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检索条件"机构=Department of Cyber Security and Computer Engineering Technology"

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Intelligent Model to Predict Early Liver Disease using Machine Learning Technique

Intelligent Model to Predict Early Liver Disease using Machi...

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2022 International Conference on Business Analytics for technology and security, ICBATS 2022

作者： Ghazal, Taher M. Rehman, Aziz Ur Saleem, Muhammad Ahmad, Munir Ahmad, Shabir Mehmood, Faisal Faculty of Information Science and Technology Ukm Network and Communication Technology Lab Center for Cyber Security Malaysia School of Computer Science NCBAandE Lahore Pakistan Gachon University Department of Computer Engineering Korea Republic of Gachon University Department of It Convergence Engineering Korea Republic of

ISBN: (纸本)9781665409209

Liver Disease (LD) is the main cause of death worldwide, affecting a large number of people. A variety of factors affect the liver, resulting in this disease. The diagnosis of this condition is both expensive and time-consuming. Machine Learning offers a lot of potential in terms of automated disease diagnosis. As a result, the purpose of this research is to assess the efficacy of various Machine Learning (ML) algorithms to lower the high cost of liver disease diagnosis through prediction. With the current rise in numerous liver disorders, it's more important than ever to detect liver disease early on. This research proposed intelligent model to predict liver disease using machine learning technique. This proposed model is more effective and comprehensive in terms of performance of 0.884 accuracy, and 0.116 miss-rate. © 2022 IEEE.

关键词： Forecasting

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Detection of Network Attacks using Machine Learning and Deep Learning Models

Detection of Network Attacks using Machine Learning and Deep...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Dhanya, K.A. Vajipayajula, Sulakshan Srinivasan, Kartik Tibrewal, Anjali Kumar, T. Senthil Kumar, T. Gireesh TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India STSM Architect IBM Security IBM India Sr. Architect IBM Security IBM India Pvt Ltd India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Anomaly-based network intrusion detection systems are highly significant in detecting network attacks. Robust machine learning and deep learning models for identifying network intrusion and attack types are proposed in this paper. Proposed models have experimented with the UNSW-NB15 dataset of 49 features for nine different attack samples. Decision Tree classifier produced the best accuracy of 99.05% compared to ensemble models - Random forest(98.96%), Adaboost(97.87%), and XGBoost(98.08%). K-Nearest Neighbour classifier trained for various values of K and best performance obtained for K=7 with the accuracy of 95.58%. A Deep Learning model with two dense layers with ReLU activation and a third dense layer with a Sigmoid activation function is designed for binary classification and produced good accuracy of 98.44% with ADAM optimizer, 80:20 Train-Test Split Ratio. Network attack exploits are detected with an accuracy 95% by XGBoost, Fuzzers attack with 90% accuracy by Random Forest, Generic attacks with 99% accuracy by Random Forest, and Reconnaissance attacks with 79% by Decision Trees. All features are relevant and strong in network attack detection, which eliminates the requirement of feature selection. © 2022 Elsevier B.V.. All rights reserved.

关键词： Network security

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Optimizing Cloud-Based IoT Architectures with an Energy-Sensitive Load Balancing Algorithm for Enhanced Resource Utilization and Network Efficiency 4

Optimizing Cloud-Based IoT Architectures with an Energy-Sens...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Kumar, Ravi Raghavendar, K. Chary, Kalvog Prakasha Naresh, K. Reddy, G. Venkata Rami Kumar, D. Sravan Sreenidhi University Department of Cse Hyderabad India Teegala Krishna Reddy Engineering College Department of Cse Hyderabad India Cvr College of Engineering Department of CSE Cyber Security Hyderabad India Tkr College of Engineering & Technology Department of Cse Hyderabad India Vardhaman College of Engineering Department of Cse Hyderabad India Narshima Reddy Engineering College Maisammaguda Hyderabad India

ISBN: (纸本)9798350387490

Cloud computing, which provides virtualized computing resources online, has become increasingly popular. Effective load balancing (LB) is essential for distributing dynamic workloads among cloud systems and ensuring equitable resource sharing, which prevents server overloads. This study presents a customized cloud-based load balancer algorithm designed for IoT network protocols, evaluating the demands of virtual and physical host machines in cloud environments. The algorithm manages loads within cloud-based IoT architectures to enhance energy efficiency and improve network response times while integrating seamlessly with existing frameworks. Utilizing proportional sharing, the method migrates heavily loaded virtual machines to less strained physical hosts. Key characteristics, including memory specifications, CPU utilization, power consumption, and SLA violation management, are considered. The Updated Energy-Sensitive Balancing Load Algorithm (ESBL) is developed to analyze IoT sensor data and, compared to older approaches, optimizes load balancing and other critical parameters. The proposed load balancer significantly improves resource utilization and network efficiency, ultimately optimizing IoT communication in cloud computing environments. © 2024 IEEE.

关键词： Cloud platforms

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Design of Lightweight Cryptography based Deep Learning Model for Side Channel Attacks 33

Design of Lightweight Cryptography based Deep Learning Model...

