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A Short Survey of AI-driven Load Distribution for Electronic Health Record Management with Recent Deployed Techniques, Research Gaps and Challenges

A Short Survey of AI-driven Load Distribution for Electronic...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： S. Suryanarayanaraju M.Chandra Naik R.N. V Jagan Mohan Department of Computer Science & Engineering GIET University Gunupur Odisha Department of Computer Science & Engineering SRKR Engineering College (A) Chinaamiram

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

With the arrival of Artificial Intelligence (AI), the Internet of Things (IoT), machine learning, and deep learning methodologies, data-driven medical applications have appeared as a promising solution for implementing scalable and robust prognostic and diagnostic approaches with the utilization of medical data. These technologies attained a lot of interest from academia and industry and led to insightful advancements in healthcare systems. The AI-driven techniques related to medical applications still face several issues including privacy, security, and Quality-of-Service (QoS) standards. The complex machine learning models are effectively trained via Federated Learning (FL) in a distributed manner, this advanced technique has become an attractive research area, especially in processing healthcare data at the network edge in a decentralized way to address security concerns and preserve privacy of the information. The availability of FL in the medical domain has several benefits, but it has not been explored to its extent. Existing review works related to FL mainly concentrated on the importance of FL in diverse applications, but no brief or comprehensive survey exists on the distribution of Electronic Health Records (EHR). Thus, this review work focuses on various techniques of AI-driven load distribution for federated networks on EHR. For this purpose, a detailed literature survey is conducted on AI-driven load distribution framework for federated networks on EHR from the previous decades. The AI techniques employed for disease detection are analyzed and categorized. The dataset details, performance metrics, and simulation tools are gathered and listed for a better understanding of the process. Subsequently, the research gaps on the conventional AI-driven load distribution framework for federated networks on EHR are provided to facilitate future work.

关键词： Surveys Data privacy Reviews Medical services Quality of service security Internet of Things Electronic medical records Medical diagnostic imaging Diseases

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Machine Learning (ML) assisted Edge security framework on FPGAs

Machine Learning (ML) assisted Edge security framework on FP...

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international conference on computer and Communication Engineering, ICCCE

作者： Abdul Manan Sheikh Md. Rafiqul Islam Mohamed Hadi Habaebi Suriza Ahmad Zabidi Athaur Rahman Bin Najeeb Ahmed Basahel Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

Edge computing (EC) is an act of bringing computational and storage capability near data sources. It helps to reduce response times and bandwidth requirements. However, the rapid proliferation of edge devices has expanded the attack surface and opportunity for adversaries to penetrate corporate networks. The limited computational abilities of edge devices and the heterogeneous nature of communication protocols further increase the security challenges of EC. Also, the trustworthiness of hardware devices is challenged due to security and privacy threats like trojan insertion, IP cloning, and hardware counterfeits. The application of Machine Language (ML) models in the edge computing paradigm creates a distributed intelligence architecture. Also, Field Programmable Gate Arrays (FPGAs) can exploit Physical Unclonable Functions (PUFs) characteristics to generate and store authentication keys. The PUF structure deployed with ML models in the edge layer can learn its complex input-output mapping from the Challenge and Response pairs (CRPs) to identify the suspicious and unknown responses. This article discusses the security and privacy issues in various layers of the EC architecture and proposes intrusion detection systems through the integration of FPGA-based edge sever and ML models. A PUF-assisted ML framework of the intrusion detection system is proposed to authenticate and detect potential attacks on the network.

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Information Science, Parallel and distributed systems (ISPDS), international conference on

作者： Ya Zhou Ning Zhao Guimin Huang Nanxiao Deng Qingkai Guo Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity; Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset.

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A Brief Study on Cloud security

A Brief Study on Cloud Security

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international conference on Computing and Networking Technology (ICCNT)

作者： Abhay Ajith Adharsh S Mathew Remya S Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

distributed, parallel, and grid computing are all used in a kind of computing known as cloud computing. It serves as a flexible, affordable, and tried-and-true online delivery platform for IT services marketed to businesses or consumers. The outsourcing of critical services to external entities has become a common practice, and this trend has led to various challenges in ensuring the security and privacy of data, maintaining data availability and recovery, and ensuring compliance. Cloud computing, in particular, has added a new layer of complexity to these issues and has introduced additional risks. Cloud computing makes use of many different technologies. Additionally, it carries along its inherent security flaws. Customer access to networks, storage, servers, services, and applications is made possible by cloud technology thanks to how well the pay-per-use model’s services function. Our main goal is to locate and connect these systems’ weaknesses, threats, and remedies.

