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Botnet Attacks Detection using Image processing Approach: Sequential neural network and Feature Selection 14

Botnet Attacks Detection using Image Processing Approach: Se...

引用

14th International conference on Computing Communication and networking Technologies, ICCCNT 2023

作者： Putra, Taufiq Odhi Dwi Ahmad, Tohari Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798350335095

Botnets are networks of compromised computers that are controlled by an attacker for malicious purposes such as spamming attacks, DDoS, or data theft. Botnet attacks detection is a challenging task due to their distributed and covert nature. Botnet attacks are a major security concern for organizations and individuals alike. In this paper we proposed a botnet attacks detection using image processing approach. We propose an AI model architecture using Sequential neural network and feature selection. There are two methods for converting raw data into image data. First method using predefined categorize for choosing features. The second method uses Pearson correlation value between features in dataset and the label. Predefined categorized features have successfully increased the performance of proposed AI model for botnet attacks detection as well as Pearson correlation value. © 2023 IEEE.

关键词： Botnet

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ML-based Translation Methods for Protocols and Data Formats 19

ML-based Translation Methods for Protocols and Data Formats

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19th International conference on network and Service Management (CNSM) - network and Service Management in the Era of Generative AI and Digital Twins

作者： Tothfalusi, Tamas Varga, Eszter Csiszar, Zoltan Varga, Pal AITIA Int Inc Ind IoT Div Budapest Hungary Budapest Univ Technol & Econ Dept Telecommun & Media Informat Budapest Hungary

ISBN: (纸本)9783903176591

In order to exchange information between systems, the information must get encoded into a predefined data format, and it must be transferred in a protocol that the communicating parties have agreed upon. This works well if all parties follow the same protocol standard and use the same data description schemes. If systems use different data formats or protocols, then some sort of translation is required. Protocol and data format translation has been attempted previously through rule-based approaches, ontologies, and also by using machine learning (ML) techniques. Due to the current advances related to AI/ML methods, tools, and infrastructure, the accuracy and feasibility of "translation" with ML-approaches improved significantly. This paper introduces a generic approach and methodology for translating data formats and protocols with ML-based methods and presents our initial results through JSON-XML and JSON-SenML translation.

关键词： protocol translation machine learning neural machine translation natural language processing LLM

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Dynamic Resource Partitioning for Multi-Tenant Systolic Array Based DNN Accelerator 31

Dynamic Resource Partitioning for Multi-Tenant Systolic Arra...

引用

31st Euromicro International conference on Parallel, distributed and network-Based processing, PDP 2023

作者： Reshadi, Midia Gregg, David School of Computer Science and Statistics Lero Trinity College Dublin Dublin 2 Ireland

ISBN: (纸本)9798350337631

Deep neural networks (DNN) have become a significant applications in both cloud-server and edge devices. Meanwhile, the growing number of DNNs on those platforms raises the need to execute multiple DNNs on the same device. This paper proposes a dynamic partitioning algorithm to perform concurrent processing of multiple DNNs on asystolic-array-based accelerator. Sharing an accelerator's storage and processing resources across multiple DNNs increases resource utilization and reduces computation time and energy consumption. To this end, we propose a partitioned weight stationary dataflow with a minor modification in the logic of the processing element. We evaluate the energy consumption and computation time with both heavy and light workloads. Simulation results show a 35% and 62% improvement in energy consumption and 56% and 44% in computation time under heavy and light workloads, respectively, compared with single tenancy. © 2023 IEEE.

关键词： Deep neural networks

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Telemetry Data Compression Algorithm Based on Balanced Cyclic neural network and Deep Learning

Telemetry Data Compression Algorithm Based on Balanced Cycli...

