged the capabilities of deep autoencoders to investigate the presence of feature disparities in the assembly and raw binary Malware samples. First, we treated the task as outliers to investigate whether the autoencoder would identify and justify features as samples from the same family. Second, we added noise to all samples and used deep autoencoder to reconstruct the original samples by denoising. Experiments with the Microsoft Malware dataset showed that the byte samples' features differed from the assembly code samples.

关键词： Malware deep autoencoder Assembly file Raw binary files

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deep autoencoders as anomaly detectors: Method and case study in a distributed water treatment plant

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COMPUTERS & SECURITY 2020年 99卷 102055-102055页

作者： Raman, Mr Gauthama Dong, Wenjie Mathur, Aditya Singapore Univ Technol & Design Ctr Cyber Secur Res iTrust Singapore Singapore

Industrial Control Systems (ICS) are found in critical infrastructure, such as, water treatment plants and oil refineries. ICS are often the target of cyber-attacks leading to undesirable consequences. It is essential to detect process anomalies resulting from such attacks before appropriate defensive actions are considered. In this work, a deep autoencoder-based anomaly detector (DAE) is proposed. DAE is trained using data collected during normal operation of a plant. The detection effectiveness of three variants of DAE was experimentally evaluated on an operational Secure Water Treatment (SWaT) plant. Further, the amount of plant design knowledge needed to design DAE was compared with that needed to create design-centric approaches for anomaly detection. Experimental results indicate that the proposed DAE, constructed with minimal design knowledge, is effective in detecting process anomalies re sulting due to single and multi-point coordinated attacks with high detection rate and few false alarms. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Anomaly Detection deep autoencoder cyber-attacks Cyber-Physical Systems Water Treatment Plant

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Improved convolution neural network integrating attention based deep sparse auto encoder for network intrusion detection

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APPLIED INTELLIGENCE 2025年第2期55卷 1-17页

作者： Geng, Zhiqiang Li, Xueming Ma, Bo Han, Yongming Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing 100029 Peoples R China

Network intrusion detection (NID) is seen as a pivotal technology in the network security which can detect malicious threats occurring in the network and lend stabilized services for expanding the network environments. However, Network-based Intrusion Detection Systems (NIDSs) are not sensitive to infrequent intrusions features and tend to have high misjudgment rate on imbalanced datasets, which lead to obvious defects for detecting minority classes of intrusion. Therefore, a novel neoteric NID methodology predicated upon an optimized convolutional neural network (CNN) integrating Attention based deep Sparse Auto Encoder (ADSAE) (ADSAE-CNN) is put forward. The data expanding method based on the ADSAE model integrates attention mechanisms with deep stacked autoencoders to expand intrusion records of minority classes in data preprocessing, so as to balance the data distribution of intrusion detection datasets, improve the sensitivity of the detection model to the intrusion of few categories, and enhance the monitoring of the intrusion by the system. Meanwhile, the ADSAE can encode and transform the intrusion data to ameliorate the feature extraction capability of convolutional layers of the proposed ADSAE-CNN for detecting and classifying different intrusions classes. Finally, the ADSAE-CNN methodology is devoted to the network intrusion detection of two experiments on UNSW-NB15 and CSE-CIC-IDS2018 datasets and achieves the total precision of detection 89.1% on UNSW-NB15 and 94.20% on CSE-CIC-IDS2018, which can lead to considerably elevate the detection rate of minority intrusions and means significant effectiveness on multi-class network intrusion detections.

关键词： deep autoencoder Convolution neural network Network intrusion detection Imbalance dataset Data expanding

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Attribute graph anomaly detection utilizing memory networks enhanced by multi-embedding comparison

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NEUROCOMPUTING 2025年 633卷

作者： Zhang, Lianming Wu, Baolin Dong, Pingping Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Peoples R China

In complex attribute networks, accurately pinpointing anomalous nodes is vital for grasping network behavior and safeguarding network security. Traditional anomaly detection methods often struggle to fully harness the intricate relationships that underpin attributes and structures, thus curbing their practical effectiveness. To transcend this limitation, we introduce a novel graph anomaly detection model that harmoniously integrates node attributes and structural information. Our model employs multi-embedding contrast modules, coupled with memory network enhancements, to pinpoint anomalous nodes. Precisely, we crafted a multi-embedding contrast module to encode the attributes and structures inherent within nodes, generating a multitude of embedding representations. By scrutinizing the discrepancies between these representations, our model adeptly identifies nodes that deviate from attribute and structural consistency, indicating anomalies. Furthermore, we incorporate a memory network to reconstruct node attributes, thereby enhancing the attribute decoding process while preserving the straightforwardness of structural decoding. To validate our method, we conducted extensive experiments on five authoritative public graph datasets, comparing various graph anomaly detection methods using rigorous metrics such as AUC, precision, and recall. The experimental results unequivocally demonstrate that our proposed method surpasses current state-of-the-art techniques in detecting anomalous nodes within graphs, solidly validating its efficacy.

