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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Hoque, Md Mozammal Alam, Khorshed Monir, Md Fahad Tarek Habib, Md University of Houston Department of Electrical and Computer Engineering United States Department of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798331517786

Trojan detection from network traffic data is crucial for safeguarding networks against covert infiltration and potential data breaches. Deep learning (DL) techniques can play a pivotal role in detecting trojans from network traffic data by learning complex patterns and anomalies indicative of malicious behavior, thus enhancing detection accuracy and efficiency. This technique is pivotal in the context of data security, specifically in next-generation network architecture for its ability to effectively detect complex and evolving cyber threats, such as Trojan horse traffic, ensuring robust network protection. The research gap in detecting network Trojans lies in developing techniques capable of efficiently identifying stealthy and polymorphic variants while minimizing false positives. This study focuses on trojan detection from network traffic data, crucial for network security. Deep learning, specifically a combination of Convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks, is proposed to enhance accuracy and efficiency by learning complex patterns indicative of malicious behavior. Our proposed approach extracts features using CNNs to capture spatial dependencies and utilizes BiLSTM networks to process sequential dependencies, achieving 99% accuracy in trojan detection on real-world datasets. We validated this performance by comparing with previous approaches to ensure robustness in a common dataset. Making our work available open-source on, can enhance its accessibility and promote future research opportunities in Trojan Network Detection. After this work, we will work on detecting trojan networks from our institute's network data and will release the trojan network dataset, as there is a gap in open-source trojan network datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Deep Learning Models for Sentiment Analysis Integrating Texts and Emojis: A Comprehensive Survey 10

Enhancing Deep Learning Models for Sentiment Analysis Integr...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Suresh Kumar, S. Gayathri, D. Shugavaneshwar, R. Department of Computer Science and Engineering Tamilnadu Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350353068

Sentiment analysis is used to get meaningful insights from data that is being retrieved from various resources or social media platforms. Sentiment analysis plays an important role in making crucial decisions that can improve the existing status significantly. This survey addresses and compares various machine learning models and deep learning models that have been recently employed for efficient sentiment analysis. In general, people use both texts and emojis to express their emotions. Considering the above fact, the proposed system addresses reviews that integrate both texts and emojis. The convolution neural network (CNN) is trained with a dataset that contains a cluster of texts and emojis. However, the texts and emojis are handled separately by processing them independently for efficient results. © 2024 IEEE.

关键词： Sentiment analysis

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Training Speech Recognition Model with Speech Synthesis and Text Discriminator

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Journal of Information science and engineering 2024年第2期40卷 359-373页

作者： Lin, Hou-An Chen, Chia-Ping Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804 Taiwan

In this paper, we build neural-network model-based automatic speech recognition (ASR) systems incrementally for performance improvement. First, we add an adversarial text discriminator module to train the speech recognition model to correct typos in recognition results. Experiments show that the character error rate (CER) and word error rate (WER) of the ASR system achieved 12.3% and 31.4%. Second, we insert a pre-trained speech synthesis (text-to-speech, TTS) module to the ASR model. When we exploit a pre-trained TTS in ASR training, the CER and WER are reduced from 12.6% and 31.7% to 10.8% and 24.4%, demonstrating that pre-trained TTS can improve ASR. Finally, we include both pre-trained TTS and text discriminator in ASR training. The performance of this ASR system is further improved, achieving the CER and WER of 9.9% and 22.7% respectively. On Formosa Speech Recognition Challenge task using Taibun Hàn-jı¯ transcription, the proposed method also achieves better CER than a system based on hybrid DNN-HMM chain model. © 2024 Institute of Information science. All rights reserved.

