浅析网络异常流量分析检测研究与实现

伴随着互联网技术与网络业务的快速发展,网络规模逐渐扩大,网络运用开始逐步朝多元化、多样化以及复杂化的方向发展.现今,网络流量监测已经逐渐发展为计算机网络运用当中一个必不可少的内容与环节.文章将对网络异常流量加以说明,并对网络异常流量检测技术研究与实现进行分析与研究.     

一种分布式拒绝服务攻击的检测方法

该文提出和实现了一种基于网络异常流量特征的检测模型。通过将多条链路或多个流的流量信号作为一个整体进行研究,构建了网络异常流量监控的系统模型。该模型包括了数据采集、分析、异常判断和警告等功能,并综合了异常信息融合及警告信息关联性分析技术。通过实验证明,该套系统模型对短时间段内的突发流量能提供有效的检测和报警服务。     

结合先天和适应性免疫的蠕虫检测免疫模型

现有的蠕虫检测方法大多通过关闭不安全的端口,切断感染主机与未感染主机之间通信等方法延缓蠕虫传播而达到将损害减少到最低程度的目的.实际上在实施这些方法时往往有许多障碍需要克服,其中的最大障碍就是存在错误检测率高的问题.现将免疫危险理论中的DCs(树突状细胞,Dendritic Cells)-T细胞协同机制用于蠕虫检测,其中DCs属于先天免疫系统细胞,T细胞属于适应性免疫系统细胞.本模型将蠕虫进程触发的系统调用序列当作抗原,将感染蠕虫导致的主机和网络异常当作危险信号.在该模型中,DCs负责危险信号的收集检测并提呈与该危险信号关联的抗原给T细胞检测器进行抗原结构检测.理论分析说明,这样的双重检测方法可以降低伪肯定率和伪否定率,并且记忆T细胞检测器的采用能使系统对类似蠕虫的再次感染反应更加迅速.     

基于决策树及遗传算法的人工免疫入侵检测算法

针对传统的否定选择算法中生成大量无效抗体,并且抗体之间缺乏多样性的问题,设计了基于决策树及遗传算法的人工免疫入侵检测算法。将决策树和遗传算法引入传统的否定选择算法中,利用决策树计算抗原和抗体之间的亲和力,提出了新fitness的计算公式,并利用抗体浓度衡量抗体集的多样性,将低浓度抗体代替高浓度抗体,实现了抗体的多样性,确保了当抗体集的数量一定时尽可能覆盖最大的非自体集空间,以提高抗体集的性能。     

Ship detection based on multi-scale weighted fusion

Ship surveillance plays an important role in ensuring the safety of maritime transportation and navigation. Due to the influence of factors such as waves and special weather, the existing detection methods still cannot balance the accuracy, speed and the parameters of the model in the changeable and complex marine environment. To solve this problem, this paper proposes an improved real-time method based on YOLOv5, which has few parameters and achieves high detection accuracy with little memory and computation cost. Collaborative Attention (CA) mechanism is added to the network structure, which enables the model to more accurately locate and identify target regions. We also design a Spatial Pyramid Pooling module (SPP) and a weighted pyramid network called Bidirectional Feature Pyramid Network (BiFPN) based on the characteristics of the ships to better fuse feature information. Transformer encoder is introduced to capture long-distance dependencies and preserve global and local features to the greatest extent. Furthermore, the ability of our proposed structure to localize objects at each stage is improved through integrating the output of multiple modules. The experimental results show that, the comprehensive performance of this method is better than the existing technology in ship detection on different evaluation criteria.     

地铁站场景下的可行驶区域检测

在室内环境下的机器人视觉导航任务中,可行驶区域检测是不可或缺的一部分,这是保证自动驾驶任务实现的基础.目前较多的解决方法是对数据集中出现过的障碍物进行识别来检测可行驶区域,缺乏灵活性,因此本文提出了一种针对地铁站等室内平坦地面的可行驶区域检测方法,提高实用性.本文采用经典的MobileNetV3网络对采集到的前方图像进行分类,判断是否为地面区域.由于室内地面的地标、箭头等贴纸的影响,因此需要对非地面区域进一步判断,与常规的立体障碍物进行区分.本文利用连续帧之间的特征点匹配获得相机移动距离,并利用直线拟合计算斜率的方法达到区分立体障碍物与平面地标的目的.实验表明,本文提出的方法能较好地检测机器人前方可行驶区域,具有较高的实用价值.     

Transfer Learning on Deep Neural Networks to Detect Pornography

While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts both pornographic and non-pornographic contents. The videos were from different ethnicities and skin color which ensures the models can detect any kind of video. A VGG16, Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49, 0.49 and 0.78 respectively. Finally, utilizing transfer learning, a convolutional neural network was designed and yielded an accuracy of 0.98.     

基于多流网络一致性的视频显著性检测

现有的视频显著性检测算法通常采用双流结构提取视频的时空线索,其中运动信息作为双流结构的一个分支,在显著物体发生剧烈或慢速移动时存在运动估计准确率低的问题,并且不合理的训练数据或方案使得权重偏向单个分支结构。提出一种基于多流网络一致性的视频显著性检测算法MSNC。设计并使用一种新的三重网络结构提取预选目标区域的颜色信息、时序信息和先验特征,通过先验特征补偿运动流的缺陷,并提高运动线索的利用率。采用多流一致性融合模型优化三流分支,得到不同特征的最佳融合方案。同时通过循环训练策略平衡三重网络的权重,以避免网络过度拟合单流分支,从而有效地提高运动估计和定位的准确率。在Davis数据集上的实验结果表明,相比PCSA、SSAV、MGA等算法,该算法的鲁棒性更优,其maxF和S-Measure值分别达到0.893和0.912,MAE仅为0.021。     

Edge detection insensitive to changes of illumination in the image

In this paper we present new edge detection algorithms which are motivated by recent developments on edge-adapted reconstruction techniques [F. Aràndiga, A. Cohen, R. Donat, N. Dyn, B. Matei, Approximation of piecewise smooth functions and images by edge-adapted (ENO-EA) nonlinear multiresolution techniques, Appl. Comput. Harmon. Anal. 24 (2) (2008) 225–250]. They are based on comparing local quantities rather than on filtering and thresholding. This comparison process is invariant under certain transformations that model light changes in the image, hence we obtain edge detection algorithms which are insensitive to changes in illumination.