Intrusion detection system (IDS) can identify abnormal network traffic and attacks, which is an important means of network security defense. However, some intrusion data are often disguised as normal data for transmis...
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Intrusion detection system (IDS) can identify abnormal network traffic and attacks, which is an important means of network security defense. However, some intrusion data are often disguised as normal data for transmission, which increases the difficulty of intrusion data classification. In addition, the existing packet-based or flow-based data feature extraction methods result in low feature dimensions, causing the problem of class overlapping between different categories with the same features. To clarify, overlapping samples are those that overlap between erroneous samples and correct samples. Nonoverlapping samples are those in the test set that do not match the characteristics of the already identified overlapping samples and are therefore considered nonoverlapping samples. Therefore, the detection effect of some attacks with high concealment is poor. In order to solve the above problems, this paper proposes a multistage intrusion detection method: an existing intrusion detection model with higher classification performance (OBLR) is used to predict the data in the first stage. In the second stage, for the overlapping data in the confusing data, the method learns the distribution of each feature group according to the randomly divided "intermediary set," and realizes the prediction of overlapping samples through the prior distribution knowledge, and achieves efficient classification of overlapping samples without increasing the computational burden of the model. For nonoverlapping data in the confusing data, KPCA (kernel principal component analysis) dimension elevation is used in the third stage to capture more detailed difference information between samples, and GMM (Gaussian mixed model) is combined with the "representative samples" proposed in this paper to assist classifier classification. At the same time, all the base classifiers are integrated through LTR (learning to rank) to improve the classification effect of the model for nonoverlapping data in the
Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation...
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This paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. The framework allows to visualize and analyze the provider results based on the...
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College of computerscience Beijing University of Technology, Beijing 100124, China, 1374622525@*** This paper proposes a trust collaboration technology for edge computing, addressing trust isolation and security issu...
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As edge computing becomes an increasingly important computing model, trust management and security issues are becoming more severe. Problems such as malicious node attacks and trust isolation pose threats to the secur...
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Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed und...
Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed...
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The Internet of Things (IoT) is transforming industries by enhancing productivity and efficiency;however, energy availability remains a significant challenge due to the limited capacity of batteries and supercapacitor...
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The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-Time computing on an e...
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ISBN:
(纸本)9798331531881
The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-Time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-The-Art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-To-end computation time for
作者：
Tarbă, NicolaeIrimescu, Ionela N.Pleavă, Ana M.Scarlat, Eugen N.Mihăilescu, MonaDoctoral School
Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School
National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center
National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept
National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept
Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania
We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by ...
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