Building on the concepts and the formal definitions of self, nonself, antigen, and detector introduced in the research of network intrusion detection, the dynamic evolution models and the corresponding recursive equations of self, antigen, immune-tolerance, lifecycle of mature detectors, and immune memory are presented. Following that, an immune-based model, referred to as AIBM, for dynamic intrusion detection is developed. Simulation results show that the proposed model has several desirable features including self-learning, self-adaption and diversity, thus providing a effective solution for network intrusion detection.

This paper analyzes the deficiencies of current grid trust systems based on PKI (Public Key Infrastructure), ambiguity certificate principal information, and complicated identification process. Inspired by biologic gene technique, we propose a novel grid trust model based on Family Gene (FG) model. The model answers the existing questions in tradition trust model by adopting the technology of family gene. The concepts and formal definitions of Family Gene in the grid trust security domains are given. Then, the mathematical models of Family Gene are established. Our theoretical analysis and experimental results show that the model is a good solution to grid trust domain.

In this paper, we present a novel code-timing estimator for uplink asynchronous direct-sequence code-division multiple-access systems utilizing bandlimited chip waveforms. The proposed estimator requires only the spreading code and training of the desired user. We start from a maximum likelihood (ML) approach that models the intersymbol interference and multiple-access interference as a colored Gaussian process with unknown covariance matrix in the frequency domain. The exact ML estimator is highly nonlinear and requires iterative searches over multi-dimensional parameter space that is impractical to implement. To deal with this difficulty, we invoke asymptotic (large-sample) approximations of the ML criterion and reparameterization techniques, which lead to an asymptotic ML estimator that yields code-timing and channel estimates via efficient noniterative quadratic optimizations. To benchmark the proposed estimator, we provide Cramér-Rao bound analysis for the code-timing estimation problem. Numerical simulation results are presented, which show that the proposed scheme is resistant to interference, fading, and modeling errors (e.g., sampling position errors), and compares favorably to several competing schemes in multipath fading channels.

According to the relationship between the antibody concentration and the pathogen intrusion intensity, here we present an immunity-based model for the network security risk estimation (Insre). In Insre, the concepts and formal definitions of self, nonself, antibody, antigen and lymphocyte in the network security domain are given. Then the mathematical models of the self-tolerance, the clonal selection, the lifecycle of mature lymphocyte, immune memory and immune surveillance are established. Building upon the above models, a quantitative computation model for network security risk estimation, which is based on the calculation of antibody concentration, is thus presented. By using Insre, the types and intensity of network attacks, as well as the risk level of network security, can be calculated quantitatively and in real-time. Our theoretical analysis and experimental results show that Insre is a good solution to real-time risk evaluation for the network security.

This paper proposes a novel grid intrusion detection model based on immune agent (GIDIA), and gives the concepts and formal definitions of self, nonself, antibody, antigen, agent and match algorithm in the grid security domain. Then, the mathematical models of mature MoA (mature monitoring agent), and dynamic memory MoA (memory monitoring agent) survival are established. Besides, effects of the important parameter Τ in the model of mature MoA on system performance are showed. Our theoretical analysis and experimental results show that the model that has higher detection efficiency and steadier detection performance than the current models is a good solution to grid intrusion detection.

提出了一种新的基于免疫的动态入侵检测模型（Idid），建立了入侵检测中有关自体、抗原的动态描述方法，提出了免疫细胞的动态耐受概念，并建立了成熟细胞的生命周期以及免疫记忆等的动态模型及其递推方程. 解决了计算机免疫系统中自体、非自体的动态描述问题，并同时有效地克服了传统计算机免疫系统中成熟细胞生成效率较低等缺陷. 对模型进行了仿真，对比实验表明这种新型的入侵检测模型较传统方法具有更好的适应性.

依据人体免疫系统抗体浓度的变化与病原体入侵强度的对应关系，提出了一种基于免疫的网络安全风险检测模型（Insre），给出了网络环境下自体、非自体、抗体、抗原、免疫细胞等的表示方法, 建立了自体演化、抗体基因库、自体耐受、克隆选择、成熟细胞的产生与淘汰机制、动态免疫记忆、免疫监视等的抽象数学模型及相应的递推方程，在此基础上建立了基于抗体浓度的网络安全风险检测的定量计算模型，并给出了其理论推导和证明.利用该模型，可以实时定量地计算出网络当前所面临攻击的类别、数量、强度及风险指标等.理论分析和实验结果表明该方法是网络安全风险在线检测一种有效的新途径.

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 mature 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. Simulations 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.