智能入侵检测技术的研究

本文简单介绍了智能入侵检测技术，主要包括神经网络技术，计算机免疫学，数据挖掘技术，状态转换分析，信息抽取，专家系统，基于多智能体的检测技术等等，以及智能入侵检测技术的发展趋势。     

基于智能体和神经网路的无线网络入侵检测系统

本文对实际工作中无线网络的日常运行,将会面临的入侵威胁,并针对传统常规处理办法进行了分析,并对传统的入侵检测系统新型试验,结合多方面改进技术,提出了基于神经网络和智能体的无线网络入侵检测系统。     

网络入侵防御系统设计与测试

该文主要研究云计算网络环境下的入侵检测与防御技术,在总结传统入侵检测技术的基础上,对云计算环境中的入侵检测系统进行比较全面的研究,开发以神经网络技术为基础的网络入侵防御系统。对于入侵检测模块,重点对数据捕获、行为规则匹配以及神经网络判别模块进行分析,并通过具体的测试检验其实现结果。     

基于隐马尔科夫模型的电信网络入侵检测方法

针对电信网络中常用异常检测算法都是用单一的方法,即分布式防火墙通过硬判决来进行检测的所带来的缺点,本文提出了一种结合隐马尔科夫模型和神经网络（HMM—BP）的入侵检测技术。该模型是一种双层的随机过程,通过两层的随机过程,可以提炼出一些比较重要的特征和特性。然后对这些特征和特性,病使用神经网络来进行软判决,实验表明该方法可以提高电信网络入侵检测的性能。     

基于机器学习的入侵检测系统

入侵检测系统是保障网络信息安全的重要手段,针对现有的入侵检测技术存在的不足.提出了基于机器学习的入侵检测系统的实现方案.简要介绍几种适合用于入侵检测系统中的机器学习算法,重点阐述基于神经网络、数据挖掘和人工免疫技术的入侵检测系统的性能特点.     

基于神经网络的信息融合算法研究及其在入侵检测中的应用

目前，网络入侵技术越来越先进，许多黑客都具备反检测的能力，他们会有针对性地模仿被入侵系统的正常用户行为；或将自己的入侵时间拉长，使敏感操作分布于很长的时间周期中；还可能通过多台主机联手攻破被入侵系统．对于伪装性入侵行为与正常用户行为来说，仅靠一个传感器的报告提供的信息来识别已经相当困难，必须通过多传感器信息融合的方法来提高对入侵的识别率，降低误警率．应用基于神经网络的主观Bayes方法，经实验，效果良好．     

基于NetMagic的并行入侵检测系统

入侵检测系统是网络安全防护的关键设备,它从网络关键节点收集信息并通过模式匹配等方式,发现网络中的入侵。随着网络的发展,流量的激增与规则库膨胀这两大问题已经成为瓶颈。本文作者设计了一个深度并行的体系结构,通过分发模块将流量调度到多个检测引擎进行并行处理。系统主要包含分发策略模块、高速检测引擎和后台管理模块三个部分。并通过硬件开发将分发策略模块在NetMagic硬件平台上进行实现。测试表明,本作品通过深度并行体系结构较好地实现了高速入侵检测任务,且具有低成本、高安全性以及良好的可扩展性。     

ANFIDS:基于模糊神经网络的自适应入侵检测系统

在研究和分析现有网络入侵检测技术的基础上,提出了一种基于神经网络和模糊推理技术的自适应入侵检测系统（ANFIDS）。该系统运用模糊理论把安全参数模糊化,使得系统能更好地描述网络流量特性与攻击的关系,从而更精确地捕获攻击行为,同时利用网络流量对隶属度函数和模糊规则进行调整和优化。实验结果表明,训练后的ANFIDS系统能够检测网络的异常行为并有效地减低误报率。     

浅析基于军用网络安全的IDS技术

未来的战场将是网络化战场,网络进攻、网络防御等作战样式纷纷出现。而所有这些都是围绕着网络入侵和网络防护进行的,网络入侵和网络防护将成为决定战争和战役战斗胜负的根本,网络入侵及入侵检测技术将成为作战中的关键。     

基于MA的分布式入侵检测系统

对于复杂网络环境，基于移动Agent的分布式入侵检测系统能主动发现的入侵攻击行为，提供实时报警和自动响应。本文将介绍入侵检测及Agent技术，提出并讨论了基于MA的分布式入侵检测系统。     

Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

In recent years, progressive developments have been observed in recent technologies and the production cost has been continuously decreasing. In such scenario, Internet of Things (IoT) network which is comprised of a set of Unmanned Aerial Vehicles (UAV), has received more attention from civilian to military applications. But network security poses a serious challenge to UAV networks whereas the intrusion detection system (IDS) is found to be an effective process to secure the UAV networks. Classical IDSs are not adequate to handle the latest computer networks that possess maximum bandwidth and data traffic. In order to improve the detection performance and reduce the false alarms generated by IDS, several researchers have employed Machine Learning (ML) and Deep Learning (DL) algorithms to address the intrusion detection problem. In this view, the current research article presents a deep reinforcement learning technique, optimized by Black Widow Optimization (DRL-BWO) algorithm, for UAV networks. In addition, DRL involves an improved reinforcement learning-based Deep Belief Network (DBN) for intrusion detection. For parameter optimization of DRL technique, BWO algorithm is applied. It helps in improving the intrusion detection performance of UAV networks. An extensive set of experimental analysis was performed to highlight the supremacy of the proposed model. From the simulation values, it is evident that the proposed method is appropriate as it attained high precision, recall, F-measure, and accuracy values such as 0.985, 0.993, 0.988, and 0.989 respectively.     

智能神经网络开发系统的实现技术

对比了智能神经元模型和传统的神经元模型，论述了智能神经网络系统的组成原理，给出了智能神经网络开发系统的基本模型，并具体地阐述了智能神经网络开发系统基本模型中的各个组成部分。利用智能神经网络开发系统，研究人员可以较为容易地开发神经网络应用程序。     

A Fused Machine Learning Approach for Intrusion Detection System

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet. The interconnectivity of networks has brought various complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities. An intrusion detection system controls the flow of network traffic with the help of computer systems. Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic. For this purpose, when the network traffic encounters known or unknown intrusions in the network, a machine-learning framework is needed to identify and/or verify network intrusion. The Intrusion detection scheme empowered with a fused machine learning technique (IDS-FMLT) is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks. The proposed IDS-FMLT system model obtained 95.18% validation accuracy and a 4.82% miss rate in intrusion detection.     

M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT

In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture known as a Multi-class Classification based Intrusion Detection Model (M-IDM), which typically relies on data collected by real devices and the use of convolutional neural networks (i.e., it exhibits better performance compared with conventional machine learning algorithms, such as naïve Bayes, support vector machine (SVM)). Unlike existing studies, the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices, such as a patient’s monitors (i.e., electrocardiogram and thermometers). The proposed architecture classifies the data into multiple classes: Critical, informal, major, and minor, for intrusion detection. Further, we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms, including naïve Bayes, SVM, and logistic regression, using neural networks.     

High impedance fault detection in power distribution networks using time?frequency transform and probabilistic neural network

An intelligent approach for high impedance fault (HIF) detection in power distribution feeders using advanced signal-processing techniques such as time-time and time-frequency transforms combined with neural network is presented. As the detection of HIFs is generally difficult by the conventional over-current relays, both time and frequency information are required to be extracted to detect and classify HIF from no fault (NF). In the proposed approach, S- and TT-transforms are used to extract time-frequency and time-time distributions of the HIF and NF signals, respectively. The features extracted using S- and TT-transforms are used to train and test the probabilistic neural network (PNN) for an accurate classification of HIF from NF. A qualitative comparison is made between the HIF classification results obtained from feed forward neural network and PNN with same features as inputs. As the combined signal-processing techniques and PNN take one cycle for HIF identification from the fault inception, the proposed approach was found to be the most suitable for HIF classification in power distribution networks with wide variations in operating conditions.     

Intelligent Real-Time Adaptation for Power Efficiency in Sensor Networks

This paper presents an intelligent, dynamic power conservation scheme for sensor networks in which the sensor network operation is adaptive to both changes in the objects under measurement and the network itself. The conservation scheme switches sensor nodes between a sleep and an active mode in a manner such that the nodes can maximize the time they spend in a power-efficient sleep state, which corresponds to a nonmeasuring and/or nontransmitting mode, while not missing important events. A switching decision is made based on changes (or their absence) in the signals sensed from the environment by an intelligent agent that has been trained to determine whether or not a special event has occurred. This intelligent agent is based on a novel neural network topology that allows for a significant reduction in the resource consumption required for its training and operation without compromising its change detection performance. The scheme was implemented to control a sensor network built from a number of Telos rev. B motes currently available on the market. A few new utilities including an original neural network-based intelligent agent, a “visualizer,” a communication manager, and a scheduler have been designed, implemented, and tested. Power consumption measurements taken in a laboratory environment confirm that use of the designed system results in a significant extension of sensor network lifetime (versus “always on” systems) from a few days to a few years.      

Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular information with incredible efficiency. Artificial intelligence, particularly machine learning methods can be used to develop an intelligent intrusion detection framework. There in this article in order to achieve this objective, we propose an intrusion detection system focused on a Deep extreme learning machine (DELM) which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimental results illustrate that the suggested framework outclasses traditional algorithms. In fact, the suggested framework is not only of interest to scientific research but also of functional importance.