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2023 IEEE International conference on Image processing and Computer Applications, ICIPCA 2023

作者： Huang, Min School of Computer Sichuan Technology and Business University Chengdu620000 China

ISBN: (纸本)9798350314670

With the rapid development of telemetry and telecontrol technology, the amount of information for acquiring telemetry data is increasing, which requires a large amount of storage space when storing, a large bandwidth when transmitting, and data processing as fast as possible. Therefore, data compression technology is needed to solve this problem. The redundancy of telemetry data is also very large. Statistics show that the redundancy of data transmitted in the standard telemetry system reaches more than 90%. In order to reduce the signal space and relieve the pressure of data processing, it is necessary and important to use data compression technology in telemetry system. Considering the space performance and time performance of the lossless compression algorithm, several commonly used lossless compression algorithms are compared for telemetry noise data. The compression space performance of each algorithm still needs to be improved. Considering that the measured engineering data is distributed in a typical balanced way, we studied the anomaly detection problem of time series data with inter class balance, and proposed an anomaly detection problem based on recurrent neural network (RNN). Finally, the overall test of the developed data acquisition and compression system is carried out. The test results show that the system has completed the real-time acquisition and compression of six channels of rapidly varying parameters, and the compression ratio is more than 1.8:1. All indicators meet the design requirements. © 2023 IEEE.

关键词： Redundancy

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distributed Radar Interval Distance Estimation Based on Deep neural network 14

Distributed Radar Interval Distance Estimation Based on Deep...

引用

14th IEEE International conference on Wireless Communications and Signal processing (WCSP)

作者： Li, Xiayu Chen, Peng Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

ISBN: (纸本)9781665450850

In recent years, millimeter wave (mmWave) radar has played an indispensable role in several applications. mmWave radars can measure the distance, speed, and angle of objects, but the transmit power of a single mmWave radar is limited. By deploying multiple mmWave radars in a distributed manner and fusing signals from them, detection results will be improved. Before data fusion, it is necessary to accurately measure the external parameters between different radars to complete the coordinates calibration of the radar network. Current methods focus on the calibration method by jointly observing moving objects in overlapping view fields of the radar network. The calibration process requires one target to move within a defined area. Because the radar cross section (RCS) characteristics of the target in all directions are usually inconsistent, if the reflected signal of this target is weak during the calibration process, the error of this method will be relatively large. This paper proposes a new neural network-based method to estimate the interval distance between different radars without passing through a moving target. The distance estimation error of the proposed network can reach within 0.1 m, which is smaller than the calibration method based on moving objects. Through the verification of actual measured data, the proposed network can more accurately estimate the interval distance between radars.

关键词： distributed radar radar calibration radar interval distance

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Neuromorphic Computing in Autonomous distributed Grid Control

Neuromorphic Computing in Autonomous Distributed Grid Contro...

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2024 IEEE International conferences of AI, Science, Engineering, and Technology, AIxSET 2024

作者： Obert, James Trevizan, Rodrigo D. Chavez, Adrian Sandia National Labs AlbuquerqueNM United States

ISBN: (纸本)9798350390995

Due to the distributed nature of the electrical grid, intelligent, timely control of critical components such as volt-age regulators, capacitor banks and switches in a highly dynamic environment is extremely challenging. Modern day localized controllers in a distribution system such as remote ter-minal units (RTUs) and programmable logic units (PLCs) are often housed in a substation with multiple connections to several critical controls and are thus good candidates for hosting advanced localized control via deep reinforcement learning (DRL) or evolutionary learning. Additionally, it is feasible to greatly reduce the computational throughput and provide fine-grained control measurement and control using a spiking neural network (SNN) neural processing unit (NPU) architecture. In this paper we explore the use of a hybrid grid control algorithm composed of an optimized genetic algorithm (GA) and SNN controller which acquires optimal distribution system voltage control policies while a system is experiencing a cyber adver-sary's injected control noise. We discuss the computational advantages of the GA-SNN hybrid algorithm in the fine-grained autonomous control of a grid's bus voltages using a commercially available neuromorphic system on chip system (NSoC) with a mean rated power consumption of 1 mW and observed mean prediction time of 4 msecs per inference. © 2024 IEEE.

关键词： Deep reinforcement learning

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Fine-grained Task Scheduling Based on Graph neural network and Federated Learning in Vehicle Edge Computing 29

Fine-grained Task Scheduling Based on Graph Neural Network a...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Zhang, Gaowei Song, Junping Hu, Yahui Fan, Pengfei Li, Chong Zhou, Xu Chinese Academy of Sciences Computer Network Information Center Beijing China University of Chinese Academy of Sciences Beijing China China University of Mining and Technology Beijing China

