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Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives 14th

Addressing Adversarial Machine Learning Attacks in Smart Hea...

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14th international conference on Sensing Technology, ICST 2022

作者： Selvakkumar, Arawinkumaar Pal, Shantanu Jadidi, Zahra School of Computer Science Queensland University of Technology BrisbaneQLD4000 Australia School of Information and Communication Technology Griffith University Gold Coast CampusQLD4222 Australia

ISBN: (纸本)9783030988852

Smart healthcare systems are gaining popularity with the rapid development of intelligent sensors, the Internet of things (IoT) applications and services, and wireless communications. However, at the same time, several vulnerabilities and adversarial attacks make it challenging for a safe and secure smart healthcare system from a security point of view. Machine learning has been used widely to develop suitable models to predict and mitigate attacks. Still, the attacks could trick the machine learning models and misclassify outputs generated by the model. As a result, it leads to incorrect decisions, for example, false disease detection and wrong treatment plans for patients. In this paper, we address the type of adversarial attacks and their impact on smart healthcare systems. We propose a model to examine how adversarial attacks impact machine learning classifiers. To test the model, we use a medical image dataset. Our model can classify medical images with high accuracy. We then attacked the model with a Fast Gradient Method Sign attack (FGSM) to cause the model to predict the images and misclassify them inaccurately. Using transfer learning, we train a VGG-19 model with the medical dataset and later implement the FGSM to the Convolutional Neural Network (CNN) to examine the significant impact it causes on the performance and accuracy of the machine learning model. Our results demonstrate that the adversarial attack misclassifies the images, causing the model’s accuracy rate to drop from 88 to 11%. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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IoT-Based Real-Time Water Quality Monitoring System Using a RC Boat 7th

IoT-Based Real-Time Water Quality Monitoring System Using a ...

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7th international conference on Internet of things and Connected Technologies, ICIoTCT 2022

作者： Asari, Utkarsh Desai, Raj Parekh, Rutu Meena, Udit Dhirubhai Ambani Institue of Information & Communication Technology Gujarat Gandhinagar382007 India

ISBN: (纸本)9789811997181

the research evaluates the development of a battery-powered remote control (RC) boat for monitoring water quality. In recent years, RC boats have evolved beyond the status of a toy or an instrument used exclusively for recreation. Various studies into the uses of RC boats have been conducted in recent years, and their performance has been an increasing worry for society. Leveraging this, we have assembled and mounted various sensors on the RC boat that monitor water quality and process the data in real-time using the "Waterlytical" app to represent the water quality. A 2200 mAh battery powers the RC airboat. the RC airboat is controlled with the help of the NRF24L01 module;this module comes with a 2.4G antenna (2dB) with a 1 MB transmission rate in the open air. the water quality monitoring sensors used in this boat are the pH sensor which measures values between 0 and 14;the TDS sensor, which measures values between 0 and 1000 ppm;and the turbidity sensor, which measures values between 0 and 3000 NTU. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Remote control

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Filtering over networks with random communication 24

Filtering over networks with random communication

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24th ACM international conference on Hybrid systems Computation and Control, HSCC 2021, held as part of the 14th Cyber Physical systems and Internet-of-things Week, CPS-IoT Week 2021

作者： Tanwani, Aneel Laas - Cnrs 7 Avenue du Colonel Roche Toulouse31400 France

ISBN: (纸本)9781450383394

We consider analysis and design of distributed filters for continuous-time stochastic systems, where the partial information about the states is measured by a distributed set of sensor units. these units are represented by nodes in an undirected and connected graph, whose edges represent the communication links between sensor units. It is stipulated that the communication between sensor nodes is time-sampled randomly and the sampling process is described by a Poisson counter. Our proposed filtering algorithm for each sensor node is a stochastic hybrid system: It comprises a continuous-time differential equation, and at random time instants when communication takes place, each sensor node updates its state estimate based on the information received by its neighbors. For this setup, we compute the expectation of the error covariance matrix for each node which is governed by a matrix differential equation, and relate its convergence with the mean sampling rate. © 2021 Owner/Author.

关键词： Sensor nodes

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14th international conference on Complex, Intelligent and Software Intensive systems, CISIS 2020, held in conjunction with the 14th international conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2020

14th International Conference on Complex, Intelligent and So...

引用

14th international conference on Complex, Intelligent and Software Intensive systems, CISIS 2020, held in conjunction with the 14th international conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2020

ISBN: (纸本)9783030504533

the proceedings contain 61 papers. the special focus in this conference is on Complex, Intelligent and Software Intensive systems. the topics include: In-advance replica arrangement of shared data over hybrid peer-to-peer network according to users’ locations and preferences;a method of data augmentation for shunt murmur angiostenosis detection;study on visualization of characteristics of local government by open data and synesthesia in cross-cutting and systematic regional community networks;an event response fuzzy-based system for actor node selection in wsans;development of training system for dental treatment using webar and leap motion controller;impact of the interactive e-learning instructions on effectiveness of a programming course;an admission control system for 5g wireless networks considering fuzzy logic and software-defined network approaches;a fuzzy-based adiantum cultivation support system design;deploy, connect and execute scientific models;software-oriented routing protocol for energy-efficient wireless communications;control theoretical modeling of trust-based decision making in food-energy-water management;a prototype system of social experiment for actual road state information platform based on sensors and v2x communication;an autonomous wireless platform for the remote inspection of pollutants;quadcoins-network: a deep learning approach to sound source localization;green fog: cost efficient real time power management service for green community;extractive summarization by rouge score regression based on bert;a development framework for rp-type serious games in a 3d virtual environment;sound source separation based on multichannel non-negative matrix factorization with weighted averaging;intelligentbox based training system for operation of radiation therapy devices;android malware detection using multi-stage classification models.

