基于连接数据分析和OSELM分类器的网络入侵检测系统

针对网络入侵的实时高效检测问题,提出一种基于网络连接数据分析和在线贯序极限学习机(OSELM)分类器的网络入侵检测系统(IDS)。首先,对入侵数据库中的网络连接数据进行分析,通过特征选择算法选择出最优特征子集。然后,迭代执行交叉验证,并通过Alpha剖析来缩减样本尺寸,以此减低后续分类器的计算复杂度。最后,利用优化后的样本特征集来训练OSELM分类器,以此构建一个网络实时入侵检测系统。在NSL-KDD数据库上的实验结果表明,提出的IDS具有较高的检测率和较低的误报率,同时检测时间较短,符合实时入侵检测的要求。     

Intrusion Detection System for Big Data Analytics in IoT Environment

In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages: data transformation and data normalization. To manage big data, Hadoop ecosystem is employed. Besides, the IMFSDL-IDS model includes a hill climbing with moth flame optimization (HCMFO) for feature subset selection to reduce the complexity and increase the overall detection efficiency. Moreover, the beetle antenna search (BAS) with variational autoencoder (VAE), called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data. The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance. To validate the intrusion detection performance of the IMFSDL-IDS system, a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects. The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25% and 97.39% on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.     

Building lightweight intrusion detection system using wrapper-based feature selection mechanisms

Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and network infrastructures. How to build a lightweight IDS is a hot topic in network security. Moreover, feature selection is a classic research topic in data mining and it has attracted much interest from researchers in many fields such as network security, pattern recognition and data mining. In this paper, we effectively introduced feature selection methods to intrusion detection domain. We propose a wrapper-based feature selection algorithm aiming at building lightweight intrusion detection system by using modified random mutation hill climbing (RMHC) as search strategy to specify a candidate subset for evaluation, as well as using modified linear Support Vector Machines (SVMs) iterative procedure as wrapper approach to obtain the optimum feature subset. We verify the effectiveness and the feasibility of our feature selection algorithm by several experiments on KDD Cup 1999 intrusion detection dataset. The experimental results strongly show that our approach is not only able to speed up the process of selecting important features but also to yield high detection rates. Furthermore, our experimental results indicate that intrusion detection system with feature selection algorithm has better performance than that without feature selection algorithm both in detection performance and computational cost.     

Comparative study for feature selection algorithms in intrusion detection system

The Intrusion Detection System (IDS) deals with the huge amount of network data that includes redundant and irrelevant features causing slow training and testing procedure, higher resource usage and poor detection ratio. Feature selection is a vital preprocessing step in intrusion detection. Hence, feature selec-tion is an essential issue in intrusion detection and need to be addressed by selec-ting the appropriate feature selection algorithm. A major challenge to select the optimal feature selection methods can precisely calculate the relevance of fea-tures to the detection process and the redundancy among features. In this paper, we study the concepts and algorithms used for feature selection algorithms in the IDS. We conclude this paper by identifying the best feature selection algorithm to select the important and useful features from the network dataset.     

一种高效的面向轻量级入侵检测系统的特征选择算法

特征选择是网络安全、模式识别、数据挖掘等领域的重要问题之一.针对高维数据对象,特征选择一方面可以提高分类精度和效率,另一方面可以找出富含信息的特征子集.文中提出一种wrapper型的特征选择算法来构建轻量级入侵检测系统.该算法采用遗传算法和禁忌搜索相混合的搜索策略对特征子集空间进行随机搜索,然后利用提供的数据在无约束优化线性支持向量机上的平均分类正确率作为特征子集的评价标准来获取最优特征子集.文中按照DOS,PROBE,R2L,U2R 4个类别对KDD1999数据集进行分类,并且在每一类上进行了大量的实验.实验结果表明,对每一类攻击文中提出的特征选择算法不仅可以加快特征选择的速度,而且基于该算法构建的入侵检测系统在建模时间、检测时间、检测已知攻击、检测未知攻击上,与没有运用特征选择的入侵检测系统相比具有更好的性能.     

基于改进多目标遗传算法的入侵检测集成方法

针对现有入侵检测算法中存在着对不同类型攻击检测的不均衡性以及冗余或无用特征导致的检测模型复杂与检测精度下降的问题,提出了一种基于改进多目标遗传算法的入侵检测集成方法.利用改进的多目标遗传算法生成检测率与误报率均衡优化的最优特征子集的集合,并采用选择性集成方法挑选精确的、具有多样性的基分类器构造集成入侵检测模型.实验结果表明,该算法能够有效地解决入侵检测中存在的特征选择问题,并在保证较高检测精度的基础上,对不同类型的攻击检测具有良好的均衡性.     