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33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Nafi, Nazmus Shaker Aman, Azana Hafizah Islam, Shayla Fadhil, Saif Aamer Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Integrated Work Package KBR SouthbankVIC Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350317138

Depending on a device's encryption mechanism, a wide variety of tangible details could be exposed. These leaks are used in side-channel analysis, which is used to get keys. Due to deep learning's sensitivity to the characteristics of the data being processed, using such algorithms can significantly improve the accuracy and efficiency of side channel analysis. However, classic neural networks are now used for the vast majority of the work that is being done. When the number of nodes in a network grows, so does the efficiency with which key recovery can function. However, the method's computing complexity grows in direct proportion. Overfitting, inadequate capacity for feature extraction, and inefficient training are all potential issues. In this study, we develop a compact convolutional neural network by enhancing a previously existing combination of neural networks. Novel neural network along with previous neural network both have their own implementations of the side-channel analysis used in comparative trials. Statistically, the new network has better accuracy, quicker convergence, and more robustness. As part of the research, heatmaps were provided as a means of data visualisation. The critical interval concentration is higher and the heat value is higher in the new network. Conventional neural networks, which serve as the foundation for various kinds of neural networks, perform much worse than side channel studies based on feature fusion networks. © 2023 IEEE.

关键词： Efficiency

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Power Load Forecasting Model Based on LSTM and Prophet algorithm 3

Power Load Forecasting Model Based on LSTM and Prophet algor...

引用

3rd International Conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Zhang, Zhiling Wang, Xu Wang, Jing Yuan, Xueyu College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China Institute of Economics and Finance Polessky State University Pinsk Brest Belarus School of Information Engineering Shaoguan University Shaoguan China

ISBN: (纸本)9798350331578

Power system load forecasting in different periods is of great practical significance to improve the economic and social benefits of the power system, while the complex and variable meteorological conditions, social events and many other uncertain factors may have effects on the load of power system, which makes the application of traditional load forecasting models have certain limitations. At the same time, with the diversification of the load structure of power system, the effect of model application reduces gradually, so the problem of power system load forecasting needs to be further studied. In this paper, the combination method based on LSTM neural network and Prophet time series forecasting model is applied in order to forecast the power load with their respective model advantages for the fluctuation pattern of power load time series in different time periods. The proposed model has a certain reference value for improving the forecasting accuracy of power load and achieving accurate power deployment. © 2023 IEEE.

关键词： Long short-term memory

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Internet Public Safety Event Grading and Hybrid Storage Based on Multi-feature Fusion for Social Media Texts 28th

Internet Public Safety Event Grading and Hybrid Storage Bas...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Hu, Die Xie, Yulai Feng, Dan Zhao, Shixun Fu, Pengyu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

With the rapid development of mobile Internet technology, various public events appear on social media platforms and attract a lot of attention. Since netizens can express their opinions freely on the Internet, some events that cause negative public opinion seriously threaten public security, which are called Internet Public Safety events (IPSe). Existing solutions use a single metric to realize event detection, which has a high false detection rate for Internet Public Safety events. In addition, they lack countermeasures after the outbreak of public opinion, resulting in inefficient information management. This paper proposes a novel Internet Public Safety event grading method based on multi-feature, which measures events from the three perspectives of heat, emotion, and sensitivity. In order to improve the retrieval efficiency of events information, we implement a smart dynamic hot/cold data migration mechanism using a hybrid storage system containing solid-state drives and hard-disk drives, which realizes real-time data adjustment in the storage layer and ensures efficient events management. In the experiments, we verify our method through several real internet events, and the results show that our method achieves the state-of-the-art accuracy in the detection of Internet Public Safety events and realizes efficient retrieval with a low query overhead. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Grading

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Detection of Crucial Power Side Channel Data Leakage in Neural Networks 33

Detection of Crucial Power Side Channel Data Leakage in Neur...

引用

33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Satar, Nurhizam Safie Mohd Nafi, Nazmus Shaker Aman, Azana Hafizah Islam, Shayla Fadhil, Saif Aamer Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Integrated Work Package KBR SouthbankVIC Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350317138

Neural network (NN) accelerators are now extensively utilized in a range of applications that need a high degree of security, such as driverless cars, NLP, and image recognition. Due to privacy issues and the high cost, hardware implementations contained within NN Propagators were often not accessible for general populace. Additionally with power and time data, accelerators also disclose critical data by electro-magnetic (EM) sided channels. Within this study, we demonstrate a side-channel information-based attack that can successfully steal models from large-scale NN accelerators deployed on real-world hardware. The use of these accelerators is widespread. The proposed method of attack consists of two distinct phases: 1) Using EM side-channel data to estimate networking's underlying architecture;2) Using margin-dependent, attackers learning actively in estimating parameters, notably weights. Deducing the underlying network structure from EM sidechannel data. Inferring the underlying network structure from EM sidechannel data. Experimental findings demonstrate that the disclosed attack technique can be used to precisely retrieve the large-scale NN via the use of EM side-channel information leaking. Overall, our attack shows how critical it is to conceal electromagnetic (EM) traces for massive NN accelerators in practical settings. © 2023 IEEE.