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Performance Analysis of High-Efficiency WPT for communication Technologies

Performance Analysis of High-Efficiency WPT for Communicatio...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Reem Emad Nafiaa A.Z. Yonis Electronic Engineering department College of Electronics Engineering Ninevah University Mosul Iraq

ISBN: (纸本)9781665487726

Wireless Power Transfer (WPT) is a technology which is become an important topic nowadays due to many advantages that have, from the ease of use, safe, reliability, no need wires, and so on, and many scientists are trying to develop this technology to be used for more new smartphone devices, also WPT is considered one of green technology. In this research paper, a wireless power transfer system for the mobile battery charger had been designed and discussed using Mat lab program to get a 10 Watt to charge a mobile device with acceptable distance and efficiency. there are three methods for WPT includes electromagnetic induction (EI), magnetic resonance coupling (MRC), and radio waves (RW) which are categorized depending on the distance that sends the power. Magnetic resonance coupling is the method that has been designed is used for short and medium distances. In the result, the effect of distance system performance has been discussed.

关键词： Couplings System performance Wires Magnetic resonance Wireless power transfer Performance analysis Reliability

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Blockchain-based Cyber-Physical systems Security Autonomous Routing Scheme 14

Blockchain-based Cyber-Physical Systems Security Autonomous ...

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IEEE/ACM 14th international conference on Utility and Cloud Computing

作者： Zhang, Yasheng Li, Chengcheng Wang, Chao Zhang, Peiying CETC Res Inst 54 Shijiazhuang Hebei Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao Shandong Peoples R China

ISBN: (纸本)9781450391634

Improving the quality of service (QoS) and quality of experience (QoE) for users of cyber-physical systems (CPS) is the key to the widespread promotion and deployment of this technology. Network latency is an important factor affecting users' network experience. Due to the limited processing capacity of a single device node of CPS, the communication relationship between equipment is extremely complicated, and multi-hop device nodes may be passed through in the middle, so designing an efficient routing scheme for CPS is a serious challenge. CPS routing also faces a security crisis. System nodes may send viruses or erroneous results to user equipment, and user equipment may also refuse to pay for routing services to system nodes. In order to solve the above problems, this paper proposes a secure routing scheme based on blockchain. Blockchain is used to ensure secure transactions between system nodes and user equipment. At the same time, deep reinforcement learning (DRL) technology is used, and two deep neural networks are used to train intelligent agent to achieve low-latency adaptive traffic scheduling. Experimental results show that the proposed autonomous routing technology can reduce the system communication latency by about 31.7%-45.4%.

关键词： Cyber-Physical systems Network Latency Security Routing Deep Reinforcement Learning

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Investigating the Generalization Capabilities of Deep FAVIB: A Data-Driven Information Bottleneck-Based Quantization Scheme for Noisy Channels

Investigating the Generalization Capabilities of Deep FAVIB:...

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international ITG conference on Source and Channel Coding (SCC)

作者： Matthias Hummert Shayan Hassanpour Dirk Wübben Armin Dekorsy Department of Communications Engineering University of Bremen Bremen Germany

ISBN: (数字)9798331522896

ISBN: (纸本)9798331522902

We consider a generic two-hop transmission setup. Explicitly, a source signal is transmitted over an imperfect channel, yielding a noisy observation. this signal shall then be compressed at a relay node before getting transmitted further over an error-prone and rate-limited channel to the sink, where the source signal is decoded/reconstructed. In [1], [2], we presented a data-driven Information Bottleneck-based quantization scheme called Deep FAVIB. For that, we derived a tractable variational lower-bound of the original objective functional that could be optimized using samples and utilized Deep Neural networks (DNNs) to realize both the quantizer/encoder at the relay node and the decoder at the sink. Based on this work, we now provide further investigations, showcasing the excellent generalization capabilities of Deep FAVIB by several Symbol-Error-Rate (SER) simulation results. Specifically, we apply the pretrained Deep FAVIB to different environments that have not been present in the training, and show that yet, it yields promising results. this gives clear evidence to the fact that Deep FAVIB can be considered as a practically efficient scheme to be utilized, especially when dealing with the highly dynamic and challenging environments.