关键词： Anomaly detection Attributed networks deep autoencoder Social networks

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Topic Diffusion Discovery based on deep Non-negative autoencoder 21

Topic Diffusion Discovery based on Deep Non-negative Autoenc...

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21st IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI)

作者： Huang, Sheng-Tai Kang, Yihuang Hung, Shao-Min Kuo, Bowen Cheng, I-Ling Natl Sun Yat Sen Univ Dept Informat Management Kaohsiung Taiwan Natl Chung Hsing Univ Grad Inst Lib & Informat Sci Taichung Taiwan

ISBN: (纸本)9781728110547

Researchers have been overwhelmed by the explosion of research articles published by various research communities. Many research scholarly websites, search engines, and digital libraries have been created to help researchers identify potential research topics and keep up with recent progress on research of interests. However, it is still difficult for researchers to keep track of the research topic diffusion and evolution without spending a large amount of time reviewing numerous relevant and irrelevant articles. In this paper, we consider a novel topic diffusion discovery technique. Specifically, we propose using a deep Non-negative autoencoder with information divergence measurement that monitors evolutionary distance of the topic diffusion to understand how research topics change with time. The experimental results show that the proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.

关键词： deep autoencoder Online Machine Learning Topic Modeling Topic Diffusion Topic Detection and Tracking

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Unsupervised environmental operating condition compensation strategies in a guided ultrasonic wave monitoring system: evaluation and comparison

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2024年第4期14卷 861-884页

作者： Yon, Kong Chen Bakhary, Norhisham Padil, Khairul Hazman Shamsudin, Mohd Fairuz Univ Teknol Malaysia Fac Civil Engn Utm Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Mech Engn Utm Skudai 81310 Johor Malaysia Univ Teknol Malaysia Inst Noise & Vibrat City CampusJalan Semarak Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Inst Smart Infrastruct & Innovat Construct Forens Engn Ctr Johor Baharu 81310 Johor Malaysia VRG Dinamik SDN BHD Shah Alam 40170 Selangor Malaysia

Guided ultrasonic wave (GUW) monitoring systems are gaining much attention in pipeline condition monitoring. However, the effects of environmental and operational conditions (EOCs), especially temperature and random noise, degrade damage detection performance. When EOC effects produce greater amplitudes than the reflected waves from small damage cases, the reflected waves remain unidentified. This paper proposes an unsupervised learning-based denoising autoencoder (DAE) to reduce the effect of EOCs in GUW monitoring systems. A DAE decodes high-dimensional data into low-dimensional features and reconstructs the original data from these low-dimensional features. By providing GUW signals at a reference temperature, this structure forces the DAE to learn the essential features hidden within complex data. The proposed DAE undergoes comparative analysis with other popular unsupervised learning algorithms used for EOC compensation in GUW monitoring systems, such as principal component analysis, independent component analysis and deep autoencoder algorithms. EOC compensation performance is evaluated through receiver operating characteristics (ROC). From the numerical model and an experimental model, the GUW database is obtained. All four algorithms showed good damage detection performance using a numerical model;however, in the experimental model, the proposed DAE showed superiority among other methods.

关键词： Guided ultrasonic wave Unsupervised learning Environmental and operational conditions Independent component analysis Principal component analysis deep autoencoder Denoising autoencoder

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Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2024年第4期62卷 925-949页

作者： Cassavia, Nunziato Caviglione, Luca Guarascio, Massimo Liguori, Angelica Zuppelli, Marco Inst High Performance Comp & Networking Via Pietro Bucci 8-9-C I-87036 Arcavacata Di Rende Italy Inst Appl Math & Informat Technol Via Marini 6 I-16149 Genoa Italy Univ Calabria Via Pietro Bucci I-87036 Arcavacata Di Rende Italy

Modern IoT ecosystems are the preferred target of threat actors wanting to incorporate resource-constrained devices within a botnet or leak sensitive information. A major research effort is then devoted to create countermeasures for mitigating attacks, for instance, hardware-level verification mechanisms or effective network intrusion detection frameworks. Unfortunately, advanced malware is often endowed with the ability of cloaking communications within network traffic, e.g., to orchestrate compromised IoT nodes or exfiltrate data without being noticed. Therefore, this paper showcases how different autoencoder-based architectures can spot the presence of malicious communications hidden in conversations, especially in the TTL of IPv4 traffic. To conduct tests, this work considers IoT traffic traces gathered in a real setting and the presence of an attacker deploying two hiding schemes (i.e., naive and "elusive" approaches). Collected results showcase the effectiveness of our method as well as the feasibility of deploying autoencoders in production-quality IoT settings.

关键词： deep autoencoder Ensemble method Covert channel Intelligent cyber attack detection system

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