IDS自适应特征选择算法——进化包装(Wrapper)算法分析

随着网络技术和网络规模的不断发展,网络安全已经成为人们无法回避的问题,因此为了保护现在越来越多的敏感信息,入侵检测技术也成为了一种非常重要的技术,得到了越来越多的重视。然而对其中一个重要部分―特征的自动选择的研究非常少。本文提出了一个EA用来执行特征的自动选择以及对RBF网络的自动优化。经过特征选择这个步骤可以显著的减少输入特征的数量,这样可以有效的减少过适应。此外,减少输入特征数目,还可以减少神经网络的执行时间。     

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.     

数据挖掘技术在入侵检测中的应用

针对入侵检测系统的研究现状和面临的问题,研究了数据挖掘技术应用到入侵检测中的优势,分析了当前基于数据挖掘的入侵检测中存在的不足。针对目前基于数据挖掘的入侵检测时空效率不高的问题,对频繁模式算法进行了研究,改进了频繁模式算法,用两步模式增长代替一步模式增长模式来加快挖掘速度,并且增加时间特性、属性相关和轴属性加以约束。通过试验证明改进后的算法在时空效率上得到了改善,减少了扫描数据库的时间和生成无意义的模式,提高了规则的有用性。     

MultiETSC: automated machine learning for early time series classification

Early time series classification (EarlyTSC) involves the prediction of a class label based on partial observation of a given time series. Most EarlyTSC algorithms consider the trade-off between accuracy and earliness as two competing objectives, using a single dedicated hyperparameter. To obtain insights into this trade-off requires finding a set of non-dominated (Pareto efficient) classifiers. So far, this has been approached through manual hyperparameter tuning. Since the trade-off hyperparameters only provide indirect control over the earliness-accuracy trade-off, manual tuning is tedious and tends to result in many sub-optimal hyperparameter settings. This complicates the search for optimal hyperparameter settings and forms a hurdle for the application of EarlyTSC to real-world problems. To address these issues, we propose an automated approach to hyperparameter tuning and algorithm selection for EarlyTSC, building on developments in the fast-moving research area known as automated machine learning (AutoML). To deal with the challenging task of optimising two conflicting objectives in early time series classification, we propose MultiETSC, a system for multi-objective algorithm selection and hyperparameter optimisation (MO-CASH) for EarlyTSC. MultiETSC can potentially leverage any existing or future EarlyTSC algorithm and produces a set of Pareto optimal algorithm configurations from which a user can choose a posteriori. As an additional benefit, our proposed framework can incorporate and leverage time-series classification algorithms not originally designed for EarlyTSC for improving performance on EarlyTSC; we demonstrate this property using a newly defined, “naïve” fixed-time algorithm. In an extensive empirical evaluation of our new approach on a benchmark of 115 data sets, we show that MultiETSC performs substantially better than baseline methods, ranking highest (avg. rank 1.98) compared to conceptually simpler single-algorithm (2.98) and single-objective alternatives (4.36).

     

Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

An intrusion detection system (IDS) becomes an important tool for ensuring security in the network. In recent times, machine learning (ML) and deep learning (DL) models can be applied for the identification of intrusions over the network effectively. To resolve the security issues, this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique, called BBOFS-DRL for intrusion detection. The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network. To attain this, the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm (BOA) to elect feature subsets. Besides, DRL model is employed for the proper identification and classification of intrusions that exist in the network. Furthermore, beetle antenna search (BAS) technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency. For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model, a wide-ranging experimental analysis is performed against benchmark dataset. The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.     

基于免疫的入侵检测模型与通信应用研究

本文提出一个基于人工免疫机制的入侵检测模型(IDS)。该模型可用于计算机网络及无线通讯网络的安全系统。同时文章提出了一种新的数据存取和分析方法,并具体描述了怎样提取人类免疫系统的特点来应用于入侵检测系统的软件包。此研究成果的一个显著优点是极大的减少了入侵检测日志文件的容量,从而有效提高了系统的可维护性,帮助管理员更好的监测和观察主机异常活动。最后文章用实验数据显示了该算法的有效和可行性。     

An automatically tuning intrusion detection system.

