A Novel Immune System Model and Its Application to Network Intrusion Detection

Based on analyzing the techniques and architecture of existing network Intrusion Detection System (IDS), and probing into the fundament of Immune System (IS), a novel immune model is presented and applied to network IDS, which is helpful to design an effective IDS. Besides, this paper suggests a scheme to represent the self profile of network. And an automated self profile extraction algorithm is provided to extract self profile from packets. The experimental results prove validity of the scheme and algorithm, which is the foundation of the immune model.

An Improved Artificial Immune System-Based Network Intrusion Detection by Using Rough Set

With theincreasing worldwide network attacks, intrusion detection (ID) hasbecome a popularresearch topic inlast decade.Several artificial intelligence techniques such as neural networks and fuzzy logichave been applied in ID. The results are varied. Theintrusion detection accuracy is themain focus for intrusion detection systems (IDS). Most research activities in the area aiming to improve the ID accuracy. In this paper, anartificial immune system (AIS) based network intrusion detection scheme is proposed. An optimized feature selection using Rough Set (RS) theory is defined. The complexity issue is addressed in the design of the algorithms. The scheme is tested on the widely used KDD CUP 99 dataset. The result shows that theproposed scheme outperforms other schemes in detection accuracy.

Immune Recognition Method Based on Analogy Reasoning in Intrusion Detection System

In this paper, we propose an analogy based immune recognition method that focuses on the implement of the clone selection process and the negative selection process by means of analogy similarity. This method is applied in an IDS （Intrusion Detection System） following several steps. Firstly, the initial abnormal behaviours sample set is optimized through the combining of the AIS （Artificial Immune System） and the genetic algorithm. Then, the abnormity probability algorithm is raised considering the two sides of abnormality and normality. Finally, an intrusion detection system model is established based on the above algorithms and models.

Immune Based Intrusion Detector Generating Algorithm

Immune-based intrusion detection approaches are studied. The methods ofconstructing self set and generating mature detectors are researched and improved. A binary encodingbased self set construction method is applied. First, the traditional mature detector generatingalgorithm is improved to generate mature detectors and detect intrusions faster. Then, a novelmature detector generating algorithm is proposed based on the negative selection mechanism.According to the algorithm, less mature detectors are needed to detect the abnormal activities inthe network. Therefore, the speed of generating mature detecotrs and intrusion detection isimproved. By comparing with those based on existing algorithms, the intrusion detection system basedon the algorithm has higher speed and accuracy.

Intrusion detection based on rough set and artificial immune

In order to increase intrusion detection rate and decrease false positive detection rate,a novel intrusion detection algorithm based on rough set and artificial immune( RSAI-IDA) is proposed.Using artificial immune in intrusion detection,anomaly actions are detected adaptively,and with rough set,effective antibodies can be obtained. A scheme,in which antibodies are partly generated randomly and others are from the artificial immune algorithm,is applied to ensure the antibodies diversity. Finally,simulations of RSAI-IDA and comparisons with other algorithms are given. The experimental results illustrate that the novel algorithm achieves more effective performances on anomaly intrusion detection,where the algorithm's time complexity decreases,the true positive detection rate increases,and the false positive detection rate is decreased.

Towards an Artificial Immune System for Detecting Anomalies in Wireless Mesh Networks

This paper focuses on investigating immunological principles in designing a multi-agent security architecture for intrusion detection and response in wireless mesh networks.In this approach,the immunity-based agents monitor the situation in the network.These agents can take appropriate actions according to the underlying security policies.Specifically,their activities are coordinated in a hierarchical fashion while sensing,communicating,determining and generating responses.Such an agent can learn about and adapt to its environment dynamically and can detect both known and unknown intrusions.The proposed intrusion detection architecture is designed to be flexible,extendible,and adaptable so that it can perform real-time monitoring.This paper provides the conceptual view and a general framework of the proposed system.In the end,the architecture is illustrated by an example and by simulation to show it can prevent attacks efficiently.

A Network Intrusion Detection Model Based on Immune Multi-Agent

A new network intrusion detection model based on immune multi-agent theory is established and the concept of multi-agents is advanced to realize the logical structure and running mechanism of immune multi-agent as well as multi-level and distributed detection mechanism against network intrusion, using the adaptability, diversity and memory properties of artificial immune algorithm and combing the robustness and distributed character of multi-agents system structure. The experiment results conclude that this system is working pretty well in network security detection.

