一种轻量级的SYN Flooding攻击检测方法

提出了一种轻量级的源端DDoS攻击检测的有效方法.本方法基于Bloom Filter技术对数据包信息进行提取,然后使用变化点计算方法进行异常检测,不仅能够检测出SYN Flooding攻击的存在,而且能够避免因为正常拥塞引起的误报.重放DARPA数据实验表明,算法的检测结果与类似方法相比更精确,使用的计算资源很少.     

一种新型实时数据模型——钟面模型

动态的实时数据总是包含对应的时间信息,现有实时数据库技术没有充分考虑时间信息的建模问题;另一方面,大型实时数据库中数据存取效率的瓶颈在于磁盘的机械运动.提出了一种新型实时数据模型--钟面模型.建立了钟面模型与实时数据的时间属性之间的逻辑对应关系,分析了钟面模型与磁盘存储介质的圆柱状结构之间的逻辑对应关系,以及根据数据在逻辑钟面的偏移位置,确定数据在物理磁盘上的偏移位置的计算方法.初步研究结果表明基于钟面模型的实时数据模型及其对应的数据存取技术,可以减少磁盘的机械运动,缩短定位操作所消耗的时间,显著提高数据处理效率.     

基于代码属性图及注意力双向LSTM的漏洞挖掘方法

随着信息安全愈发严峻的趋势,软件漏洞已成为计算机安全的主要威胁之一.如何准确地挖掘程序中存在的漏洞,是信息安全领域的关键问题.然而,现有的静态漏洞挖掘方法在挖掘漏洞特征不明显的漏洞时准确率明显下降.一方面,基于规则的方法通过在目标源程序中匹配专家预先定义的漏洞模式挖掘漏洞,其预定义的漏洞模式较为刻板单一,无法覆盖到细节特征,导致其存在准确率低、误报率高等问题;另一方面,基于学习的方法无法充分地对程序源代码的特征信息进行建模,并且无法有效地捕捉关键特征信息,导致其在面对漏洞特征不明显的漏洞时,无法准确地进行挖掘.针对上述问题,提出了一种基于代码属性图及注意力双向LSTM的源码级漏洞挖掘方法.该方法首先将程序源代码转换为包含语义特征信息的代码属性图,并对其进行切片以剔除与敏感操作无关的冗余信息;其次,使用编码算法将代码属性图编码为特征张量;然后,利用大规模特征数据集训练基于双向LSTM和注意力机制的神经网络;最后,使用训练完毕的神经网络实现对目标程序中的漏洞进行挖掘.实验结果显示,在SARD缓冲区错误数据集、SARD资源管理错误数据集及它们两个C语言程序构成的子集上,该方法的F1分数分别达到了82.8%,77.4%,82.5%和78.0%,与基于规则的静态挖掘工具Flawfinder和RATS以及基于学习的程序分析模型TBCNN相比,有显著的提高.     

Open-set face recognition across look-alike faces in real-world scenarios

The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face recognition systems in real-world scenarios focus on handling pose, expression and illumination problems on face recognition. In addition to these challenges, when the number of subjects is increased for face recognition, these problems are intensified by look-alike faces for which there are two subjects with lower intra-class variations. In such challenges, the inter-class similarity is higher than the intra-class variation for these two subjects. In fact, these look-alike faces can be created as intrinsic, situation-based and also by facial plastic surgery. This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation. Since some real-world databases for face recognition do not have multiple images per person in the gallery, with just one image per subject in the gallery, this paper proposes a novel idea to overcome this challenge by 3D modeling from gallery images and synthesizing them for generating several images. Accordingly, a 3D model is initially reconstructed from frontal face images in a real-world gallery. Then, each 3D reconstructed face in the gallery is synthesized to several possible views and a sparse dictionary is generated based on the synthesized face image for each person. Also, a likeness dictionary is defined and its optimization problem is solved by the proposed method. Finally, the face recognition is performed for open-set face recognition using three proposed representation classifications. Promising results are achieved for face recognition across plastic surgery and look-alike faces on three databases including the plastic surgery face, look-alike face and LFW databases compared to several state-of-the-art methods. Also, several real-world and open-set scenarios are performed to evaluate the proposed method on these databases in real-world scenarios.     

