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A Novel Network Intrusion Detection System Based on Semi-Supervised Approach for IoT

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第4期14卷 207-216页

作者： Bhavani, A. Durga Mangla, Neha BMS Inst Technol & Management Dept Comp Sci & Engn Bangalore India Atria Inst Technol Dept Informat Sci & Engn Bangalore India

An intrusion detection system (IDS) is one of the most effective ways to secure a network and prevent unauthorized access and security attacks. But due to the lack of adequately labeled network traffic data, researchers have proposed several feature representations models over the past three years. However, these models do not account for feature generalization errors when learning semantic similarity from the data distribution and may degrade the performance of the predictive IDS model. In order to improve the capabilities of IDS in the era of Big Data, there is a constant need to extract the most important features from large-scale and balanced network traffic data. This paper proposes a semi-supervised IDS model that leverages the power of untrained autoencoders to learn latent feature representations from a distribution of input data samples. Further, distance function-based clustering is used to find more compact code vectors to capture the semantic similarity between learned feature sets to minimize reconstruction loss. The proposed scheme provides an optimal feature vector and reduces the dimensionality of features, reducing memory requirements significantly. Multiple test cases on the IoT dataset MQTTIOT2020 are conducted to demonstrate the potential of the proposed model. Supervised machine learning classifiers are implemented using a proposed feature representation mechanism and are compared with shallow classifiers. Finally, the comparative evaluation confirms the efficacy of the proposed model with low false positive rates, indicating that the proposed feature representation scheme positively impacts IDS performance.

关键词： IoT security intrusion detection system semi-supervised autoencoder clustering machine learning

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Deep learning-based extended field of view computed tomography image reconstruction: influence of network design on image estimation outside the scan field of view

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BIOMEDICAL PHYSICS & ENGINEERING EXPRESS 2022年第2期8卷

作者： Khural, Bhupinder Singh Baer-Beck, Matthias Fournie, Eric Stierstorfer, Karl Huang, Yixing Maier, Andreas FAU Erlangen Nurnberg Pattern Recognit Lab Martensstr 3 D-91058 Erlangen Germany Siemens Healthcare GmbH Computed Tomog Siemensstr 3 D-91301 Forchheim Germany

The problem of data truncation in Computed Tomography (CT) is caused by the missing data when the patient exceeds the Scan Field of View (SFOV) of a CT scanner. The reconstruction of a truncated scan produces severe truncation artifacts both inside and outside the SFOV. We have employed a deep learning-based approach to extend the field of view and suppress truncation artifacts. Thereby, our aim is to generate a good estimate of the real patient data and not to provide a perfect and diagnostic image even in regions beyond the SFOV of the CT scanner. This estimate could then be used as an input to higher order reconstruction algorithms [1]. To evaluate the influence of the network structure and layout on the results, three convolutional neural networks (CNNs), in particular a general CNN called ConvNet, an autoencoder, and the U-Net architecture have been investigated in this paper. Additionally, the impact of L1, L2, structural dissimilarity and perceptual loss functions on the neural network's learning have been assessed and evaluated. The evaluation of data set comprising 12 truncated test patients demonstrated that the U-Net in combination with the structural dissimilarity loss showed the best performance in terms of image restoration in regions beyond the SFOV of the CT scanner. Moreover, this network produced the best mean absolute error, L1, L2, and structural dissimilarity evaluation measures on the test set compared to other applied networks. Therefore, it is possible to achieve truncation artifact removal using deep learning techniques.

关键词： computed tomography convolutional neural network U-Net autoencoder extended field of view truncation artifacts

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Data compression methods based on Neural Networks 21

Data compression methods based on Neural Networks

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Proceedings of the 5th International Conference on Future Networks and Distributed Systems

作者： Aleksandr Berezkin Alexey Slepnev Ruslan Kirichek Dmitry Kukunin Daniil Matveev Communication Networks and Data Transmission Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia Software Engineering and Computer Science Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia

ISBN: (纸本)9781450387347

The modern stage of the development of telecommunication systems is characterized by the widespread development of wired and wireless data transmission networks. The growth in the number of users of such networks, the emergence of new multimedia services impose high demands on speed, reliability, and delay time in information processing. One of the factors in increasing the data transfer rate is the on-the-fly compression of information at the link level. The paper describes a method for compressing data in a communication channel using error-correcting BCH codes and a feed-forward neural network autoencoder. This method converts a binary vector of user data into BCH codewords, which are used to train the autoencoder. This ultimately makes it possible to reduce the number of transmitted bits in the communication channel.