关键词： Speech recognition

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Parallel Computation of Dominance Scores for Multidimensional Datasets on GPUs

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IEEE Transactions on Parallel and Distributed Systems 2024年第6期35卷 764-776页

作者： Chen, Wei-Mei Tsai, Hsin-Hung Ling, Joon Fong National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan

The dominance scoring problem in a multidimensional dataset is to return the number of points dominated by a given point, which is a common metric for evaluating the quality of a data point. Dominance scoring is an elementary operator for variations of the skyline operator, including top k dominating and k-skyband queries. This study proposes query processing for dominance scores that operates primarily on the graphics processing unit (GPU) to fully utilize its massive processing resources and restricted memory space while reducing the transfer overhead between the central processing unit (CPU) and GPU. We introduce a heap-based multidimensional data structure with complete and well-balanced characteristics. Using our preprocessed data, we can construct a complete R-tree with the non-overlapping property, ensuring that the bounding boxes of internal nodes of the same level do not overlap, thereby reducing redundant operations. In addition, we propose two algorithms based on depth-first and breadth-first traversals to accumulate the dominance score on GPUs in parallel. Both take full advantage of the GPU's computing resources and memory space supported by the non-overlapping tree structures. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithms implemented on GPUs dramatically improve the efficiency of dominance scoring. © 1990-2012 IEEE.

关键词： Graphics processing unit

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An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features 3rd

An Ensemble Machine Learning-Based Approach for Detecting Ma...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Uddin, Khandaker Mohammad Mohi Islam, Md. Ashraful Hasan, Md. Nahid Ahmad, Kawsar Haque, Mir Aminul Department of Computer Science andEngineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1205 Bangladesh Department of Computer Science and Engineering Jahangirnagar Univerisity Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

The Internet has transformed into a hub for a wide array of illegal activities, ranging from annoying spam ads to financial scams, all thanks to advancements in modern technology. With the constant enhancements in network technology, data traffic on the Internet is ballooning in both volume and scope. Consequently, the complexity and prevalence of cyber threats and attacks are on the rise, with websites becoming the prime targets for hackers. These intruders, or hackers, embed harmful content into web pages with the intent of carrying out nefarious activities. They employ various tactics, such as hacking into computers or trying to harvest information through compromised or malicious websites. Some of these tactics involve injecting malicious software into web links. Therefore, it has become more critical than ever to identify and block such websites before the average user can stumble upon them. In this paper, we propose a method that utilizes machine learning techniques to detect fraudulent websites. We evaluate the prediction accuracy of different machine learning classification methods in this study. We trained our model using various algorithms, including Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), Decision Trees, Random Forests, and AdaBoost. Ultimately, we employed an ensemble approach and achieved an outstanding accuracy rate of 99.98%, surpassing other methods in effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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SRC:Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

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computer Systems science & engineering 2023年第8期46卷 2337-2349页

作者： Basanta Kumar Swain Mohammad Zubair Khan Chiranji Lal Chowdhary Abdullah Alsaeedi Department of Computer Science&Engineering Government College of EngineeringKalahandiBhawanipatna766002India Department of Computer Science and Information Taibah UniversityMedina42353Saudi Arabia School of Information Technology and Engineering VIT University Vellore632014Tamil NaduIndia Computer Science Department College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network(DHO-ANN).The noisy crowdsourced cough datasets were collected from the public *** research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech *** proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures,F1 score,confusion matrix,specificity,and sensitivity ***,it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset,***,the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5%compared to the existing methods.

关键词： COVID-19 GFCC DHO-ANN cough data

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supported by the National Natural science Foundation of China(Nos.62172051,61772085,and 61877005)and Jiangsu Agriculture science and Technology Innovation Fund(***(18)3054).

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Tsinghua science and Technology 2023年第1期28卷 82-92页

作者： Jinghui Zhang Yuchen Wang Tianyu Huang Fang Dong Wei Zhao Dian Shen School of Computer Science and Engineering Southeast UniversityNanjing 211189China Hefei Comprehensive National Science Center Hefei 231299China School of Computer Science and Technology Anhui University of TechnologyHefei 230026China Department of Computer Science&Engineering The Chinese University of Hong KongHong Kong 999077China

Numerous neural network(NN)applications are now being deployed to mobile *** applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to the computing ability of mobile ***,devices’life and performance depend on ***,in many scenarios,such as industrial production and automotive systems,where the environmental temperatures are usually high,it is important to control devices’temperatures to maintain steady *** this paper,we propose a thermal-aware channel-wise heterogeneous NN inference *** contains two parts,the thermal-aware dynamic frequency(TADF)algorithm and the heterogeneous-processor single-layer workload distribution(HSWD)*** on a mobile device’s architecture characteristics and environmental temperature,TADF can adjust the appropriate running speed of the central processing unit and graphics processing unit,and then the workload of each layer in the NN model is distributed by HSWD in line with each processor’s running speed and the characteristics of the layers as well as heterogeneous *** experimental results,where representative NNs and mobile devices were used,show that the proposed method can considerably improve the speed of the on-device inference by 21%–43%over the traditional inference method.

