Machine learning techniques have been have proven to be more effective than conventional extensively used in the creation of intrusion detection systems (IDS) that can swiftly and automatically identify and classify c...
Machine learning techniques have been have proven to be more effective than conventional extensively used in the creation of intrusion detection systems (IDS) that can swiftly and automatically identify and classify cyber attacks at the host-and network-levels. A scalable solution is needed since destructive attacks are happening so quickly and are changing all the time. For more investigation, the malware community has access to a number of malware databases. the performance of several machine learning algorithms on a range of datasets that were made available to the general public, however, has not yet been thoroughly evaluated by any study. the publicly available malware datasets should be regularly updated and benchmarked due to the dynamic nature of malware and the continuously evolving attacking techniques. In this study, a deep neural network (DNN), a type of deep learning model, is examined in order to create a flexible and efficient IDS to identify and categorise unexpected and unanticipated cyber threats. In order to analyse a variety of datasets that have been produced throughout time using both static and dynamic methodologies, it is vital to take into account the rapid increase in attacks and the constant evolution of network behaviour. It is simpler to select the most effective algorithm for accurately predicting forthcoming cyber attacks withthe help of this type of research. Many publicly available benchmark malware datasets are used to offer a thorough review of DNN and other conventional machine learning classifier studies. the KDDCup 99 dataset and the accompanying hyper parameter selection techniques are used to choose the ideal network parameters and topologies for DNNs. A learning rate of [0.01-0.5] is applied to every 1,000-epoch DNN experiment. A variety of datasets, including NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, as well as the DNN model that performed well on KDDCup 99 are used to conduct the benchmark. Our DNN model trains a
Acoustic signals are widespread applied in fault diagnosis. However, it is still a difficult problem to extract feature of acoustic signals under large rotation speed fluctuation. Sparse filtering (SF) as an effective...
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ISBN:
(数字)9781728151816
ISBN:
(纸本)9781728151823
Acoustic signals are widespread applied in fault diagnosis. However, it is still a difficult problem to extract feature of acoustic signals under large rotation speed fluctuation. Sparse filtering (SF) as an effective unsupervised algorithm, has been skillfully applied in many fields. However, the effect of SF on acoustic signal processing under large rotation speed fluctuation is not ideal. Hence, we proposed an effective feature learning algorithm called deep sparse filtering (DSF) to overcome this teaser. Firstly, frequency domain signals are used as the basis of DSF for feature leaning, then weight adjustment is performed by back propagation (BP). the efficiency of the DSF model is verified by the collected bearing data set.
the paper employs Web log mining technology through the analysis of student learning behavior,to implement data collection of students learning *** with network learning evaluation system,a set of online intelligence ...
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the paper employs Web log mining technology through the analysis of student learning behavior,to implement data collection of students learning *** with network learning evaluation system,a set of online intelligence evaluation index system is constructed,furthermore,calculating the weight of evaluation index by the square root method to establish evaluation *** the end,it is applied in network courses to optimize the system model.
In recent years, the advancement of artificial intelligence algorithms, particularly in the domain of semantic segmentation within computer vision, has been noteworthy. this progress is attributed to the availability ...
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ISBN:
(数字)9798350385670
ISBN:
(纸本)9798350385687
In recent years, the advancement of artificial intelligence algorithms, particularly in the domain of semantic segmentation within computer vision, has been noteworthy. this progress is attributed to the availability of diverse large-scale datasets and the continual improvement of computational resources, with deep learning serving as a representative paradigm. Consequently, numerous sophisticated segmentation models have emerged for visual tasks. However, the efficacy of these models hinges on the availability of substantial annotated datasets for training. A notable challenge arises in scenarios where few-shot datasets are prevalent due to inherent limitations. the sparse data volume in these instances often stems from difficulties in obtaining a sufficiently large and high-quality dataset. the process of image annotation is resource-intensive, demanding considerable time and human effort. Addressing the consequential shortage of sample size in few-shot datasets becomes a paramount concern. this paper tackles the aforementioned issues related to few-shot datasets and introduces the Hilbert encoded cooperative coevolutionary genetic algorithm (HCCGA), a novel image enhancement algorithm designed for the automated generation of 3D-Lookup Tables (3D LUTs). HCCGA leverages 3D LUTs generated automatically through the genetic algorithm to map the original few-shot dataset, thereby obtaining a more extensive set of high-quality samples. the methodology employed in this research encompasses the generation and optimization of 3D LUTs using a divide-and-conquer strategy based on genetic algorithms. Additionally, it involves the mapping of new images using 3D LUTs in conjunction with sampling and interpolation. the evaluation of the proposed approach encompasses comparisons based on various image binary classification evaluation metrics applied to images before and after mapping through the results of model inference. Experimental results obtained using HCCGA on diverse publ
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