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Deep learning methods for malicious URL detection using embedding techniques as Logistic Regression with Lasso penalty and Random Forest 7

Deep learning methods for malicious URL detection using embe...

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7th international conference on parallel, distributed and Grid computing, PDGC 2022

作者： Thakur, Isha Panda, Kajal Kumar, Sanjeev Centre for Development of Advanced Computing Cyber Security Technology Division Mohali India

ISBN: (纸本)9781665454018

In the Internet ecosystem, URLs (Uniform Resource Locators) are widely used to propagate malicious infections through spam, spear-phishing, drive-by-download exploitation, malware embedding, etc. The blacklisting, pattern analysis, and signature-matching approaches are widely used to detect such threats. However, these techniques are very effective in detecting known types of malicious URLs but unable to detect new types of attacks launched by the malicious URLs. Moreover, traditional machine learning-based malicious URL detection is highly dependent upon manual feature engineering, which is a costly approach. , This research study proposes deep learning-based malicious URL detection with less dependent on manual feature engineering to overcome the above-mentioned challenges. The Natural Language Processing (NLP) technique, i.e., Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, along with the N-gram parameter, is used for extracting the features. Afterward, two feature selection techniques as Embedded methods are used - Logistic Regression with L1 (Lasso) penalty and Random Forest Selection. Neural network models such as Deep Neural networks (DNN), Long Short-Term Memory (LSTM), and Convolution Neural networks (CNN) are constructed to detect malicious URLs. The experiment results demonstrate that the DNN model gives the best performance results as 96.95% accuracy, 99% precision, 100% recall, and 99% F1-score with logistic regression along with the L1 (Lasso) penalty as a feature selection method. Finally, this study compares state-of-art implemented deep learning methods for malicious URL detection. © 2022 IEEE.

关键词： Inverse problems

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Optimal Network Architecture and Inference Dependencies for Efficient Training of Deep Neural networks in Bioinformatics

Optimal Network Architecture and Inference Dependencies for ...

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international conference on parallel distributed and Grid computing (PDGC)

作者： J. Logeshwaran Durgesh Srivastava Manoj Pal S. Dhanasekaran Anuradha S Nigade Keshav Kaushik Department of Computer Science Christ University Bengaluru Karnataka India Chitkara University Institute of Engineering & Technology Chitkara University Rajpura Punjab India Artnet Worldwide Corporation Newyork USA Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India Department of Electronics & Communication Engineering Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune Maharashtra India Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Optimal Network Architecture and Inference Dependencies for Deep Learning in Bioinformatics are all about how to efficiently design and train deep neural networks for bioinformatics use-cases referring to training or inference dependencies. It attempts to optimize the network architecture and reduce the entanglement between different parts of the network, which can train deep neural networks in bioinformatics more eventfully and faster. The performance of the deep learning models and their accuracy can be enhanced overall. It is beneficial for the study, analysis, and interpretation of biological data by cautiously opting for the network architecture and minimizing the dependencies. Moreover, such optimization can prevent overfitting as well as save the computational resources and time consumed during training, making it more feasible to upscale it for deep learning on big data in bioinformatics.

关键词： Training Deep learning Proteins Biological system modeling Artificial neural networks Network architecture Grid computing Bioinformatics Optimization Overfitting

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Scalable Network Intrusion Detection using Hybrid RNN-MLP Architectures

Scalable Network Intrusion Detection using Hybrid RNN-MLP Ar...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Muthaiah U VidhyaSagar B S Adireddy Srinivas Naidu Karri Likhith Naidu Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai Tamilnadu India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

There is an elevated rise of threats against network infrastructure in this digital age through cyber threats. This cause makes the risk of malicious network intrusions elevate and higher for organizations and individuals. This paper discusses Network Intrusion Detection (NID) utilizing deep learning to differentiate benign and malicious network traffic. A number of performances of algorithms were evaluated MLP, Random Forest, and LSTM-based recurrent neural networks. The results of evaluation show that the proposed solution is appropriate; therefore, the highest accuracy was achieved by the MLP model with 98.49%, which outperformed the others in the related metrics of accuracy, precision, recall, and F1-score. Therefore, it aims to provide organizations with a robust network security solution that will minimize the impact of cyber intrusions against their network infrastructures while ensuring integrity and safety.

