非平衡技术在高速网络入侵检测中的应用

针对现有的高速网络入侵检测系统丢包率高、检测速度慢以及检测算法对不同类型攻击检测的非平衡性等问题,提出了采用两阶段的负载均衡策略的检测模型。在线检测阶段对网络数据包按协议类型进行分流的检测,离线建模阶段对不同协议类型的数据进行学习建模,供在线部分检测。在讨论非平衡数据处理的各种采样技术基础上,采用改进后的过抽样少数样本合成过采样技术（SMOTE）对网络数据进行预处理,采用AdaBoost 、随机森林算法等进行分类。另外对特征选取等方面进行了实验,结果表明SMOTE过抽样可提高各少数类的检测,随机森林算法分类效果好而且建模所用的时间稳定。     

高速网络环境下的自适应入侵检测方法研究

为了实现高速网络环境下的入侵检测,对入侵检测的机理进行探讨,将入侵检测归结于不完备数据集上的推理过程,提出知识库的相似度、完备度等概念,并用其对知识库的规模和增长速度进行控制,从而保证入侵检测在有限规模的空间中进行搜索。同时,采用信息增益等方法将入侵检测转换到低维空间上进行。实验结果表明上述方法有效降低了入侵检测系统的计算负荷,提高了其实时响应性能。     

高速网络环境下的入侵检测技术研究

首先介绍了目前高速网络环境下的入侵检测系统的研究概况,接着对基于FPGA和基于负载均衡技术的两类入侵检测系统模型进行了分析,并重点研究了基于网络处理器的采用负载均衡技术的入侵检测系统中的关键技术即数据捕获技术、负载均衡技术和数据分析技术.     

高速入侵检测研究

高速入侵检测是当前网络安全领域研究的热点之一,分析了高速环境下入侵检测面临的主要问题和各种制约因素,并对高速入侵检测的进行了多方面地研究,分析和介绍了零拷贝技术、快速匹配算法.分析指出基于分流的分布式入侵检测是高速检测的发展方向.最后给出高速入侵检测后续有待研究和解决的问题.     

高速网络环境下的入侵检测技术研究综述*

高速网的普及应用对入侵检测技术提出了更高要求,传统的方法已难以适应处理大流量的网络数据。对入侵检测过程进行分析,指出高速网络环境下制约入侵检测效果的不利因素和难点,强调应从数据包捕获、模式匹配、负载均衡、系统架构等方面入手,充分利用软件的灵活性、专用硬件的并行性和快速性来提高入侵检测系统的性能,以适应高速的网络环境。     

高速网络环境下的网络入侵检测系统的研究

高速网络环境下的入侵检测是一个新的研究方向。基于负载均衡技术和协议分析技术,提出了一个能够应用在高速环境下的网络入侵检测系统。负载均衡技术把在前端捕获的高速数据流进行分化,以利于后端处理;协议分析技术利用网络协议的层次性和相关协议的知识快速地判断攻击特征是否存在。基于代理的分布式体系结构,增强了系统的可扩展性,提高了系统的检测效率。     

数据库入侵检测的一种数据挖掘方法

针对在数据库系统中检测恶意事务提出了一种数据挖掘方法。该方法挖掘数据库中各数据项事务之间的数据关联规则,所设计的数据关联规则挖掘器主要用来挖掘与数据库日志记录相关的数据。不符合关联规则的事务作为恶意事务。试验证明该方法可以有效的检测到恶意事务。     

Of daemons and men: A file system approach towards intrusion detection

We present FI²DS a file system, host based anomaly detection system that monitors Basic Security Module (BSM) audit records and determines whether a web server has been compromised by comparing monitored activity generated from the web server to a normal usage profile. Additionally, we propose a set of features extracted from file system specific BSM audit records, as well as an IDS that identifies attacks based on a decision engine that employs one-class classification using a moving window on incoming data. We have used two different machine learning algorithms, Support Vector Machines (SVMs) and Gaussian Mixture Models (GMMs) and our evaluation is performed on real-world datasets collected from three web servers and a honeynet. Results are very promising, since FI²DS detection rates range between 91% and 95.9% with corresponding false positive rates ranging between 8.1× 10⁻² % and 9.3× 10⁻⁴ %. Comparison of FI²DS to another state-of-the-art filesystem-based IDS, FWRAP, indicates higher effectiveness of the proposed IDS in all three datasets. Within the context of this paper FI²DS is evaluated for the web daemon user; nevertheless, it can be directly extended to model any daemon-user for both intrusion detection and postmortem analysis.     

Approximate reduction of finite automata for high-speed network intrusion detection

International Journal on Software Tools for Technology Transfer - We consider the problem of approximate reduction of non-deterministic automata that appear in hardware-accelerated network...     

