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Zero-day Ransomware Attack Detection using Deep Contractive autoencoder and Voting based Ensemble Classifier

APPLIED INTELLIGENCE 2022年第12期52卷 13941-13960页

作者： Zahoora, Umme Rajarajan, Muttukrishnan Pan, Zahoqing Khan, Asifullah Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad 45650 Pakistan City Univ London Sch Math Comp Sci & Engn London EC1V 0HB England Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Pakistan Inst Engn & Appl Sci Ctr Math Sci Islamabad 45650 Pakistan Pakistan Inst Engn & Appl Sci PIEAS Artificial Intelligence Ctr PAIC Islamabad 45650 Pakistan

Ransomware attacks are hazardous cyber-attacks that use cryptographic methods to hold victims' data until the ransom is paid. Zero-day ransomware attacks try to exploit new vulnerabilities and are considered a severe threat to existing security solutions and internet resources. In the case of zero-day attacks, training data is not available before the attack takes place. Therefore, we exploit Zero-shot Learning (ZSL) capabilities that can effectively deal with unseen classes compared to the traditional machine learning techniques. ZSL is a two-stage process comprising of: Attribute Learning (AL) and Inference Stage (IS). In this regard, this work presents a new Deep Contractive autoencoder based Attribute Learning (DCAE-ZSL) technique as well as an IS method based on Heterogeneous Voting Ensemble (DCAE-ZSL-HVE). In the proposed DCAE-ZSL approach, Contractive autoencoder (CAE) is employed to extract core features of known and unknown ransomware. The regularization term of CAE helps in penalizing the classifier's sensitivity against the small dissimilarities in the latent space. On the other hand, in case of the IS, four combination rules Global Majority (GM), Local Majority (LM), Cumulative Vote-against based Global Majority (CVAGM), Cumulative Vote-for based Global Majority (CVFGM) are utilized to find the final prediction. It is empirically shown that in comparison to conventional machine learning techniques, models trained on contractive embedding show reasonable performance against zero-day attacks. Furthermore, it is shown that the exploitation of these core features through the proposed voting based ensemble (DCAE-ZSL-HVE) has demonstrated significant improvement in detecting zero-day attacks (recall = 0.95) and reducing False Negative (FN = 6).

关键词： Zero-shot Learning Zero-day Attack Ransomware Deep Learning autoencoder Ensemble Classification

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Quadratic autoencoder (Q-AE) for Low-Dose CT Denoising

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第6期39卷 2035-2050页

作者： Fan, Fenglei Shan, Hongming Kalra, Mannudeep K. Singh, Ramandeep Qian, Guhan Getzin, Matthew Teng, Yueyang Hahn, Juergen Wang, Ge Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA Massachusetts Gen Hosp Dept Radiol Boston MA 02114 USA Northeastern Univ Sino Dutch Biomed & Informat Engn Sch Shenyang 110169 Peoples R China

Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic operation on input data, thereby enhancing the capability of an individual neuron. Along this direction, we are motivated to evaluate the power of quadratic neurons in popular network architectures, simulating human-like learning in the form of "quadratic-neuron-based deep learning". Our prior theoretical studies have shown important merits of quadratic neurons and networks in representation, efficiency, and interpretability. In this paper, we use quadratic neurons to construct an encoder-decoder structure, referred as the quadratic autoencoder, and apply it to low-dose CT denoising. The experimental results on the Mayo low-dose CT dataset demonstrate the utility and robustness of quadratic autoencoder in terms of image denoising and model efficiency. To our best knowledge, this is the first time that the deep learning approach is implemented with a new type of neurons and demonstrates a significant potential in the medical imaging field.