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Optimizing RF-Sensing for Drone Detection: The Synergy of Ensemble Learning and Sensor Fusion

Optimizing RF-Sensing for Drone Detection: The Synergy of En...

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international conference on distributed Computing in Sensor systems (DCOSS)

作者： Laiba Tanveer Muhammad Zeshan Alam Maham Misbah Farooq Alam Orakzai Ahmed Alkhayyat Zeeshan Kaleem Electrical and Computer Engineering COMSATS University Islamabad Wah Campus Pakistan Wah Pakistan Department of Computer Science Brandon University Canada Brandon Canada Department of Computer Technical Engineering College of Engineering Islamic University Najaf Iraq

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Unmanned Aerial Vehicles (UAVs) find extensive applications across various industries, surveillance, and communication services. However, concerns regarding their potential misuse have prompted the development of counter-drone measures. In this paper, we propose a counter-UAV approach centered on radio frequency (RF) signal sensing. Upon the detection of an RF signal, our system employs a Short-Time Fourier Transform (STFT)-based spectrogram (SP) generation process. This SP is further refined through adaptive windowing and logarithmic tuning to extract multi-intensity features. To classify the complex RF time-domain signals and STFT spectrograms, we utilize two deep learning classifiers: RF-Network and SP-Network, facilitating a multi-class classification process by using deep neural networks (DNN). To enhance the overall accuracy of our model, we leverage an ensemble neural network (EN-Net) by combining predictions from the RF-Network and SP-Network classifiers. Fusing data from a single sensor in both time and frequency domains enhances DNN accuracy by providing complementary information, improving robustness, and reducing overfitting, resulting in increased model performance and a deep understanding of the data. Our results demonstrate a notable improvement in accuracy—specifically, a 36% increase for multi-class models when compared to single-class models. This proves the effectiveness of our EN-Net model in addressing security threats posed by UAVs through advanced RF signal analysis and classification.

关键词： Radio frequency Accuracy Computational modeling RF signals Artificial neural networks Sensor fusion Feature extraction

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SAfER: Simplified Auto-encoder for (Anomalous) Event Recognition

SAfER: Simplified Auto-encoder for (Anomalous) Event Recogni...

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international conference on distributed Computing in Sensor systems (DCOSS)

作者： Yuvin Perera Gustavo Batista Wen Hu Salil Kanhere Sanjay Jha University of New South Wales Australia

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

The IoT device marketplace has seen a large boom over the past decade. Myriad tech companies introduce IoT products to the market to capitalize on this demand. However, most IoT products have hidden security vulnerabilities, and these back-doors could be exploited by adversaries, which could lead to privacy-sensitive data leakage, unauthorized access or even physical harm to IoT device users. The event spoofing attack is one such attack where the adversary compromises one IoT device in the smart home ecosystem and sends spoofed event updates to cause undesired and unexpected outcomes of other IoT devices. Fortunately, previous research in this area has shown that it is possible to identify these attacks by analyzing the sensor readings of IoT devices to validate the status updates of other IoT devices. This paper proposes a novel Recurrent Neural Network based auto-encoder architecture that performs better than the current state-of-the-art in terms of detection accuracy, false positive rate and training time. In addition, the auto-encoder output is easily interpretable; therefore, subsequent identification of misbehaving IoT devices is more accessible for cybersecurity analysts.

关键词： Training Performance evaluation Accuracy Recurrent neural networks Smart homes computer architecture Logic gates

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An Integrated Rule-Based and Machine Learning Technique for Efficient DoS Attack Detection in WSN

An Integrated Rule-Based and Machine Learning Technique for ...

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Disruptive Technologies (ICDT), international conference on

作者： Shalini Swami Pushpa Singh Sansar Singh Chauhan Department of Computer Science GL Bajaj Institute of Technology and Management Greater Noida UP

Wireless Sensor networks (WSNs) are highly distributed, lightweight nodes, deployed in large numbers to monitor systems. Because of the nature of wireless, there are various challenges for security and threats. An effective anomaly detection system is required to find out these attacks, especially Denial of Service (DoS) attacks. These attacks are the major threat to any system connected to the internet. To identify the DoS attacks in WSN, Machine Learning (ML) algorithms helps to increase the security measures and prevent attacks; however, makes the system computationally slower. An integrated rule-based and ML technique is proposed to quickly identify the DoS attacks. Rule-based technique is used to differentiate between normal flow and attack, particularly DoS attacks. If any flow is an ‘attack’ then ML models are used to detect types of DoS attack patterns such as TDMA, Blackhole, Grayhole, and Flooding. We have used a decision tree (DT) and support vector machine (SVM) classifier with WSN-DS dataset to train the proposed model. Analysis of the classifiers shows that DT outperforms with a high accuracy to detect the types of DoS attack. The proposed method is fast and efficient, since it avoids the use of ML model if flow is ‘normal’.