关键词： Training Deep learning 6G mobile communication Quantization (signal) Simulation Artificial neural networks Encoding Decoding Noise measurement Relays

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Malicious PDF detection Based on Machine Learning with Enhanced Feature Set

Malicious PDF detection Based on Machine Learning with Enhan...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Suleiman Y. Yerima Abul Bashar Ghazanfar Latif Cyber Technology Institute De Montfort University Leicester United Kingdom Department of Computer Engineering Prince Mohammad Bin Fahd University Al-Khobar Kingdom of Saudi Arabia

ISBN: (纸本)9781665487726

PDF is one of the most popular document file formats due to its flexibility, platform independence and ability to embed different types of content. Over the years, PDF has become a popular attack vector for spreading malware and compromising computer systems. Existing signature-based defense systems have extremely high recall rates, but quickly become obsolete and ineffective against zero-day attacks, which makes them easy to circumvent by malicious PDF files. Recently, Machine Learning (ML) has emerged as a viable tool to improve discovery of previously unseen attacks. Hence, in this paper we present enhanced ML-based models for the detection of malicious PDF documents. We develop an approach for ML-based detection with static features derived from PDF documents leveraging existing tools and propose new, previously unused features to enhance the performance of the ML-based classifiers. Our investigative study is conducted on the recently published Evasive-PDFMal2022 dataset, which was used to evaluate seven ML classifiers based on our proposed method. the EvasivePDFMal2022 dataset consists of 4,468 benign samples and 5,557 malicious PDF samples. the results of the experiments show that our proposed approach with the enhanced features enabled improved accuracies in five out of seven of the classifiers that were evaluated. the results demonstrate the potential of the new features to increase the robustness of feature-based PDF malware detection.

关键词： Machine learning Portable document format Feature extraction Malware Robustness communication networks Computational intelligence

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Detecting Toxic Comments Using Convolutional Neural Network Approach

Detecting Toxic Comments Using Convolutional Neural Network ...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Varun Mishra Monika Tripathi Dept. of Computer Science & Engineering Shri Krishna University Chhatarpur Madhya Pradesh INDIA

ISBN: (纸本)9781665487726

In the most significant issue now plaguing social networking platforms and online communities is toxicity identification. therefore, it is necessary to create an automatic hazardous identification system to block and restrict individual from certain online environments. We introduce multichannel Convolutional Neural Network (CNN) approach in this paper for the detection of toxic comments in a multi-label context. With the help of pre-trained word embeddings, the suggested model produces word vectors. Also, to model input words with long-term dependency, this hybrid model extracts local characteristics using a variety of filters and kernel sizes. then, to forecast multi-label categories, we integrate numerous channels with three layers as fully linked, normalization, and an output layer. the results of the experiments show that the suggested model performs where we are presenting the fresh modeling CNN approach to detect the toxicity of textual content present on the social media platforms and we categorized the toxicity into positive and negative impact on our society.

关键词： Toxicology Social networking (online) Computational modeling Convolutional neural networks communication networks Kernel Computational intelligence

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Overcoming Dataset Imbalances and Computational Challenges in IoT Intrusion Detection: A SMOTE-Enhanced Transformer-Based Model

Overcoming Dataset Imbalances and Computational Challenges i...

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international conference on communication systems and Network Technologies (CSNT)

作者： S. Venkatasubramanian S. K. B. Pradeep Kumar Ch Bairysetti Prasad Babu Computer Science and Business Systems Saranathan College of Engineering Trichy India Electirical and Electronics Engineering Ramachandra College of Engineering Eluru India Computer Science and Engineering Ramachandra College of Engineering Eluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Due to security holes in IoT-enabled networks, the massive adoption of IoT has unleashed a new wave of threats into computer networks. Due to their limited resources, these devices are ill-equipped to handle security measures, making them the most vulnerable part of our computer networks and putting our data and systems at risk. Some have suggested Intrusion Detection systems (IDS) as a means to lessen the impact of intrusions connected to the IoT in an effort to solve this problem. Although IDS are essential for detecting threats, they are not widely used because of their high computing costs and reliance on labelled data. Finding a happy medium between fast detection and excellent accuracy is the objective of this model development effort. In order to accomplish this, to present a feature selection method based on the black-winged kite algorithm (BKA), a groundbreaking swarm intelligence technique that combines the Leader tactic and the Cauchy variation procedure to find the expansive appropriate convergence solution. BKA is based on the migratory and predatory instincts of black-winged kites. the study used few-shot learning techniques by leveraging transfer learning models and specifically using pre-trained architectures for categorisation. It also fine-tuned four pre-trained transformers. To take into account the imbalance in these datasets as a significant component and use the Synthetic Minority Oversampling Technique (SMOTE) to address it. the research team used BoT-IoT, TON-IoT, and CIC-DDoS2019 as benchmark datasets to assess the impact of the chosen features on the suggested model's performance. Metrics for evaluation include F1-score, recall, precision, and accuracy of detection. Evidence from experiments shows that the suggested ensemble model outperforms state-of-the-art AIDS topologies for IoT networks in terms of detection accuracy and efficiency.

关键词： Measurement Accuracy Computational modeling Intrusion detection Feature extraction Transformers Prediction algorithms Internet of things Security Few shot learning

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