关键词： Deep learning quadratic neurons autoencoder low-dose CT

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Particle Swarm Optimization for Efficiently Evolving Deep Convolutional Neural Networks Using an autoencoder-Based Encoding Strategy

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第5期28卷 1190-1204页

作者： Yuan, Gonglin Wang, Bin Xue, Bing Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

Deep convolutional neural networks (DCNNs) have achieved surpassing success in the field of computer vision, and a number of elaborately designed networks refresh the performance records in benchmark datasets. Recently, evolutionary neural architecture search (ENAS) has become an emerging area, which could employ an evolutionary computation (EC) technique to automatically construct promising network architectures without human intervention. However, existing algorithms still have limitations: most standard EC approaches cannot process flexible-length architecture representations directly, and most fitness evaluation processes suffer from the exhibitive computational cost and the unreliable prediction results. To overcome these shortcomings, we propose an efficient particle swarm optimization (PSO)-based neural architecture search algorithm to search for appropriate dense blocks on image classification tasks, named EAEPSO. EAEPSO addresses the first limitation by designing an autoencoder to encode variable-length network representations as fixed-length latent vectors, which converts the original search space to a latent space that can facilitate the downstream search. Besides, an efficient and effective hierarchical fitness evaluation method is designed to guide the search process to address the second limitation. The experimental results show that EAEPSO is a very competitive ENAS algorithm that achieves an error rate of 2.74% on CIFAR-10 and 16.17% on CIFAR-100, and reduces the computational cost from hundreds or thousands of GPU-days to only 2.2 and 4 GPU-days, respectively. Further analyses investigate the reduced training data's effect and confirm the effectiveness of both the proposed autoencoder and the proposed hierarchical fitness evaluation method.

关键词： Computer architecture Feature extraction Task analysis Computational efficiency Classification algorithms Encoding Training autoencoder evolutionary neural architecture search (ENAS) image classification particle swarm optimization (PSO)

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A Novel OFDM autoencoder Featuring CNN-Based Channel Estimation for Internet of Vessels

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IEEE INTERNET OF THINGS JOURNAL 2020年第8期7卷 7601-7611页

作者： Lin, Bin Wang, Xudong Yuan, Weihao Wu, Nan Dalian Maritime Univ Dept Informat Sci & Technol Dalian 116026 Peoples R China

This article proposes a novel orthogonal frequency-division multiplexing (OFDM) autoencoder featuring convolutional neural networks (CNNs)-based channel estimation for marine communications with complex and fast-changing environments. We demonstrate that the proposed OFDM autoencoder system can be generalized to work under various channel environments, different throughputs, while outperforming the traditional OFDM counterparts, especially when working at high throughputs. In addition, since OFDM systems require accurate channel estimations to function properly, this treatise also proposes a new channel estimation algorithm for OFDM systems that combine the power of deep learning (DL) with the philosophy of super-resolution reconstruction, which uses dense convolutional neural networks (Dense-Nets) to reconstruct low-resolution pilot information images into high-resolution full-channel impulse responses (CIRs). The Dense-Net structure has the characteristics of dense connections and feature multiplexing. The simulation results show that under slow fading, the proposed channel estimator (CE) can estimate the CIRs perfectly. Under fast fading, the proposed CE outperforms the existing learning-based algorithms with fewer neural network parameters. Therefore, the proposed novel autoencoder scheme and the powerful CE are potentially attractive approaches for the Internet of Vessels (IoV).

关键词： autoencoder channel estimation convolutional neural networks (CNNs) Internet of Vessels (IoV) orthogonal frequency-division multiplexing (OFDM)

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Anomaly detection in random circuit patterns using autoencoder

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JOURNAL OF MICRO-NANOPATTERNING MATERIALS AND METROLOGY-JM3 2021年第4期20卷

作者： Fukuda, Hiroshi Kondo, Tsuyoshi Hitachi High Tech Corp Tokyo Japan

Background: Systematic/stochastic pattern defects affect the production yield of integrated circuits (IC) containing trillions of 10-nm level features. Aim: We detect pattern anomalies/defects from images obtained from scanning electron microscopes (SEM) for random/arbitrary IC patterns without using design data. Approach: We decompose SEM images into small sub-images and apply an identical autoencoder to each of them to detect anomalies. The astronomical varieties in random IC patterns are reduced into limited varieties in elementary patterns, which are coded onto limited dimension latent vectors in autoencoder. The discrepancy between autoencoder input and output represents a deviation of local pattern shapes from ideal or allowed ones and is used as an index of anomaly. Results: A wide variety of anomalies/defects are detected in regular and random IC patterns fabricated by extreme ultra-violet lithography without prior knowledge about anomalies at a high signal-to-noise ratio within a time shorter than the typical image acquisition time of SEMs. They include missing/necking in holes/trenches, collapsing/breaking in lines, various local pattern distortion/deformation, and tiny particles. Frequency and spatial distributions of the discrepancy index are sensitive to process changes and can be used for visualizing the sign or causes of anomalies. Conclusions: The method is effective for inspecting memory and random logic ICs with high-speed SEMs. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.