ISBN: (纸本)9798350330717

Vehicle Edge Computing (VEC) has emerged as an efficacious paradigm that supports real-time, computation-intensive vehicular applications. However, due to the highly dynamic nature of computing node topology, existing scheduling algorithms need to more effectively apprehend the characteristics of fine-grained task topologies and network topologies. Moreover, they require significant communication overhead and training costs, making them inadequate for fine-grained task scheduling in vehicular networks. In response, our research explores fine-grained task scheduling issues within VEC scenarios, proposing a scheduling algorithm based on Graph neural networks and Federated Learning (FL-GNN). This algorithm maintains a global scheduling model that periodically aggregates local scheduling models deployed on Roadside Units (RSUs) and high-performance vehicles. Furthermore, to enhance the model's ability to perceive topology and expedite the convergence rate, we incorporate a graph neural network layer in each local model to preprocess the raw state of the VEC environment. Lastly, we construct a simulation platform and implement multiple competitive solutions, demonstrating the superiority of the FL-GNN algorithm in aspects such as reducing the average task delay, balancing the load, and improving the task scheduling success rate. © 2023 IEEE.

关键词： deep reinforcement learning Federated learning fine-grained task scheduling graph neural networks vehicle edge computing

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Parareal with a Physics-Informed neural network as Coarse Propagator 29th

Parareal with a Physics-Informed Neural Network as Coarse Pr...

引用

29th International conference on Parallel and distributed Computing (Euro-Par)

作者： Ibrahim, Abdul Qadir Goetschel, Sebastian Ruprecht, Daniel Hamburg Univ Technol Inst Math Chair Computat Math Hamburg Germany

ISBN: (纸本)9783031396977;9783031396984

Parallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal's single-node performance. This suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures.

关键词： Parareal parallel-in-time integration PINN Machine learning GPUs heterogeneous architectures

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A GNN-DRL-based Collaborative Edge Computing Strategy for Partial Offloading

A GNN-DRL-based Collaborative Edge Computing Strategy for Pa...

引用

IEEE conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Tao, Tianhao Hou, Jiacheng Nayak, Amiya Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9798350310900

Edge computing is an emerging distributed computing paradigm that reduces computation latency and energy consumption by offloading application tasks from user devices to near-edge servers for execution. In order to utilize the computing resources of edge servers and increase efficiency, collaborative edge computing is proposed as a new type of edge computing method where multiple edge servers can work together to solve a task. By dividing a task into interrelated subtasks, each subtask can be processed locally at the device or offloaded to an edge server to minimize processing latency. In the paper, we propose a GNN-DRL-based offloading strategy that considers the edge servers' task attributes and network topology to make an optimal offloading decision for each application subtask. Experiments show that our proposed method performs better than state-of-the-art and baseline strategies in reducing the average latency for each task.

关键词： graph neural network deep reinforcement learning partial computation offloading collaborative edge computing

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Brain Tumour Segmentation and Volume Estimation using Efficient Convolution neural network for MRI Images 2

Brain Tumour Segmentation and Volume Estimation using Effici...

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2nd IEEE International conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Jaichandran, R. Shobana, R. Senthilkumar, S. Srinivasan, A. Arora, Renuka Kumar, Ashok Department of Computer Science and Engineering Paiyanoor India Jagannath University Department of Computer Science and Engineering Haryana India Banasthali Vidyapith Department of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798350347456

A brain tumor is a type of blood clot in the form of cerebral disease. A brain tumor is a way to view and Magnetic Resonance Imaging (MRI) image detail is required. It is difficult to distinguish between normal tissue and tumor tissue from the brain. To should be an accurate analysis of brain tumours. Brain tumour segmentation is one of the most important and difficult tasks in the medical imaging field. Manual classification with human assistance can lead to incorrect predictions and diagnoses. Moreover, it is a difficult task when there is a large amount of data to help. Brain tumours are highly heterogeneous in appearance, and similarities between tumor and normal tissue make it difficult to extract tumour regions from images. In this paper, we proposed a method for extracting brain tumours from 2D magnetic resonance brain images (MRI) by a Fuzzy C-Means clustering algorithm with a traditional classifier and a convolutional neural network. Initially the pre-processing was carried out to apply the filter for image normalization with wiener filter and Robust active shape model was applied to predict the voluminous of data presence and segmentation was carried out through fuzzy c-means clustering and selected features was trained into optimize fuzzy c-means convolution neural network (FCM-CNN). This proposed system produces high detection accuracy in precision rate as well compared to other system. © 2023 IEEE.

关键词： Brain

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