关键词： neural network inference mobile device temperature adjustment channel-wise parallelization

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Music genre classification using convolution temporal pooling network

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Multimedia Tools and Applications 2024年 1-16页

作者： T. M, Vijayameenakshi T. R, Swapna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Music genre classification is one of the most interesting topics in digital music. Classifying genres is basically subjective, and different listeners may perceive genres in various ways. Furthermore, it might be difficult to classify some songs accurately since they belong to numerous genres. Genres are incredibly wide and ill-defined categories, which makes them problematic. Thus, genre-based measures are inherently inaccurate and coarse. Moreover, not every piece of music cleanly fits into a particular genre. Many papers based on deep neural networks perform sound recognition and classification with input images of audio, which do not affect the time–frequency representation of a signal. The traditional method adds waveform augmentation to the audio signal, thereby increasing the network's training speed. This paper considers music genre classification with the convolution temporal pooling framework and explores the impact of adding the SpecAugment method to augment the spectrogram itself. The augmented spectrogram is then fed into a convolutional temporal pooling network. In this model, the temporal and pooling layers identify the genre pattern and classify the songs based on the genre. It also predicts these duplication that will occur in the given sample. We apply this model to the GTZAN dataset, a widely used dataset for music genre classification. This method improves the identification of Rock and Pop song and also eliminates the replication of the songs. The trained model reports an accuracy of 0.75 for training a 30-s audio file. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional temporal pooling network Genre classification Music genre classification SpecAugment Spectrogram

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A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 1-18页

作者： Kumar, Ankit Shukla, Santosh Kumar Prakash, Navin Yadav, Rakesh Kumar Department of Information Technology Babu Banarasi Das Institute of Technology and Management Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering MSOET Maharishi University of Information Technology Lucknow India

Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing. As a result, the mean grey-level approach was used to segment the MRI images using a threshold. In the second stage of statistical feature analysis, the picture features were extracted using the spatial gray-level dependency matrix using Hara lick's feature equations. As a consequence, the best features were picked and the tumor was placed in the appropriate location. In the third stage, an automated tool for classifying the photographs under assessment as having a tumor or not was created utilizing supervised learning and an artificially intelligent methodology. An efficient test of the network's performance yielded 97% of the desired results. © 2023, The Institution of Engineers (India).

关键词： Tumors

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PCA-IFNNR: Principal component analysis based image fusion directed nearest neighbor representation for dark face image super resolution

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Multimedia Tools and Applications 2024年 1-19页

作者： Kumar, Banti Rajput, Shyam Singh Department of Computer Science & amp Engineering National Institute of Technology Patna Patna800005 India

Facial image captured under uncontrolled environment often degraded by blur, noise and low-light effect which significantly affect the performance of different real life applications. To solve this problem numerous face image super resolution (FISR) methods developed in last two decades. However, getting state-of-the-art accuracy for low-light face images is still a challenging task. Hence, to solve this problem a new FISR method for dark and low-resolution face images is proposed in this paper to enhance the robustness of the existing FISR techniques against darkness (low-light) problems. The proposed method tends to remove the darkness effects present in the input low-resolution (LR) face image. This method first uses the brightness enhancement approach to generate two most suitable brighter images with different brightness levels. Further, the Principal Component Analysis (PCA) based image fusion technique is developed to fuse those two images in order to generate darkness free image. After this, min-max based contrast enhancement technique is modified and applied on the fused image to make it compatible with dataset (dictionary) images for the reconstruction purpose. Further, this enhanced image is used in nearest neighbor representation to generate its equivalent brighter and high-resolution (HR) version. The simulation results using publicly accessible data sets and some real-world images imply that the proposed method outperforms existing state-of-the-art methods in darker scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Principal component analysis

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