A triangle area based nearest neighbors approach to intrusion detection

Intrusion detection is a necessary step to identify unusual access or attacks to secure internal networks. In general, intrusion detection can be approached by machine learning techniques. In literature, advanced techniques by hybrid learning or ensemble methods have been considered, and related work has shown that they are superior to the models using single machine learning techniques. This paper proposes a hybrid learning model based on the triangle area based nearest neighbors (TANN) in order to detect attacks more effectively. In TANN, the k-means clustering is firstly used to obtain cluster centers corresponding to the attack classes, respectively. Then, the triangle area by two cluster centers with one data from the given dataset is calculated and formed a new feature signature of the data. Finally, the k-NN classifier is used to classify similar attacks based on the new feature represented by triangle areas. By using KDD-Cup ’99 as the simulation dataset, the experimental results show that TANN can effectively detect intrusion attacks and provide higher accuracy and detection rates, and the lower false alarm rate than three baseline models based on support vector machines, k-NN, and the hybrid centroid-based classification model by combining k-means and k-NN.     

A multinomial logistic regression modeling approach for anomaly intrusion detection

Although researchers have long studied using statistical modeling techniques to detect anomaly intrusion and profile user behavior, the feasibility of applying multinomial logistic regression modeling to predict multi-attack types has not been addressed, and the risk factors associated with individual major attacks remain unclear. To address the gaps, this study used the KDD-cup 1999 data and bootstrap simulation method to fit 3000 multinomial logistic regression models with the most frequent attack types (probe, DoS, U2R, and R2L) as an unordered independent variable, and identified 13 risk factors that are statistically significantly associated with these attacks. These risk factors were then used to construct a final multinomial model that had an ROC area of 0.99 for detecting abnormal events. Compared with the top KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm, the multinomial logistic model-based classification results had similar sensitivity values in detecting normal (98.3% vs. 99.5%), probe (85.6% vs. 83.3%), and DoS (97.2% vs. 97.1%); remarkably high sensitivity in U2R (25.9% vs. 13.2%) and R2L (11.2% vs. 8.4%); and a significantly lower overall misclassification rate (18.9% vs. 35.7%). The study emphasizes that the multinomial logistic regression modeling technique with the 13 risk factors provides a robust approach to detect anomaly intrusion.     

SLA-based complementary approach for network intrusion detection

Enhancing the intrusion detection system is essential to maintain user confidence in network services security. However, the threat of intruders on Internet services is prevalent. This paper proposes a distributed edge-to-edge complementary approach for intrusion detection in a DiffServ/MPLS domain. The QoS metrics are inspected at the edges routers to determine anomalous behavior in the network traffic. Consumed ratios of one-way delay variation (OWDV) and packet loss are computed to monitor service level agreement (SLA) violations. The bandwidth ratio is measured to differentiate abnormal from normal traffic as well as to detect multiple intrusions launched simultaneously. We employed SLA as a comparison scale to infer the deviation between the users consumed ratios and the predefined ratios in the SLA. Service violation occurs and intrusion may be launched when the predefined ratios are exceeded. The complementary services of DiffServ and MPLS techniques guarantee accurate measurements, whereas the complementary measurements of active and passive techniques immunize network performance against scalability limitation. Simulation results indicate that the proposed approach is capable of monitoring SLA violations and can filter out traffic of intruders who breach SLA without disturbing the normal traffic of legitimate users.     

Comprehensive approach to intrusion detection alert correlation

Alert correlation is a process that analyzes the alerts produced by one or more intrusion detection systems and provides a more succinct and high-level view of occurring or attempted intrusions. Even though the correlation process is often presented as a single step, the analysis is actually carried out by a number of components, each of which has a specific goal. Unfortunately, most approaches to correlation concentrate on just a few components of the process, providing formalisms and techniques that address only specific correlation issues. This paper presents a general correlation model that includes a comprehensive set of components and a framework based on this model. A tool using the framework has been applied to a number of well-known intrusion detection data sets to identify how each component contributes to the overall goals of correlation. The results of these experiments show that the correlation components are effective in achieving alert reduction and abstraction. They also show that the effectiveness of a component depends heavily on the nature of the data set analyzed.     

基于粗糙熵的离群点检测方法及其在无监督入侵检测中的应用

香农的信息熵被广泛用于粗糙集.利用粗糙集中的粗糙熵来检测离群点,提出一种基于粗糙熵的离群点检测方法,并应用于无监督入侵检测.首先,基于粗糙熵提出一种新的离群点定义,并设计出相应的离群点检测算法-–基于粗糙熵的离群点检测(rough entropy-based outlier detection,REOD);其次,通过将入侵行为看作是离群点,将REOD应用于入侵检测中,从而得到一种新的无监督入侵检测方法.通过多个数据集上的实验表明,REOD具有良好的离群点检测性能.另外,相对于现有的入侵检测方法,REOD具有较高的入侵检测率和较低的误报率,特别是其计算开销较小,适合于在海量高维的数据中检测入侵.     