An intrusion detection system (IDS) is a security layer used to detect ongoing intrusive activities in information systems. Traditionally, intrusion detection relies on extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been deployed for intrusion detection. An IDS is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current systems depends on the system operators in working out the tuning solution and in integrating it into the detection model. In this paper, an automatically tuning IDS (ATIDS) is presented. The proposed system will automatically tune the detection model on-the-fly according to the feedback provided by the system operator when false predictions are encountered. The system is evaluated using the KDDCup'99 intrusion detection dataset. Experimental results show that the system achieves up to 35% improvement in terms of misclassification cost when compared with a system lacking the tuning feature. If only 10% false predictions are used to tune the model, the system still achieves about 30% improvement. Moreover, when tuning is not delayed too long, the system can achieve about 20% improvement, with only 1.3% of the false predictions used to tune the model. The results of the experiments show that a practical system can be built based on ATIDS: system operators can focus on verification of predictions with low confidence, as only those predictions determined to be false will be used to tune the detection model.     

An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection

Intrusion detection system (IDS) is to monitor the attacks occurring in the computer or networks. Anomaly intrusion detection plays an important role in IDS to detect new attacks by detecting any deviation from the normal profile. In this paper, an intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection is proposed. The key idea is to take the advantage of support vector machine (SVM), decision tree (DT), and simulated annealing (SA). In the proposed algorithm, SVM and SA can find the best selected features to elevate the accuracy of anomaly intrusion detection. By analyzing the information from using KDD’99 dataset, DT and SA can obtain decision rules for new attacks and can improve accuracy of classification. In addition, the best parameter settings for the DT and SVM are automatically adjusted by SA. The proposed algorithm outperforms other existing approaches. Simulation results demonstrate that the proposed algorithm is successful in detecting anomaly intrusion detection.     

基于量子进化算法的网络入侵检测特征选择

针对当前网络入侵检测中普遍存在检测速度较慢的缺陷,提出了一种新的网络入侵检测特征选择方法。该方法将量子进化算法应用于网络入侵检测的特征选择,从网络连接的原始特征属性中选出一组有效的特征用于入侵检测,以提高检测效率。首先以增强寻优性能为目标改进了量子进化算法,基于特征属性的Fisher比构造了特征子集的评价函数,然后按照量子进化算法的流程设计了网络入侵检测特征选择算法。通过KDD99样本数据集的实验,表明算法是有效的,既保证了入侵检测的分类性能,也提高了入侵检测的效率。     

A neuro-genetic based short-term forecasting framework for network intrusion prediction system

Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by ＂authorized＂ users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system （IDS） is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference （KDD 2009） intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS （NGIDS） involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.     

一个基于数据挖掘的入侵检测系统模型

1.概述入侵检测实质上归结为对安全审计数据的处理。按分析引擎所使用的检测方法可以将入侵检测系统分为误用(基于知识)检测和异常(基于行为)检测。前者运用已知攻击方法,根据已定义好的入侵模式,通过判断这些入侵模式是否出现来进行检测。为了克服误用检测的缺陷,人们提出了针对入侵行为的异常检测模型,指根据使用者的行为或资源使用状况的正常程度来判断是否入侵,而不依赖于具体行为是否出现来检测,目前处于研究阶段。     

面向入侵检测的基于多目标遗传算法的特征选择

针对刻画网络行为的特征集中存在着不相关或冗余特征,从而导致入侵检测性能下降的问题,本文提出了一种基于多目标遗传算法的特征选择方法,将入侵检测中的特征选择问题视为多目标优化问题来处理。实验结果表明,该方法能够实现检测精度与检测算法复杂性的均衡优化,在显著提高检测算法效率的同时,检测精度也有所提高。     

基于GPU和特征选择的SVM入侵检测模型

基于支持向量机的入侵检测模型检测效率较低,为此,提出一种基于图形处理器(GPU)和特征选择的入侵检测模型。在入侵检测过程中,采用基于GPU的并行计算模型进行训练,并对样本的特征进行合理选择,从而提高检测效率。实验结果表明,在保证系统性能的情况下,该模型可以缩短训练时间。     

面向入侵检测的基于IMGA和MKSVM的特征选择算法

入侵检测系统处理的数据具有数据量大、特征维数高等特点,会降低检测算法的处理速度和检测效率。为了提高入侵检测系统的检测速度和准确率,将特征选择应用到入侵检测系统中。首先提出一种基于免疫记忆和遗传算法的高效特征子集生成策略,然后研究基于支持向量机的特征子集评估方法。并针对可能出现的数据集不平衡造成的特征子集评估能力下降,以黎曼几何为依据,利用保角变换对核函数进行修改,以提高支持向量机的分类泛化能力。实验仿真表明,提出的特征选择算法不仅可以提高特征选择的效果,而且在不平衡数据集上具有更好的特征选择能力。还表明,基于该方法构建的入侵检测系统与没有运用特征选择的入侵检测系统相比具有更好的性能。