关键词： autoencoder

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A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules

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TECHNOLOGY IN CANCER RESEARCH & TREATMENT 2018年第17期17卷

作者： Shaffie, Ahmed Soliman, Ahmed Fraiwan, Luay Ghazal, Mohammed Taher, Fatma Dunlap, Neal Wang, Brian van Berke, Victor Keynton, Robert Elmaghraby, Add El-Baz, Ayman Univ Louisville Bioengn Dept 200 E Shipp Ave Louisville KY 40208 USA Univ Louisville Comp Engn & Comp Sci Dept Louisville KY 40208 USA Abu Dhabi Univ Dept Elect & Comp Engn Abu Dhabi U Arab Emirates Zayed Univ Coll Technol Innovat Dubai U Arab Emirates Univ Louisville Dept Radiat Oncol Louisville KY 40208 USA Univ Louisville Dept Cardiovasc & Thorac Surg Louisville KY 40208 USA

A novel framework for the classification of lung nodules using computed tomography scans is proposed in this article. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following 2 groups of features: ( I ) appearance features modeled using the higher order Markov Gibbs random field model that has the ability to describe the spatial inhomogeneities inside the lung nodule and (2) geometric features that describe the shape geometry of the lung nodules. The novelty of this article is to accurately model the appearance of the detected lung nodules using a new developed seventh-order Markov Gibbs random field model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted geometric features. Finally, a deep autoencoder classifier is fed by the above 2 feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium. We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 91.20%.

关键词： computer-aided diagnosis higher order MGRF computed tomography autoencoder pulmonary nodule lung cancer

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Robust estimation of hydrogeological parameters from wireline logs usingsemi-supervised deep neural networks assisted with global optimization-based regression methods

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GROUNDWATER FOR SUSTAINABLE DEVELOPMENT 2024年 27卷

作者： Mohammed, Musaab. A. A. Szabo, Norbert P. Szucs, Peter Univ Miskolc Fac Earth & Environm Sci & Engn H-3515 Miskolc Egyetemvaros Hungary Univ Miskolc Inst Water Resources & Environm Management Natl Lab Water Sci & Water Secur Miskolc Hungary Univ Bahri Coll Petr Geol & Minerals Khartoum Sudan

Understanding the distribution of hydrogeological properties of the aquifers is crucial for sustainable groundwater resource development. This research explores the application of deep autoencoder neural networks (AENN), assisted with global optimization methods for estimating hydrogeological parameters in the Quaternary aquifer system in the Debrecen area, Hungary. Traditional methods for estimating aquifer parameters typically depend on field experiments and laboratory analyses, which are both costly and time-consuming, and often fail to account for the heterogeneity of groundwater formations. In this study, deep AE-NN models are trained to extract latent space (LS) representations that capture key features from the available well logs, including spontaneous potential (SP), natural gamma ray (NGR), shallow resistivity (RS), and deep resistivity (RD). The LS log is then correlated with shale volume and hydraulic conductivity, as determined by the Larionov and Csokas methods, respectively. Regression analysis revealed a Gaussian relationship between the LS log and shale volume and a negative nonlinear relationship with hydraulic conductivity. Global optimization methods, including simulated annealing (SA) and particle swarm optimization (PSO), were used to refine the regression parameters, enhancing the predictive capabilities of the models. The results demonstrated that AE-NN assisted with global optimization methods can be effectively used to estimate shale volume and hydraulic conductivity, proposing a novel and independent approach for estimating hydrogeological parameters critical to groundwater flow and contaminant transport modeling.