Multimodal Evolution Approach to Multidimensional Intrusion Detection

An artificial immunity based multimodal evolution algorithm is developed to generate detectors with variable coverage for multidimensional intrusion detection. In this algorithm, a proper fitness function is used to drive the detectors to fill in those detection holes close to self set or among self spheres, and genetic algorithm is adopted to reduce the negative effects that different distribution of self imposes on the detector generating process. The validity of the algorithm is tested with spherical and rectangular detectors, respectively, and experiments performed on two real data sets （machine learning database and DAPRA99） indicate that the proposed algorithm can obtain good results on spherical detectors, and that its performances in detection rate, false alarm rate, stabih＇ty, time cost, and adaptability to incomplete training set on spherical detectors are all better than on rectangular ones.

An Intrusion Detection System Model Based on Immune Principle and Performance Analysis

The study of security in computer networks is a key issue, which is a rapidlygrowing area of interest because of its importance. Main network security problems are analyzed inthis paper above all, which currently are confronted with network systems and existing works inintrusion detection. And then an intrusion detection system model based on Immune Principle (IPIDS)is presented. Meanwhile, it expatiates detailed implementation of the methods how to reduce the highfalse positive and negative alarms of the traditional Intrusion Detection System (IDS). At last asimple simulation is performed on this model just using string match algorithm as binding mechanism.The simulation results indicate that the model can detect malicious activity effectively, andconsequently the security and steadiness of the whole network system are improved also.

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,the nonselfdetector generation efficiency is low;a large number of nonselfdetector is needed for precise detection;low detection rate with various application data sets.Aiming at those problems,a novel radius adaptive based on center-optimized hybrid detector generation algorithm(RACO-HDG)is put forward.To our best knowledge,radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity.RACO-HDG works efficiently in three phases.At first,a small number of self-detectors are generated,different from typical NSAs with a large number of self-sample are generated.Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible.Secondly,without any prior knowledge of the data sets or manual setting,the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism.In this way,the number of abnormal detectors is decreased sharply,while the coverage area of the nonself-detector is increased otherwise,leading to higher detection performances of RACOHDG.Finally,hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected.Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate,lower false alarm rate and higher detection efficiency compared with other excellent algorithms.

基于改进V-detector算法的入侵检测研究与优化

随着互联网用户数量的剧增,网络威胁也在迅速增长,传统的被动防御措施不足以防御日益多变的网络入侵。传统入侵检测系统原理是收集病毒特征再进行特征匹配,对于未知病毒,传统检测机制存在滞后性。面对日益繁杂的网络安全环境,研究基于人工免疫理论的入侵检测系统具有重要意义。文章首先介绍人工免疫理论的核心思想否定选择算法,进而介绍实值否定选择算法和V-detector算法。针对V-detector算法的不足,进行3个方面的改进:提出基于定距变异的克隆选择算法提高检测器生成效率;提出去冗算法减少检测器冗余,加快算法收敛;引入并改进假设检验方法,对检测器集合的覆盖率进行评估。实验证明,文章提出的改进V-detector算法能有效提升检测精度,减少检测黑洞,并大大缩减检测时间。

基于灰狼优化的V-detector检测器分布方法

基于免疫否定选择机制的V-detector算法,其检测器分布不优仍导致检测黑洞或冗余问题。本文借助群体智能优化算法中灰狼优化算法的无导数寻优机制,提出了一种改进的V-detector算法。引入混沌机制对灰狼种群进行合理的初始化,通过莱维飞行增强算法的全局寻优能力,利用改进后的V-detector算法优化检测器的落点分布,使得检测器的分布更加合理,并获得更高的覆盖率。在二维数据集上的验证,表明改进算法提高了检测器覆盖率;在NSL-KDD数据集上的验证,表明优化后的检测器集合能在较短时间内生成质量较高的检测器。

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

An immune based dynamic intrusion detection model

With the dynamic description method for self and antigen, and the concept of dynamic immune tolerance for lymphocytes in network-security domain presented in this paper, a new immune based dynamic intrusion detection model (Idid) is proposed. In Idid, the dynamic models and the corresponding recursive equations of the lifecycle of ma- ture lymphocytes, and the immune memory are built. Therefore, the problem of the dynamic description of self and nonself in computer immune systems is solved, and the defect of the low efficiency of mature lymphocyte generating in traditional computer immune systems is overcome. Simu- lations of this model are performed, and the comparison experiment results show that the proposed dynamic intrusion detection model has a better adaptability than the traditional methods.