关键词： Deep learning Measurement Recurrent neural networks Accuracy Organizations Telecommunication traffic Network security NSL-KDD Safety Random forests

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A Multi-Path Orchestration Method for Cloud-Edge Data Transmission Based on DRL

A Multi-Path Orchestration Method for Cloud-Edge Data Transm...

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international conference on parallel and distributed Systems (ICPADS)

作者： Zhihui Wang Xubing Dou Shuyun Li Huan Ma Xinchang Zhang Maoli Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China QuFu normal university Jining China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In addressing the multi-path problem of cloud-edge data transmission, this paper introduces a multi-path orchestration method based on deep reinforcement learning (DRL) within the context of software-defined networks (SDN), termed Deep Q-Routing (DQR). This method establishes optimal data transmission paths between the cloud and multiple edges, based on available bandwidth and transmission delay, while avoiding path congestion to improve bandwidth utilization and reduce transmission delay. The optimal routing in SDN is defined through the design of state space sets, reward functions, and neural networks. Simulation experiments were conducted to evaluate the DQR method. Compared to existing routing methods, the proposed DQR method, which includes a reward function with weighted parameters, demonstrates superior overall network performance.

关键词： Cloud computing Scalability Neural networks distributed databases Bandwidth Routing Deep reinforcement learning Delays Data communication Software defined networking

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Optimizing Depthwise Convolutions on ARMv8 Architecture 23rd

Optimizing Depthwise Convolutions on ARMv8 Architecture

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23rd international conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Hao, Ruochen Wang, Qinglin Yin, Shangfei Zhou, Tianyang Zhang, Qingyang Mei, Songzhu Shen, Siqi Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha410073 China Xiamen University Xiamen China

ISBN: (纸本)9783031299261

Depthwise convolutions are widely used in lightweight convolutional neural networks (CNNs). The performance of depthwise convolutions is mainly bounded by the memory access rather than the arithmetic operations for classic convolutions so that direct algorithms are often more efficient than indirect ones (matrix multiplication-, Winograd-, and FFT-based convolutions) with additional memory accesses. However, the existing direct implementations of depthwise convolutions on ARMv8 architectures feature a bad trade-off between register-level reuse of different tensors, which usually leads to sub-optimal performance. In this paper, we propose a new direct implementation of depthwise convolutions by means of implicit padding, register tiling, etc. Compared to the existing ones, our new implementations can incur much less communication overhead between registers and cache. Experimental results on two ARMv8 CPUs show that our implementation can averagely deliver 4.88 × performance improvement over the existing direct ones in open-source libraries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Balance Graphs: An Aid for Studying Convolutional Neural networks 24

Balance Graphs: An Aid for Studying Convolutional Neural Net...

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24th IEEE/ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD 2022

作者： Embery, Lyell Ignatious, Eva Azam, Sami Jonkman, Miriam De Boer, Friso College of Engineering and It Charles Darwin University Casuarina Australia

ISBN: (纸本)9798350310412

Deep Learning Neural networks offer a powerful tool to process visual data and to make decisions, but a limitation is its black box nature which offers low transparency to human examiners. This hurdle presents a particular challenge in using Neural networks in safety-critical systems, which require high performance and transparency such as medical diagnosis of life-threatening diseases. This paper seeks to build and test a neural network for grading gliomas of comparable function and complexity to others in the field, then to apply Data Visualisation techniques to render the internal workings of the NN more understandable to a human observer. The purpose is to develop a system that can classify brain tumors into low-grade gliomas (LGG) and high-grade gliomas (HGG), to aid with diagnosis and prognosis The Brain Tumor Segmentation Challenge 2020 (BraTS2020) data set was used, with data categorised based on a combination of grade assigned in BraTS2020, and the labels in the segmentation data. As some categories are over-represented, methods were employed to ensure a better balance between different categories. Data augmentation was used to expand the limited number of scans in the BraTS2020. A 3D convolution neural network (CNN) was constructed to grade gliomas. With the method developed in this paper, an accuracy of 94.1% was achieved. A newly devised method to visually represent the weights of a convolution is explored. These graphs, called 'weight graphs' allow convolutions to be condensed into a visual medium. The weight graph is designed for easy visual interpretation of the weights assigned within a particular convolution. To overcome the limitations of weight graphs, an alternate graph was devised, called a balance graph, because it shows the overall balance of weights in a kernel, allowing for a quick impression of what effect a single kernel has. It is demonstrated that Balance Graphs improve the accessibility and transparency of the of the weights in convolut