Robust pedestrian detection under deformation using simple boosted features

Many existing methods for pedestrian detection have the limited detection performance in case of deformation such as large appearance variations. To overcome this limitation, we propose a novel pedestrian detection method that uses two low-level boosted features to detect pedestrians despite the presence of deformations. One is a boosted max feature (BMF) that uses a max operation to aggregate a selected pair of features to make them invariant to deformation. Another is a boosted difference feature (BDF) that uses a difference operation between a selected pair of features to improve localization accuracy of pedestrian detection. We incorporate a spatial pyramid pool method that uses multiple sized blocks to increase the richness of boosted features in a local region and use a RealBoost method to train a tree-structured classifier for the proposed pedestrian detection method. We also apply a region-of-interest method to the detected results to remove false positives effectively. Our proposed detector achieved log-average miss rates of 19.95%, 10.39%, 36.12%, and 39.57% on the Caltech-USA, INRIA, ETH, and TUD-Brussels dataset, respectively, which are the lowest among those of all state-of-the-art pedestrian detectors.     

常用循环摘要的自动生成方法及其应用

采用形式化方法证明软件的正确性是保障软件可靠性的有效方法,而对循环语句的分析与验证是形式化证明中的关键,对循环语句的处理一直是程序分析与验证中的一个难点问题.本文提出使用循环语句修改的内存和这些内存中存放的新值来描述循环语句的执行效果,并将该执行效果定义为循环摘要.同时,本文提出了一种自动生成循环摘要的方法,可以为操作常用数据结构的循环自动生成循环摘要,包含嵌套循环.此外,基于循环摘要,我们可以自动生成循环语句的规约,包括循环不变式、循环的前置条件以及循环的后置条件.我们已经实现了自动生成循环摘要以及循环规约的方法,并将它们集成到验证工具Accumulator中,实验表明,我们的方法可以有效地生成循环摘要,并生成多种类型的规约,从而辅助软件程序的形式化证明,提高验证的自动化程度和效率,减轻验证人员的负担.     

支持透明集成的数据缓存机制

数据缓存是提升动态Web应用性能的重要手段,传统数据缓存方案主要进行数据查询缓存,通常需要开发人员对应用程序进行大量修改并负责缓存失效维护,导致缓存方案的部署成本和维护成本过高．提出一种新的基于键值存储结构的数据缓存机制EasyCache ,支持缓存数据的自动加载,兼容常用的标准数据访问接口及SQL语法,并提出一种基于规则的优化模型及数据一致性保障策略,开发人员无需修改应用程序即可完成EasyCache的集成．T PC‐W基准测试结果表明系统性能得到显著提升,在单表数据规模增加以及并发用户规模增加等情况下,系统响应速度可提高10倍,吞吐率提高近1倍．     

An intelligent water drops-based workflow scheduling for IaaS cloud

Cloud computing is an emerging technology in a distributed environment with a collection of large-scale heterogeneous systems. One of the challenging issues in the cloud data center is to select the minimum number of virtual machine (VM) instances to execute the tasks of a workflow within a time limit. The objectives of such a strategy are to minimize the total execution time of a workflow and improve resource utilization. However, the existing algorithms do not guarantee to achieve high resource utilization although they have abilities to achieve high execution efficiency. The higher resource utilization depends on the reusability of VM instances. In this work, we propose a new intelligent water drops based workflow scheduling algorithm for Infrastructure-as-a-Service (IaaS) cloud. The objectives of the proposed algorithm are to achieve higher resource utilization and minimize the makespan within the given deadline and budget constraints. The first contribution of the algorithm is to find multiple partial critical paths (PCPs) of a workflow which helps in finding suitable VM instances. The second contribution is a scheduling strategy for PCP-VM assignment for assigning the VM instances. The proposed algorithm is evaluated through various simulation runs using synthetic datasets and various performance metrics. Through comparison, we show the superior performance of the proposed algorithm over the existing ones.