The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless ***,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to impl...
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The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless ***,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT *** this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT ***,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value *** addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the *** effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other *** analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
Image captioning is a technique that generates concise and meaningful descriptions of the visual contents present in an image. Image captioning frameworks generally employ an encoder-decoder-based pipeline to generate...
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Threshold signature is an important branch of the digital signature scheme,which can distribute signature rights and avoid the abuse of signature *** the continuous development of quantum computation and quantum infor...
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Threshold signature is an important branch of the digital signature scheme,which can distribute signature rights and avoid the abuse of signature *** the continuous development of quantum computation and quantum information,quantum threshold signatures are gradually becoming more ***,a quantum(t,n)threshold group signature scheme was analyzed that uses techniques such as quantum-controlled-not operation and quantum ***,this scheme cannot resist forgery attack and does not conform to the design of a threshold signature in the signing *** on the original scheme,we propose an improved quantum(t,n)threshold signature scheme using quantum(t,n)threshold secret sharing *** analysis proves that the improved scheme can resist forgery attack and collusion attack,and it is *** the same time,this scheme reduces the level of trust in the arbitrator during the signature phase.
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights o...
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We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.
The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the *** detection using machine learning(ML)with flow-based features has...
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The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the *** detection using machine learning(ML)with flow-based features has been extensively studied in the *** flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious ***,Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network *** purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and *** propose BotSward,a graph-based bot detection system that is based on *** apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the *** efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,*** is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art *** proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.
Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially cata...
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Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,tr...
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Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.
Deep neural networks have demonstrated exceptional performance across numerous applications. However, DNNs require large amounts of labeled data to avoid overfitting. Unfortunately, the labeled data may not be availab...
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For differentiating and customizing different classes of traffic and virtualizing physical resources of networks and machines, B5G/5G specifies several novel mechanisms, including VNF, SDN, Service Function Chaining, ...
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Crop yield Prediction based on environmental, soil, water, and crop parameters has been an active area of research in agriculture. Many studies have shown that these parameters can have a significant impact on crop yi...
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