融合Inception与SE-Attention的加密流量移动业务识别

  

移动设备通常接入无线局域网,并依赖WiFi加密协议对网络中数据链路层流量进行加密,以维护通信安全.然而,现有加密流量识别方法主要针对网络层及以上的流量载荷进行分析,无法有效识别链路层加密流量的移动业务类别.针对该问题,提出了 一种在WiFi加密场景下基于链路层流量的移动业务识别方… …   相似文献

王依菁  王清贤  丁大钊  闫廷聚  曹琰 《计算机科学》2024,51(10):399-407

基于深度学习的函数名一致性检查及推荐方法

下载全文  

函数是大多数传统编程语言中聚合行为的最小命名单元,函数名的可读性对于程序员理解程序功能及不同模块之间的交互有着至关重要的作用,低质量的函数名会使开发人员感到困惑,增加代码中的坏味道,进而引发由API误用而导致的软件缺陷.为此,提出一种基于深度学习的函数名一致性检查及推荐方法,该方… …   相似文献

郑炜  唐辉  陈翔  张永杰 《软件学报》2024,35(10):4604-4622

基于频谱地图重构的辐射源识别

下载全文  

无线环境地图(REM)是呈现电磁态势的一种有效形式,考虑实际观测的不完整频谱地图受到干扰和噪声污染的问题,该文对频谱地图进行重构,并在此基础上完成辐射源识别。首先,将复杂电磁环境下的频谱地图建模为高维张量,在预处理中通过线性插值对其初始化补全。然后,使用视觉Transformer… …   相似文献

王雪刚  王方刚  王意卓 《电子与信息学报》2024,46(10):3949-3956

利用部分可信信号的导航终端欺骗干扰检测方法

下载全文  

导航信号认证服务处于初步部署阶段,认证信号对地覆盖重数无法满足独立定位授时需求,现有研究对这一阶段利用部分通过认证的信号,即可信信号,实现欺骗检测的方法关注度较低。针对这一现状,该文根据欺骗攻击原理,提出以可信信号为基准,基于可信信号伪距残差的欺骗检测方法,建立该场景下的欺骗检测… …   相似文献

王环宇  林红磊  欧钢  唐小妹 《电子与信息学报》2024,46(10):4053-4061

下载全文  

ObjectiveThe structure from motion （SfM） technique serves as the fundamental step in the sparse reconstruction process, finding extensive applications in remote sensing mapping, indoor modeling, augmented reality, and ancient architecture preservation. SfM technology retrieves camera poses from images, encompassing two main categories： incremental and global approaches. The global SfM, in contrast to the iterative nature of incremental SfM, simultaneously estimates the absolute poses of all cameras through motion averaging, resulting in relatively high efficiency. However, it still encounters challenges regarding robustness and accuracy. Rotation averaging and translation averaging constitute crucial components within the motion averaging. Compared with rotation averaging, translation averaging is more difficult due to the following three reasons： 1） Only relative translation directions could be recovered by essential matrix estimation and decomposition, i.e., the produced relative translations are scale ambiguous. 2） Only cameras in the same parallel rigid component could their absolute locations be uniquely determined by translation averaging, while for rotation averaging, the requirement simply degenerates to the connected component. 3） Compared with relative rotation, the estimation accuracy of relative translation is more vulnerable to the feature point mismatches and more likely to be outlier contaminated. In traditional approaches, the translation averaging method based on scale separation （-） calculates the relative baseline length between cameras before estimating the absolute locations, eliminates the scale ambiguity, and the solving range is no longer constrained by the camera triplet, but its robustness and accuracy still need to be improved. Incremental translation averaging （ITA） introduces the idea of incremental parameter estimation into the translation averaging process for the first time, which has good robustness and high accuracy. However, its solving process depends on camera triplets and may suffer from degeneracy during collinear camera motion. To solve the above problems, this study proposes a translation averaging method based on incremental scale estimation （-）, which eliminates the scale ambiguity and enhances the method's robustness and result accuracy.MethodIncremental SfM has been proven to be highly accurate and robust, making it a preferred choice for many applications. It has shown to be particularly effective in handling large datasets and overcoming the challenges posed by complex real-world scenarios. Recognizing its potential, researchers have sought to transfer the incremental parameter estimation ideology to other related tasks, such as incremental rotation averaging （IRA） and ITA. In particular, IRA is designed to estimate the camera absolute rotations incrementally and efficiently. Meanwhile, ITA is performed for the camera absolute locations, enabling it to handle outliers effectively and avoid error propagation. Overall, the adoption of incremental parameter estimation ideology for motion averaging tasks demonstrates the versatility and effectiveness of this approach. With its ability to handle complex datasets and overcome a range of challenges, the incremental parameter estimation ideology holds great promise for future research in the field of 3D reconstruction and beyond. In this study, - is proposed by incorporating the scale separation strategy and incremental parameter estimation ideology. Specifically, the translation averaging problem is decomposed into three sub-ones and sequentially solved： 1） incremental estimation of local absolute scale, 2） incremental estimation of global absolute scale, 3） scale-aware absolute location estimation based on optimization. The input of our proposed method is the pairwise scale invariant feature transform point matches, and its output is the absolute camera locations. First, the relative motion between cameras is obtained by estimating and decomposing the essential matrix. Next, the two-view triangulation is performed to calculate the relative depths in the local coordinate system. On the basis of depth ratios, incremental estimations are conducted for the local and global absolute scales. Subsequently, the relative baseline length between cameras is computed, and rotation averaging is performed for absolute rotation estimation, enabling the final scale-aware absolute location estimation.ResultWe performed experimental tests to evaluate the selection of scale distance function and scale distance threshold. The experimental results confirmed that the normalized perfect square deviation function effectively eliminates the impact of scaling effects. Furthermore, the incremental scale estimation method shows good robustness and insensitivity to scale distance threshold and achieves remarkably higher baseline accuracy compared with . The experiments were conducted on the 1DSfM dataset. In comparison with various state-of-the-art methods including bilinear angle-based translation averaging （BATA）, correspondence reweighted translation averaging （CReTA）, ITA, and -, our proposed method exhibited the following performance： 1） In terms of the number of cameras solved, the average percentage of successfully solved cameras using the proposed method is 96%. 2） The median error of absolute location estimation is slightly worse than that of BATA and CReTA and ranks third overall under different absolute rotations. 3） In terms of the mean error in absolute location estimation, the proposed method has remarkable advantages, ranking first and second respectively. Compared with the original -, the method in this study has a great improvement in the number of cameras solved and the accuracy of locations estimated.ConclusionThe proposed method combines the concept of scale separation with incremental parameter estimation. By integrating these two ideas, our method effectively eliminates scale ambiguity while ensuring the effectiveness of outlier rejection and maintaining a concise solving process. As a result, the obtained absolute camera locations are stable and reliable.… …   相似文献