关键词： integrated circuits defect inspection anomaly detection machine learning autoencoder lithography

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A physics-informed autoencoder for system health state assessment based on energy-oriented system performance

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 242卷

作者： Huang, Xucong Peng, Zhaoqin Tang, Diyin Chen, Juan Zio, Enrico Zheng, Zaiping Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Sch Mech Engn & Automat Control Beijing 100191 Peoples R China Politecn Milan Energy Dept Milan Italy PSL MINES Paris France Beijing Inst Precise Mechatron & Controls Beijing 100076 Peoples R China

Health Indicators (HIs) have been widely used for health state assessments. In many applications, HI with physical meaning is a preferred choice to assist system health management due to its inherent nature of objectively and accurately representing the system health state. However, in many cases, the true value of HI with physical meaning is difficult to obtain due to the difficulty in measuring them, which means, the HI is hidden from the user during system operation. It results in difficulty in training HI construction methods. In light of these challenges, we propose a physics-informed autoencoder for HI construction by fusing the physics-based model with deep learning (DL) approaches. In this framework, we redefine the conventional HI construction process with autoencoders into a new paradigm: mapping the sensor readings to a degradation-represented latent space by a DL model and reconstructing the sensor readings by a physics based model. The latent variable, bridging the connection between the encoder and decoder, works as the HI and is meticulously designed with an energy-oriented perspective, thus ensuring its applicability across various systems. Furthermore, a novel training strategy is proposed for this framework to be well-trained. The superiority and effectiveness of the proposed framework are validated on the CALCE battery dataset and electromechanical actuator simulation data. In the two examples, the SOH of batteries and the energy efficiency of electromechanical actuators can both be estimated using the proposed method.

关键词： Health indicator Physics-informed autoencoder Energy-oriented Latent variable

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Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems

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NEUROCOMPUTING 2019年 365卷 137-146页

作者： Abiri, Najmeh Linse, Bjorn Eden, Patrik Ohlsson, Mattias Lund Univ Dept Astron & Theoret Phys Lund Sweden Halmstad Univ Ctr Appl Intelligent Syst Res Halmstad Sweden

Dealing with missing data in data analysis is inevitable. Although powerful imputation methods that address this problem exist, there is still much room for improvement. In this study, we examined single imputation based on deep autoencoders, motivated by the apparent success of deep learning to efficiently extract useful dataset features. We have developed a consistent framework for both training and imputation. Moreover, we benchmarked the results against state-of-the-art imputation methods on different data sizes and characteristics. The work was not limited to the one-type variable dataset;we also imputed missing data with multi-type variables, e.g., a combination of binary, categorical, and continuous attributes. To evaluate the imputation methods, we randomly corrupted the complete data, with varying degrees of corruption, and then compared the imputed and original values. In all experiments, the developed autoencoder obtained the smallest error for all ranges of initial data corruption. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Deep learning autoencoder Imputation Missing data

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One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites

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COMPOSITES PART B-ENGINEERING 2024年 272卷

作者： Zhang, Yubin Xu, Changhang Liu, Pengqian Xie, Jing Han, Yage Liu, Rui Chen, Lina China Univ Petr East China Coll Mech & Elect Engn Qingdao 266580 Peoples R China