关键词： Side channel attack

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Innovative Method for Detecting Liver Cancer using Auto Encoder and Single Feed Forward Neural Network 2

Innovative Method for Detecting Liver Cancer using Auto Enco...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Sowparnika, B. Yamini, Kalva Walid, Md. Abul Ala Prasad, Jhakeshwar Aparna, N. Chauhan, Amit Nandha Engineering College Department of Biomedical Engineering Tamilnadu Erode India Sriher University Cyber Security in Computer Science and Engineering Sri Ramachandra Faculty of Engineering Tamilnadu Chennai India Department of Computer Science and Engineering Bangladesh Shri Shankaracharya College of Pharmaceutical Sciences Chhattisgarh Junwani Bhilai India Dhaanish Ahmed Institute of Technology Department of Bio Medical Engineering Tamilnadu Coimbatore India Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9781665456302

Liver cancer ranks sixth among all cancers in frequency of incidence. A CT scan is the gold standard for diagnosis. These days, CT scan images of the liver and its tumor can be segmented using deep learning and Neural Network techniques. In this proposed approach to identifying cancer cells, it's focus on four important areas: To enhance a photo by taking out imperfections and unwanted details. An ostu method is used for this purpose. Specifically, this proposed approach to use the watershed segmentation technique for image segmentation, followed by feature extraction, in an effort to isolate the offending cancer cell. After finishing the model training with AE-ELM. To do this, Extreme Learning Machine incorporates an auto encoder. To achieve effective and supervised recognition, the network's strengths of Extreme Learning Machine (ELM) are thoroughly leveraged, including its few training parameters, quick learning speed, and robust generalization ability. The auto encoder-extreme learning machine (AE-ELM) network has been shown to have a respectable recognition impact when the sigmoid activation function is used and the number of hidden layer neurons is set to 1200. According to the results of this investigation, a method based on AE-ELM can be utilized to detect the liver tumor. As compared to the CNN and ELM models, this technique achieves superior accuracy (around 99.23%). © 2023 IEEE.

关键词： Support vector machines

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Privacy and security of Content: A Study of User-resilience and Pre-checks on Social Media 5

Privacy and Security of Content: A Study of User-resilience ...

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5th International Conference on Information technology for Education and Development, ITED 2022

作者： Nwankwo, Chukwuemeka Uwadia, Francis Nwankwo, Wilson Adigwe, Wifred Chinedu, Paschal Ojei, Emmanuel Edo State University Department of Computer Science Uzairue Nigeria Delta State University of Science and Technology Department of Cyber Security Ozoro Nigeria Edo State University Department of Computer Engineering Uzairue Nigeria Department of Software Engineering Delta State University of Science and Technology Ozoro Nigeria

ISBN: (纸本)9781509064229

In recent times, cybercrimes, kidnapping, and ritual killings are being enabled through the use and abuse of social media technologies and students are becoming cheap targets. Consequently, this study seeks to investigate the imperative of electronic communication styles among students via social media channels vis-a-vis the users' resilience before and during communication on social media to ensure that the message is routed to the intended recipient. In this study, we adopted the case study approach and 3500 students were drawn from different academic programmes in a known tertiary institution in Southern Nigeria. Validly completed questionnaires from 1000 students were analyzed. Findings revealed that 96% of the students who use social media are not concerned with any form of security screening before sending messages on social media networks via their smartphones. © 2022 IEEE.

关键词： Students

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A Comprehensive Analysis on Time Synchronization Protocols in Wireless Sensor Networks 4

A Comprehensive Analysis on Time Synchronization Protocols i...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Senthilkumar, R. Venkateshkumar, S. Sachithanantham, N.C. Rajkumar, D. Revathi, M. Suganthapriya, S. Dr. SNSRajalakshmi College of Arts & Science Department of Computer Science Coimbatore641049 India Dr. SNSRajalakshmi College of Arts & Science Department of Computer Science with Cyber Security Coimbatore641049 India Dr. SNSRajalakshmi College of Arts & Science Department of Information Technology Coimbatore641049 India Coimbatore641049 India

ISBN: (纸本)9798331529635

While fundamental communication in wireless networks relies on time synchronization, it also enables motion, location, and proximity sensing. Four features-time spent sending and receiving, accessing and distributing-define the synchronization challenge. Three modern synchronization strategies are presented: sensor networks' broadcast and temporal synchronization protocols and the Flooding Synchronize Time Protocol. In addition, industrial examples and security concerns are discussed. This study of 32 survey paper articles investigates numerous existing techniques, protocols, and crucial temporal aspects for achieving accurate coordination in a sensor network. An all-node method, a diffusion-based strategy, and group sync techniques are application-specific solutions that strive to provide network-wide synchronization. Traditional time synchronization protocols like RBS and TPSN are also available. A new kind of scalable, topologically independent, fast-converging, energy-efficient, less-latent, and application-dependent system is needed, and this study will examine the most popular current time synchronization methods to bring attention to this requirement. An approach to secure time synchronization under dangerous conditions. This survey's various examined criteria provide a solid foundation for evaluating novel and present synchronization strategies. © 2024 IEEE.

关键词： Proximity sensors

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