关键词： Convolution

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural networks and Transfer-Learning 4

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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4th international conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Yu, Yang Xie, Yufei Wang, Wenlin Wu, Guohua Lan, Haitao Lin, Enbo An, Ping Sun, Zibin Zhang, Haichuan Wu, Yixian National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9798331531409

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R2: 0.735). © 2024 IEEE.

关键词： Coolants

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Underwater Waste Detection with YOLO: A High-Performance computing Technique

Underwater Waste Detection with YOLO: A High-Performance Com...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Vivek Kumar Umangi Nigam Manan Goel Neeraj Rathore Ajitesh Kumar Mayank Srivastava School of Comp. Sc. Engg. & Tech. Bennett University Greater Noida India Operations & Technology Management UPES Dehradun India Computer Engineering and Application GLA University Mathura India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Waste accumulation in the oceans poses severe environmental challenges that require efficient mechanisms for detection and removal. The methods usually employ slow and resource-intensive techniques like sonar-based detection and manual inspection, hence the need for automated mechanisms of detection. Waste detection under water is a regular monitoring task. Thus, the work proposes rapid model training which allows real-time deployment and enhancement for prevention of degradation of water bodies for various type of wastes. The work uses high-performance computing techniques such as parallel processing, NVIDIA GPUs, CUDA, and YOLO. A deep learning model featuring Convolutional Neural networks (CNN) is integrated with YOLO (You Only Look Once) to augment detection speed along with accuracy. The model training time is particularly important for real-time applications which allows rapid iteration for the researchers and practitioners. This work points to the fact that the GPU architecture has accelerated 41X in training deep models and improved over precision, and inference time. In all aspects the proposed model claims to perform real-time waste detection within permissible computational latency. This work is in demand for autonomous systems like underwater drones which demand the fast and timely decision-making capability to operate and maneuver properly in dynamic operational environments.

关键词： YOLO Training Deep learning Computational modeling High performance computing Graphics processing units Computer architecture parallel processing Real-time systems Convolutional neural networks

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Nona: A Framework for Elastic Stream Provenance

Nona: A Framework for Elastic Stream Provenance

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international conference on distributed computing Systems

作者： Bastian Havers Marina Papatriantafilou Vincenzo Gulisano Chalmers University Volvo Cars Gothenburg Sweden Chalmers University Gothenburg Sweden

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Forward Provenance for streaming queries run by distributed and parallel Stream Processing Engines gives fine-grained insights on input-output data dependencies enabling, e.g., precise debugging and smart data selection. State-of-the-art provenance frameworks, though, build on an assumption that is unrealistic for distributed systems like Vehicular networks and Smart Grids, namely, that the whole set of queries in need of provenance is known in advance and static. In real-world use cases, queries are continuously added, removed, and modified over time by both data analysts and SPE systems themselves. Motivated by the lack of solutions for the forward provenance of dynamic sets of queries, we introduce a novel framework, named Nona, for parallel and distributed streaming queries. We formalize the notion of forward provenance for evolving query sets and prove it is possible to extend the same guarantees the state-of-the-art offers for static query sets. Our evaluation shows that Nona can cope with adaptations to changes in query sets with sub-second responsiveness; moreover, it incurs negligible overheads compared to the state-of-the-art, during the periods in which a query set does not undergo changes.

关键词： distributed databases Watermarking Debugging Computer architecture Smart grids Maintenance Vehicle dynamics

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CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms 23

CD-Sched: An Automated Scheduling Framework for Accelerating...

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Proceedings of the 2023 international conference on Power, Communication, computing and Networking Technologies

作者： Yuanyuan Xiao Zhiquan Lai Dongsheng Li National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology China

ISBN: (纸本)9781450399951

DSP holds significant potential for important applications in Deep Neural networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%.

关键词： computation offloading

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