yantaidahan的博客

构建了网络未知威胁检测系统原型： 实现了一个网络未知威胁检测系统 原型，解决了传统检测方法对网络流量中未知威胁检测能力不足的问题， 达到了保障网络安全的目的。

Network anomaly detection is a vibrant research area. Researchers have approached this problem using various techniques such as artificial intelligence, machine learning, and state machine modeling. In this paper, we first review these anomaly detection methods and then describe in detail a statistical signal processing technique based on abrupt change detection. We show that this signal processing technique is effective at detecting several network anomalies. Case studies from real network data that demonstrate the power of the signal processing approach to network anomaly detection are presented. The application of signal processing techniques to this area is still in its infancy, and we believe that it has great potential to enhance the field, and thereby improve the reliability of IP networks.

Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concernsregardingthefeasibilityandsustainabilityofcurrentapproacheswhenfacedwiththedemandsofmodernnetworks.More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs.Ourproposedclassifierhasbeenimplementedingraphics processing unit (GPU)-enabled TensorFlow and evaluated using the benchmark KDD Cup ’99 and NSL-KDD datasets. Promising resultshavebeenobtainedfromourmodelthusfar,demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

Increasing interest in the adoption of cloud computing has exposed it to cyber-attacks. One of such is distributed denial of service (DDoS) attack that targets cloud’s bandwidth, services and resources to make it unavailable to both the cloud providers and users. Due to the magnitude of traffic that needs to be processed, data mining and machine learning classification algorithms have been proposed to classify normal packets from an anomaly. Feature selection has also been identified as a pre-processing phase in cloud DDoS attack defence that can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset, during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. An extensive experimental evaluation of our proposed method was performed using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The result obtained shows that our proposed method effectively reduced the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques

网络入侵情报协同分析的过程中，告警数据的共享使得被攻击者面临隐私泄露的风险。针对现有的告警信息隐 私保护方法无法应对任意背景知识下恶意分析的问题，提出了一种支持差分隐私保护的网络告警关联分析方法。首先以原 始告警序列数据集作为输入，利用 Laplace 机制构建满足差分隐私的噪声告警序列前缀树，然后通过遍历噪声前缀树生成泛 化告警序列数据集

研究保证网络安全有效阻止入侵行为，针对网络入侵检测问题，传统 K 均值聚类算法在网络入侵检测应用过程中， 存 在对聚类中心初始值敏感、易陷入局部最优值等不足，从而使网络入侵检测正确率低，误检测率高难题。为了提高检测准确 性，提出一种改进的 K 均值聚类网络入检测算法

关于入侵检测系统的论文，明确入侵检测系统的研究方向，总结入侵检测系统的研究现状。将其他的系统安全技术与入侵检测系统结合。

中国大数据发展趋势及预测解读，选自《中国计算机协会通讯》

计算机等级考试二级C语言的题库，共一百到题目，上机题库

这是关于在Linux环境下汇编语言的图书，是ubantu操作系统的环境下。

容时容断网络DTN的体系结构和原理课件，产生的背景需求，未来发展方向。