Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on differe...
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Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.
Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids i...
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Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.
Environmental sound classification(ESC) is the trending research area. ESC categorizes sounds such as dog barking, gunshots, and children playing in the surroundings. Due to overlapping sound signals, the presence of ...
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Environmental sound classification(ESC) is the trending research area. ESC categorizes sounds such as dog barking, gunshots, and children playing in the surroundings. Due to overlapping sound signals, the presence of several audio sources while recording audio, and different distances from audio sources to the microphone make this problem complex. This study proposes a robust model for ESC, which can help in crime investigation systems, security warning systems, and the development of smart homes and hearing aids. Researchers have designed numerous frameworks for classifying surrounding events. Various techniques for ESC have been used in the past, but they are either computationally intensive or provide less accuracy. A hybrid model consisting of Convolutional Neural Network and Recurrent Neural Network for ESC is proposed to provide an accuracy of 99.89%, which is the highest till now, as far as we know. The model is a combination of both models;it is called CRNN. CRNN has already been used in a few past studies, but raw waveforms are used, and the accuracy attained is quite low. The publicly available Dataset UrbanSound8 K is used. Augmentation techniques are used to overcome the scarcity of datasets. The cepstral features are extracted and input to the CRNN. CRNN is encouraged due to its ability to capture spatial and temporal dependencies of environmental sound waves. Various hyperparameters, such as the number of LSTM layers, number of filters, batch size, momentum, and number of neurons in the LSTM layer, are altered to find the best value for hyperparameters for ESC. It is found that 0.5 momentum, 128 filters, 512 neurons in the LSTM layer, 256 batch size, and one LSTM layer give the highest accuracy. Another dataset, ESC- 10, is used to validate the model. It is found that the proposed model provides considerable accuracy for ESC- 10, even though it is lower than in the case of UrbanSound8 K. In the future, the model can be applied to different applications
The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study intro...
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The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.
This paper is concerned with the multi-level secure warning problem for lithium-ion battery systems subject to multi-fault scenarios. First, the long short-term memory (LSTM) network is employed to design a series of ...
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In the field of imaging, the image resolution is required to be higher. There is always a contradiction between the sensitivity and resolution of the seeker in the infrared guidance system. This work uses the rosette ...
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The Metaverse can leverage intelligent traffic management technology to simulate the Cellular Vehicle-to-Everything (C-V2X) environment, integrating closely with the Internet of Vehicles due to its advanced connectivi...
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The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporate...
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1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves ...
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1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.
Nomadic Vehicular Cloud(NVC)is envisaged in this *** predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road ...
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Nomadic Vehicular Cloud(NVC)is envisaged in this *** predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic *** specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network ***-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network *** speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration *** the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital *** infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled *** simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.
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