Fiber -reinforced polymer (FRP) composites have been widely applied in different industrial fields, thereby necessitating the employment of non-destructive testing (NDT) methods to ensure structural integrity and safety. Active infrared thermography (AIRT) is a fast and cost-efficient NDT technique for inspecting FRP composites. However, this method is easily affected by factors such as inhomogeneous heating, leading to a low level of visualization of defects. To address this issue, this study proposes a novel method called one-dimensional deep convolutional autoencoder active infrared thermography (1D-DCAE-AIRT) to enhance the visualization of internal defects in FRP composites. This method first preprocesses the thermal image sequence acquired by AIRT inspections. Subsequently, high-level thermal features at the pixel level are extracted from the aforementioned preprocessed thermal image sequence using a designed one-dimensional deep convolutional autoencoder (1DDCAE) model. Finally, the extracted high-level thermal features are employed to generate enhanced visualization results that exhibit improved defect visibility. The results of three kinds of AIRT (eddy current pulsed thermography, flash thermography, and vibrothermography) experiments on FRP composite specimens with artificially introduced defects show that 1D-DCAE-AIRT can effectively enhance the visualization of internal defects. The enhancement effect is better than the conventional techniques of fast Fourier transform (FFT), principal component analysis (PCA), independent component analysis (ICA), and partial least -squares regression (PLSR).

关键词： Fiber -reinforced polymer composites Active infrared thermography Thermal image sequence processing One-dimensional deep convolutional autoencoder Feature extraction

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Network Anomaly Detection Using Memory-Augmented Deep autoencoder

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IEEE ACCESS 2021年 9卷 104695-104706页

作者： Min, Byeongjun Yoo, Jihoon Kim, Sangsoo Shin, Dongil Shin, Dongkyoo Sejong Univ Convergence Engn Intelligent Drones Dept Comp Engn Seoul 05006 South Korea Sejong Univ Dept Comp Engn Seoul 05006 South Korea Agcy Def Dev Daejeon 05600 South Korea

In recent years, attacks on network environments continue to rapidly advance and are increasingly intelligent. Accordingly, it is evident that there are limitations in existing signature-based intrusion detection systems. In particular, for novel attacks such as Advanced Persistent Threat (APT), signature patterns have problems with poor generalization performance. Furthermore, in a network environment, attack samples are rarely collected compared to normal samples, creating the problem of imbalanced data. Anomaly detection using an autoencoder has been widely studied in this environment, and learning is through semi-supervised learning methods to overcome these problems. This approach is based on the assumption that reconstruction errors for samples that are not used for training will be large, but an autoencoder is often over-generalized and this assumption is often broken. In this paper, we propose a network intrusion detection method using a memory-augmented deep auto-encoder (MemAE) that can solve the over-generalization problem of autoencoders. The MemAE model is trained to reconstruct the input of an abnormal sample that is close to a normal sample, which solves the generalization problem for such abnormal samples. Experiments were conducted on the NSL-KDD, UNSW-NB15, and CICIDS 2017 datasets, and it was confirmed that the proposed method is better than other one-class models.

关键词： Decoding Anomaly detection Network intrusion detection Data models Memory modules Mathematical model Machine learning Network intrusion detection autoencoder anomaly detection memory-augmented autoencoder

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Utilization of acoustic signals with generative Gaussian and autoencoder modeling for condition-based maintenance of injection moulds

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年第4期37卷 438-453页

作者： Ronsch, G. O. Lopez-Espejo, I Michelsanti, D. Xie, Y. Popovski, P. Tan, Z-H Tech Univ Denmark Dept Stat & Data Anal Lyngby Denmark Aalborg Univ Dept Elect Syst Aalborg Denmark

Predictive and condition-based maintenance is given more and more attention to further optimize the utilization of manufacturing and production equipment. Utilizing acoustic signals for equipment monitoring and condition-based maintenance has been proven effective in many applications. Many manufacturing and production setups consist of multiple alike machines (e.g. within injection moulding) where it would be beneficial to use the same monitoring setup and configuration on all machines. Based on an industrial application within injection moulding (using five different injection moulds), a methodology is proposed utilizing acoustic signals from injection moulds combined with generative Gaussian or autoencoder modeling. To improve the generalization ability of generative modeling to moulds not seen at training time, a simple yet effective model adaptation is proposed, which only requires a few faultless moulding cycles at runtime/test time. The best results are obtained using the Gaussian model, where area under the curve values close to one are achieved when employing a model adapted to the specific mould at test time to detect abnormal situations like mechanical-defective moulds (loose latch lock) and the need for lubrication. The proposed framework is light in terms of computation and makes the setup implementation practically feasible in a real industrial context with multiple similar machines.

关键词： Acoustic signal processing predictive maintenance injection moulding Industry 4 0 machine learning autoencoder

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