关键词： Pannonian Basin Shale volume Hydraulic conductivity autoencoder Simulated annealing Particle swarm optimization

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A deep learning framework for target localization in error-prone environment

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INTERNET OF THINGS 2023年 22卷

作者： Mohammed, Shahmir Khan Singh, Shakti Mizouni, Rabeb Otrok, Hadi Khalifa Univ Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates

The use of Internet of Things (IoT) in environment monitoring has led to the development of Smart Environmental Monitoring (SEM) paradigm. Target or source localization, that determines the underlying cause of environmental occurrences, is an important aspect of SEM. To prevent an environmental event in becoming a potential disaster, swift and early localization of its source is crucial. Target localization is performed with the help of IoT sensors, which are typically deployed in hazardous environments and are not easily serviceable, making energy efficiency a requirement for SEM systems. Furthermore, IoT sensors may provide anomalous readings due to low battery, low sensor sensitivity, faults, or malicious attacks. This alters the decision-making process and leads to inaccurate localization of the source. Hence, detecting and dealing with anomalies, while utilizing a limited number of sensor nodes, are key factors in addressing reliability and energy efficiency issues. The current works are iterative in nature, employs redundant nodes, do not efficiently address anomalous readings, which leads to slow and imprecise localization, while increasing energy consumption. Moreover, they are application specific, making them less adaptable to other sensing tasks. Therefore, this work proposes a comprehensive and novel deep learning-based system that - (a) repopulates missing data from inactive sensors, (b) detects and rectifies anomalous data, and (c) instantly localizes a target. The efficacy of the proposed approach is validated in a radioactive environment and compared to existing benchmark. The results show that the proposed approach achieves precise, rapid, and efficient localization by achieving 5 times better accuracy, 50 times faster speeds, and 3 times lesser energy consumption.

关键词： Localization Machine learning Deep learning CNN autoencoder Anomalies IoT

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AI-driven modelling approaches for predicting oxygen levels in aquatic environments

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JOURNAL OF WATER PROCESS ENGINEERING 2024年 66卷

作者： Singh, Rosysmita Bikram Olbert, Agnieszka I. Samantra, Avinash Uddin, Md Galal Natl Inst Technol Dept Civil Engn Rourkela 769008 Odisha India Univ Galway Coll Sci & Engn Nat Sci Galway Ireland Univ Galway Sch Engn Galway Ireland Univ Galway Ryan Inst Galway Ireland Univ Galway MaREI Res Ctr Galway Ireland Univ Galway Coll Sci & Engn Sch Engn Eco HydroInformat Res Grp EHIRG Galway Ireland Natl Inst Technol Dept Comp Sci & Engn Rourkela 769008 Odisha India

Reliable water quality models are crucial for better water management and pollution control. Biochemical oxygen demand (BOD) and dissolved oxygen (DO) are the widely recognized indicators used to examine the quality of water. Stakeholders often face challenges when it comes to monitoring water quality indicators daily. The conventional approach for testing is laborious and costly. Therefore, there is a need to incorporate alternate concepts for predicting water quality parameters. This article proposes a novel approach "autoencoder-DeepAutoregressive (AE-DeepAR)" to predict the concentration of BOD and DO using in-situ measurable water quality parameters as input. The autoencoder is employed to utilize a latent space that accurately captures the fundamental characteristics of the nonlinear data. On the other hand, the DeepAR model is utilized to produce point predictions. This approach is being implemented in the Mahanadi River system for the first time. It is found that conductivity, total suspended solids (TSS), turbidity, and ammoniacal nitrogen (NH3-N) are correlated with BOD. Likewise, turbidity, temperature, and total dissolved solids (TDS) correlate significantly with DO. The performance of the AE-DeepAR model in predicting BOD surpasses that of other models at all stations. The R2 range from 0.90 to 0.93, mean absolute error (MAE) varies from 0.061 to 0.147, mean squared error (MSE) lies between 0.005 to 0.028, and mean absolute percentage error (MAPE) differs from 9.20% to 19.95%. All stations show a greater degree of sensitivity. The uncertainty observed is less in most circumstances, but the unpredictability of the values caused by the occurrence of outliers in severe events may influence the outcome. The PICP value varies between 87 % to 95 % in BOD and 89 % to 95 % in DO. The results reveal that AE-DeepAR can be used as an alternative approach to predict BOD and DO with a high degree of accuracy.