《中国图象图形学报》2024,29(10):2992-3007

FBC模型的伪随机性和超伪随机性

下载全文  

FBC分组密码算法是入选2018年全国密码算法设计大赛第2轮的10个分组密码算法之一. FBC主要采用四分支两路Feistel结构设计,是一个实现效率高的轻量级分组密码算法.将FBC算法抽象为FBC模型,并研究该模型的伪随机性和超伪随机性,在FBC轮函数都是相互独立的随机函数的条… …   相似文献

刘楠  金晨辉  于俊伟  崔霆 《软件学报》2024,35(10):4826-4836

多模态数据融合研究综述

下载全文  

尽管深度学习强大的学习能力已经在单一模态应用领域取得了优异成果;但研究发现单一模态的特征表示很难完整包含某个现象的完整信息。为了突破在单一模态上特征表示的阻碍;更大化利用多种模态所蕴含的价值;学者们开始提出利用多模态融合的方式去提高模型学习性能。多模态融合技术是让机器在文本、语音… …   相似文献

《计算机科学与探索》2024,18(10)

基于共享近邻密度峰值聚类的过采样方法

下载全文  

不平衡数据集中存在噪声和类重叠问题时, 传统分类器性能较低, 导致少数类样本难以被准确分类. 为了提高分类性能, 提出一种基于共享近邻密度峰值聚类和集成过滤机制的不平衡数据处理方法. 该方法首先利用共享近邻密度峰值聚类算法将少数类样本自适应地分为多个簇, 然后根据子簇内密度和大小… …   相似文献

李红玲  王彪 《计算机系统应用》2024,33(10):245-254

工业控制网络多模式攻击检测及异常状态评估方法

下载全文  

近年来,工控网络发展势头迅猛. 其数字化、智能化、自动化的优势为工业带来巨大效益的同时,也面临着愈发复杂多变的攻击威胁. 在数据要素安全的背景下,及时发现和应对工控网络威胁成为一项迫切需要得到解决的任务. 通过对工控网络中的数据流进行连续监测和分析,工控网络威胁检测问题可以转化为时间序列异常检测问题. 然而现有时间序列异常检测方法受限于工控网络数据集的质量,且往往仅对单一类型异常敏感而忽略其他异常. 针对上述问题,提出了一种基于深度强化学习和数据增强的工控网络威胁检测方法（deep reinforcement learning and data augmentation based threat detection method in industrial control networks,DELTA）. 该方法提出了一种新的时序数据集数据增强选择方法,可以针对不同的基准模型选择合适的数据增强操作集以提升工控网络时间序列数据集的质量;同时使用深度强化学习算法（A2C/PPO）在不同时间点从基线模型中动态选取候选模型,以利用多种类型的异常检测模型解决单一类型异常敏感问题. 与现有时间序列异常检测模型对比的实验结果表明,在付出可接受的额外时间消耗成本下,DELTA在准确率和F1值上比所有基线模型有明显的提升,验证了方法的有效性与实用性.

… …   相似文献

王泽鹏  马超  张壮壮  吴黎兵  石小川 《计算机研究与发展》2024,58(10):2404-2416

考虑容量和成本的最大最小分散度选址问题的降阶回溯算法

下载全文  

  相似文献

《小型微型计算机系统》2024,45(10):2384-2393