Effective network intrusion detection using stacking-based ensemble approach

The increasing demand for communication between networked devices connected either through an intranet or the internet increases the need for a reliable and accurate network defense mechanism. Network intrusion detection systems (NIDSs), which are used to detect malicious or anomalous network traffic, are an integral part of network defense. This research aims to address some of the issues faced by anomaly-based network intrusion detection systems. In this research, we first identify some limitations of the legacy NIDS datasets, including a recent CICIDS2017 dataset, which lead us to develop our novel dataset, CIPMAIDS2023-1. Then, we propose a stacking-based ensemble approach that outperforms the overall state of the art for NIDS. Various attack scenarios were implemented along with benign user traffic on the network topology created using graphical network simulator-3 (GNS-3). Key flow features are extracted using cicflowmeter for each attack and are evaluated to analyze their behavior. Several different machine learning approaches are applied to the features extracted from the traffic data, and their performance is compared. The results show that the stacking-based ensemble approach is the most promising and achieves the highest weighted F1-score of 98.24%.

     

TermID: a distributed swarm intelligence-based approach for wireless intrusion detection

With the mushrooming of wireless access infrastructures, the amount of data generated, transferred and consumed by the users of such networks has taken enormous proportions. This fact further complicates the task of network intrusion detection, especially when advanced machine learning (ML) operations are involved in the process. In wireless environments, the monitored data are naturally distributed among the numerous sensor nodes of the system. Therefore, the analysis of data must either happen in a central location after first collecting it from the sensors or locally through collaboration by viewing the problem through a distributed ML perspective. In both cases, concerns are risen regarding the requirements of this demanding task in matters of required network resources and achieved security/privacy. This paper proposes TermID, a distributed network intrusion detection system that is well suited for wireless networks. The system is based on classification rule induction and swarm intelligence principles to achieve efficient model training for intrusion detection purposes, without exchanging sensitive data. An additional achievement is that the produced model is easily readable by humans. While these are the main design principles of our approach, the accuracy of the produced model is not compromised by the distribution of the tasks and remains at competitive levels. Both the aforementioned claims are verified by the results of detailed experiments withheld with the use of a publicly available security-focused wireless dataset.     

高速网络入侵检测系统中基于Hash函数的分流算法

如何使入侵检测系统能适用于高速网络环境,成为当今入侵检测领域急需解决的技术难题.结合机群系统,提出一种基于散列函数的分流算法,将高流量数据流通过该分流算法分为多个数据流,交由机群系统中各节点上的IDS分析引擎处理.实验结果表明,该算法保证同一连接的所有数据报文由同一IDS分析引擎处理,在高速网络环境下保持高检测率,并有效地解决负载平衡问题.     

高速环境下基于数据分流的入侵检测系统设计

提出了一种数据分流的方法,将捕获的网络数据包按某种策略分流转发至多个检测设备进行处理,提高了在高速网络环境下检测系统的性能,解决了硬件发展跟不上网速发展而带来的漏报率高的问题。     

MOVIH-IDS: A mobile-visualization hybrid intrusion detection system

A novel hybrid artificial intelligent system for intrusion detection, called MObile-VIsualization Hybrid IDS (MOVIH-IDS), is presented in this study. A hybrid model built by means of a multiagent system that incorporates an unsupervised connectionist intrusion detection system (IDS) has been defined to guaranty an efficient computer network security architecture. This hybrid IDS facilitates the intrusion detection in dynamic networks, in a more flexible and adaptable manner. The proposed improvement of the system in this paper includes deliberative agents characterized by the use of an unsupervised connectionist model to identify intrusions in computer networks. This hybrid IDS has been probed through several real anomalous situations related to the simple network management protocol as it is potentially dangerous. Experimental results probed the successful detection of such attacks through MOVIH-IDS.     

Swarm intelligence in intrusion detection: A survey

Intrusion Detection Systems (IDS) have nowadays become a necessary component of almost every security infrastructure. So far, many different approaches have been followed in order to increase the efficiency of IDS. Swarm Intelligence (SI), a relatively new bio-inspired family of methods, seeks inspiration in the behavior of swarms of insects or other animals. After applied in other fields with success SI started to gather the interest of researchers working in the field of intrusion detection. In this paper we explore the reasons that led to the application of SI in intrusion detection, and present SI methods that have been used for constructing IDS. A major contribution of this work is also a detailed comparison of several SI-based IDS in terms of efficiency. This gives a clear idea of which solution is more appropriate for each particular case.