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An Analysis Model of English Text Coherence Based on RST Dependency Relationship

An Analysis Model of English Text Coherence Based on RST Dep...

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Information Science, Parallel and distributed systems (ISPDS), international conference on

作者： Guimin Huang Wanying Shuai Qingkai Guo Nanxiao Deng Jianxing Lin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

Text coherence analysis is an important and challenging task that is essential for subtasks such as automatic summarisation, viewpoint extraction and machine translation in natural language processing (NLP). A large body of previous work has used linguistic features such as lexical, syntactic and entity features to capture relatively shallow coherence features, while ignoring deeper logically relevant features hidden in the text. We propose an end-to-end English text coherence analysis model (RGCM for short) that incorporates RST and graph convolutional neural networks (GCN). The intra- and inter-sentence logical relations of the input text are first mapped onto a discourse relationship tree, and then transformed into an RST-dependent context graph by certain pruning strategies. Subsequently, we propose a GCN-based text coherence assessment framework to capture intra- and inter-sentence interactions to assess text coherence. We conducted experiments on two different coherence assessment datasets and achieved accuracy rates of 95.5% and 97.8% respectively.

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Resilience of Blockchain Overlay networks 17th

Resilience of Blockchain Overlay Networks

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Network and System security - 17th international conference, NSS 2023, Proceedings

作者： Paphitis, Aristodemos Kourtellis, Nicolas Sirivianos, Michael Cyprus University of Technology Limassol Cyprus Telefonica Research Barcelona Spain

ISBN: (纸本)9783031398278

Blockchain (BC) systems are highly distributed peer-to-peer networks that offer an alternative to centralized services and promise robustness to coordinated attacks. However, the resilience and overall security of a BC system rests heavily on the structural properties of its underlying peer-to-peer overlay. Despite their success, critical design aspects, connectivity properties, and interdependencies of BC overlay networks are still poorly understood. In this work, our aim was to fill this gap by analyzing the topological resilience of seven distinct BC networks. In particular, we probed and crawled these BC networks for 28 days. We constructed, at frequent intervals, connectivity graphs for each BC network consisting of all potential connections between peers. We analyze the structural graph properties of these networks and their topological resilience. We show that by targeting fewer than 10 highly connected peers, major BCs such as Bitcoin can be partitioned into disconnected components. Finally, we uncover a hidden overlap between different BC networks, where certain peers participate in more than one BC network. Our findings have serious implications for the robustness of the overall ecosystem of the BC network. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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security and Privacy in Wireless Sensor networks Using Intrusion Detection Models to Detect DDOS and Drdos Attacks: A Survey

Security and Privacy in Wireless Sensor Networks Using Intru...

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IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Kosaraju Chaitanya Sankara Narayanan Department of Computer Science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Vignan's Nirula Institute of Technology and Science for Women Guntur Department of Computer Science and Engineering Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilndu Indi

Wireless sensor networks (WSN) playa significant role in the collection and transmission of data. The principal data collectors and broadcasters are small wireless sensor nodes. As a result of their disorganized layout, the nodes in this network are vulnerable to intrusion. Every aspect of human life includes some form of technological interaction. While the Covid-19 pandemic has been ongoing, the whole corporate and academic world has gone digital. As a direct result of digitization, there has been a rise in the frequency with which Internet-based systems are attacked and breached. The distributed Denial of Service (DDoS) and distributed Reflective Denial of Service (DRDoS) assaults are new and dangerous type of cyberattacks that can quickly bring down any service or application that relies on the Internet's infrastructure. Cybercriminals are always refining their methods of attack and evading detection by using techniques that are out of date. Traditional detection systems are not suited to identify novel DDoS attacks since the volume of data created and stored has expanded exponentially in recent years. This research provides a comprehensive overview of the relevant literature, focusing on deep learning for DDoS and DRDoS detection. Due to the expanding number of loT gadgets, distributed DDoS and DRDoS attacks are becoming more likely and more damaging. Due to their lack of generalizability, current attack detection methods cannot be used for early detection of DDoS and DRDoS, resulting in significant load or service degradation when implemented at the endpoint. In this research, a brief review is performed on the models that are used for identification of DDoS and DRDoS attacks. The working of the existing models and the limitations of the models are briefly analyzed in this research.

关键词： Wireless communication Wireless sensor networks Analytical models Computational modeling Refining Traffic control Denial-of-service attack

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