Multi-Agent Network Intrusion Active Defense Model Based on Immune Theory

Inspired by the immune theory and multi-agent systems, an immune multi-agent active defense model for network intrusion is established. The concept of immune agent is introduced, and its running mechanism is established. The method, which uses antibody concentration to quantitatively describe the degree of intrusion danger, is presented. This model implements the multi-layer and distributed active defense mechanism for network intrusion. The experiment results show that this model is a good solution to the network security defense.

Novel design concepts for network intrusion systems based on dendritic cells processes

An abstraction and an investigation to the worth of dendritic cells (DCs) ability to collect, process and present antigens are presented. Computationally, this ability is shown to provide a feature reduction mechanism that could be used to reduce the complexity of a search space, a mechanism for development of highly specialized detector sets as well as a selective mechanism used in directing subsets of detectors to be activated when certain danger signals are present. It is shown that DCs, primed by different danger signals, provide a basis for different anomaly detection pathways. Different antigen-peptides are developed based on different danger signals present, and these peptides are presented to different adaptive layer detectors that correspond to the given danger signal. Experiments are then undertaken that compare current approaches, where a full antigen structure and the whole repertoire of detectors are used, with the proposed approach. Experiment results indicate that such an approach is feasible and can help reduce the complexity of the problem by significant levels. It also improves the efficiency of the system, given that only a subset of detectors are involved during the detection process. Having several different sets of detectors increases the robustness of the resulting system. Detectors developed based on peptides are also highly discriminative, which reduces the false positives rates, making the approach feasible for a real time environment.

An immunity-based technique to detect network intrusions

This paper briefly reviews other people’s works on negative selection algorithm and their shortcomings. With a view to the real problem to be solved, authors bring forward two assumptions, based on which a new immune algorithm, multi-level negative selection algorithm, is developed. In essence, compared with Forrest’s negative selection algorithm, it enhances detector generation efficiency. This algorithm integrates clonal selection process into negative selection process for the first time. After careful analyses, this algorithm was applied to network intrusion detection and achieved good results.

改进复合免疫算法的大规模网络入侵攻击检测

为提高入侵攻击检测效果以应对多种网络攻击,提出一种改进复合免疫算法的大规模网络入侵攻击检测方法。通过对自我和非我的区分匹配,描述网络入侵攻击检测问题,凭借模糊算法规则明确免疫机制界限。将遗传算法带入否定选择法中,令任意染色体可描述为模糊规则中的部分限定,设定惩罚系数限制规则覆盖正常样本的概率,令检测器选择过程简单化。选用二进制编码和汉明距离计算抗体与抗原间亲和度,得出二者在大规模网络内的浓度,最后基于检测器数据集与网络数据对比,若匹配则存在入侵攻击,根据对比结果记录抗体并报警。实验结果表明,所提方法能够提高检测精度和效率,具有极佳的适用性和应用价值。

基于人工智能技术的轻量级网络入侵检测系统设计

以提升网络入侵检测技术水平为目的,设计基于人工智能技术的轻量级网络入侵检测系统。该系统数据采集层利用若干个用户探针连接IDS检测服务器后,使用网络数据包捕获模块捕获用户网络运行数据,再通过传输层内防火墙、核心交换机和MQTT/CoAP通信协议将用户网络运行数据发送到逻辑运算层内,该层利用数据预处理模块对用户网络运行数据进行去噪预处理后,将其输入到基于人工智能的网络入侵检测模块内,通过该模块输出轻量级网络入侵检测结果,然后将检测结果发送到展示层,通过入侵告警信息、数据可视化展示等模块实现人机交互。实验表明:该系统运行较为稳定,可有效检测不同类型网络入侵的同时,其检测及时性和入侵告警能力较好,应用效果良好。