关键词： AE-DeepAR Oxygen level Water management Prediction autoencoder

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DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data

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GIGASCIENCE 2020年第12期9卷 giaa122页

作者： Simon, Lukas M. Yan, Fangfang Zhao, Zhongming Univ Texas Hlth Sci Ctr Houston Ctr Precis Hlth Sch Biomed Informat 7000 Fannin St Houston TX 77030 USA Univ Texas Hlth Sci Ctr Houston Human Genet Ctr Sch Publ Hlth 7000 Fannin St Houston TX 77030 USA MD Anderson Canc Ctr UTHlth Grad Sch Biomed Sci 6767 Bertner Ave Houston TX 77030 USA Vanderbilt Univ Dept Biomed Informat Med Ctr 2525 West End Nashville TN 37203 USA

Background: Single-cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic datasets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps. Findings: Here, we present DrivAER, a machine learning approach for the identification of driving transcriptional programs using autoencoder-based relevance scores. DrivAER scores annotated gene sets on the basis of their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. DrivAER iteratively evaluates the information content of each gene set with respect to the outcome variable using autoencoders. We benchmark our method using extensive simulation analysis as well as comparison to existing methods for functional interpretation of scRNA-seq data. Furthermore, we demonstrate that DrivAER extracts key pathways and transcription factors that regulate complex biological processes from scRNA-seq data. Conclusions: By quantifying the relevance of annotated gene sets with respect to specified outcome variables, DrivAER greatly enhances our ability to understand the underlying molecular mechanisms.

关键词： autoencoder machine learning manifold interpretation single-cell RNA sequencing transcription factor

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Self-correction method for sensor faulty heat pump system based on machine learning

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RESULTS IN ENGINEERING 2024年 22卷

作者： Sun, Zhe Yao, Qiwei Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China Zhejiang Univ Technol Taizhou Inst Taizhou Key Lab Adv Mfg Technol Taizhou 318014 Peoples R China

Sensor faults are a common type of failure in heat pump systems, which can seriously affect the normal operation of systems. Self-correction of the sensor fault in the system is crucial. State-of-the-art sensor fault correction methods based on data-driven and physical models face challenges, such as the need for co-located sensors, accurate physical models, and a large amount of labeled data, greatly limiting their applicability. This paper proposes using machine learning methods for fault self-correction. Firstly, a data self-correction strategy based on the convolutional autoencoder is introduced. Furthermore, an artificial sample generation strategy is proposed to address the scarcity of sensor fault data for data-driven training of the self-correction model. The results demonstrate that the proposed method effectively self-corrects both single and multiple faults. Simultaneously, thermal fault diagnosis evaluations reveal over 90 % accuracy in corrected data, with a maximum diagnostic improvement of 53.5 %. Furthermore, the study shows that the number of parameters is crucial for effective correction, underscoring that over-constraint is essential for successful self-correction.

关键词： Sensor fault Data self -correction Machine learning autoencoder Heat pump

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Study for Aortic Atherosclerotic Plaque Classification Using Deep Learning

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TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS A 2022年第2期46卷 187-193页

作者： Cho, You Hee Bang, Hye Jin Park, Jong Won Cho, Jung Sun Korea Inst Machinery & Mat Dept Reliabil Assessment Daejeon South Korea Catholic Univ Korea Daejeon St Marys Hosp Daejeon South Korea

Stroke and heart disease, which account for a high percentage of the causes of death amongst the elderly population, can occur suddenly leading to death. Hence, early diagnoses and continuous management are required. High-risk diseases should be diagnosed through medical personnel using established medical techniques. However, it is time consuming to decide on a diagnosis or the opinion may differ depending on the medical professional. This study aims to shorten the diagnosis period and provide high accuracy diagnoses by establishing the semi-supervised convolution autoencoder and the U-Net models that can classify aortic atherosclerotic plaque conditions and predict the primary locations for stroke occurrence.

关键词： Aorta Plaque Classification Plaque Area Prediction autoencoder U-Net

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