Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control uni...
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ISBN:
(数字)9798350364910
ISBN:
(纸本)9798350364927
Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control units for critical car functions such as engines and brakes, experiencing rapid technological advancements. However, this swift progression in ECU technology has also made them prime targets for cyber attacks. This vulnerability has spurred researchers to focus on securing ECUs. Numerous studies have proposed intrusion detection systems (IDS) to protect against attacks on ECUs in vehicles. Yet, these IDSs are not impenetrable; attackers can exploit them by launching evasion attacks, which can trigger numerous false positive alarms. Such false alarms can be disruptive and potentially hazardous for drivers. Additionally, attackers can evade IDSs from detecting malicious data that can cause harm to the vehicle. Accordingly, in this paper, we propose a novel training framework to train a robust in-vehicle IDS that can encounter evasion attacks. Our methodology is based on implementing two rounds of mimic learning technique for training Random Forest (RF) based IDS. RF has been chosen to incorporate the randomness of the RF architecture to enhance the robustness of the model. Additionally, in each round of the two rounds of the mimic learning technique, an RF model with a different architecture is chosen to improve the resilience of the model against evasion attacks without affecting its accuracy. Our Experimental results have shown the effectiveness of our framework against evasion attacks.
Children are the future of this world. Therefore, teaching them to have a better future is very important. Also, as the adults we have to motivate them to overcome the obstacles and challenges they face throughout the...
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A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people’s daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various...
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Additive manufacturing is an innovative production approach aimed at creating products that traditional techniques cannot produce with the desired quality and requirements. Throughout the additive manufacturing proces...
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A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people’s daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various...
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ISBN:
(数字)9798350381993
ISBN:
(纸本)9798350382006
A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people’s daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various respiratory illnesses and lung disorders. The traditional diagnosis of such sounds requires specialized knowledge, which can be costly and reliant on human expertise. Recently, cough audio recordings have been used to automate the process of detecting respiratory conditions. This research aims to examine various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals. This study investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, on two ML algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and thus proposes an efficient COVID-19 detection system. The proposed system produces a practical solution and demonstrates higher state-of-the-art classification performance with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the Virufy dataset for COVID-19 detection.
Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerab...
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In today's era smart devices are integral part of our life and every device becomes smart if it is equipped with sensors and having ability to connect with internet. Without any hindrance we can connect and intera...
In today's era smart devices are integral part of our life and every device becomes smart if it is equipped with sensors and having ability to connect with internet. Without any hindrance we can connect and interact with soundings via smart devices, this is possible because of the technological advancement such as cloud computing and Internet of things. With the latest technologies we are using advance algorithms such as machine learning to make our decision more effective. The importance and need of smart devices in life have more vulnerability attacks so it needs concrete security mechanism and privacy policies to safeguard from cyber security and threats via intrusion detection. The existing IoT security literature is thoroughly analysed in this paper, outlining the many vulnerabilities and possible defences. The paper also looks into the use of machine learning techniques for traffic inflow classification, highlighting comparisons of different classifiers for problems that are special to different domains and different machine learning techniques for early identification of intrusion detection. The paper is concluded with a review of the current problems and potential future directions for IoT security research.
Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural N...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural Networks (CNNs) with the optimization capabilities of the Crow Search Algorithm (CSA). The resulting model, called Crow Search Optimized CNN (CSO-CNN), harnesses the feature extraction and classification strengths of CNNs, while the CSA is used to optimize the hyperparameters of the CNN architecture for optimal performance. The model is trained and validated on a comprehensive dataset that includes satellite imagery and meteorological data from a wide range of cyclone events. The CSO-CNN achieves remarkable classification accuracy, surpassing traditional CNN models and other leading techniques. Its ability to quickly and accurately assess the likelihood of cyclone formation provides crucial decision support for disaster management authorities, enabling timely and effective response strategies. By integrating the CSA optimization technique with the CNN framework, this study presents a novel approach to cyclone prediction. It highlights the potential of combining advanced Machine Learning (ML) algorithms to tackle complex environmental challenges and deepen our understanding of intricate atmospheric phenomena.
Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output ...
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The remaining risk of safety-instrumented systems is an important non-functional requirement that is regulated by international standards. Several ways towards computing the safety as a function of its relevant design...
The remaining risk of safety-instrumented systems is an important non-functional requirement that is regulated by international standards. Several ways towards computing the safety as a function of its relevant design parameters have been studied in the literature. However, the standard approach only covers two special cases of high or low demand, which simplify the treatment by either ignoring the effects of demand rate or test interval on the safety. More detailed treatments in the literature derive Markov models, which can be numerically analyzed, or approximate solutions using Taylor series expansions etc. This paper introduces closed-form exact formulas for the average probability of failure on demand (PFD) and the resulting hazardous event frequency (HEF, or accident rate), taking into account demand rate and test interval. It integrates all cases of low, high and medium demand in one formula. The derivation is based on an analysis of the cyclostationary semi-Markov stochastic process of the safety-integrated system and its symbolic transient analysis over the test interval.
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