基于自适应直觉模糊推理的入侵检测方法

将直觉模糊集理论引入信息安全领域,提出一种基于自适应直觉模糊推理的入侵检测方法。首先,分析现有入侵检测方法的特点与局限性,建立基于自适应神经—直觉模糊推理系统（ANIFIS）的Takagi-Sugeno型入侵检测模型。其次,设计系统的推理规则,确定各层输入输出的计算关系,以及系统输出结果的计算表达式。再次,设计网络学习算法,对网络结构进行调节以及对网络参数进行学习。最后,选择KDD CUP 99入侵检测数据集作为样本集,获得相应的检测结果,验证了方法的有效性和模型的正确性。     

基于模糊控制技术的分布式入侵检测系统

模糊控制技术在工业控制等众多领域已得到了广泛的应用,其理论和技术已经较为成熟。为进一步提高网络系统的安全性,将模糊控制理论应用于分布式入侵检测系（distributed intrusion detection system,DIDS）中,并设计出具体的应用数据模型。通过模糊控制技术的引入,可有效提高分布式入侵检测系统的预报可信度和系统的辅助决篆能力。     

Uniformly ultimately bounded fuzzy adaptive tracking controllers for uncertain systems

Fuzzy adaptive tracking controllers for a class of uncertain nonlinear dynamical systems are proposed and analyzed. The controllers consist of adaptive and robustifying components whose role is to ify the effects of uncertainties and to achieve a desired tracking performance. The interactions between the two components have been investigated. The closed-loop system driven by the proposed controllers is shown to be stable with all the adaptation parameters being bounded. In particular, the proposed controllers guarantee uniform ultimate boundedness of the tracking error and the time bound of the uniform ultimate boundedness is obtained. An upper bound on the steady-state tracking error is obtained as a function of the gain of the robustifying term and the parameters of the adaptive component. The controllers are tested on an inverted pendulum and simulation results are included. A comparison of the proposed controllers with the ones in the literature is conducted.     

Synthesis of supervisory controllers for hybrid systems based onapproximating automata

The paper concerns the synthesis of supervisory controllers for a class of continuous-time hybrid systems with discrete-valued input signals that select differential inclusions for continuous-valued state trajectories and event-valued output signals generated by threshold crossings in the continuous state space, the supervisor is allowed to switch the input signal value when threshold events are observed. The objective is to synthesize a nonblocking supervisor such that the set of possible sequences of control and threshold event pairs for the closed-loop system lies between given upper and lower bounds in the sense of set containment. We show how this problem can be converted into a supervisor synthesis problem for a standard controlled discrete-event system (DES). A finite representation may not exist for the exact DES model of the hybrid system, however. To circumvent this difficulty, we present an algorithm for constructing finite-state Muller automata that accept outer approximations to the exact controlled threshold-event language, and we demonstrate that supervisors that solve the synthesis problem for the approximating automata achieve the control specifications when applied to the original hybrid system     

Stable auto-tuning of hybrid adaptive fuzzy/neural controllers for nonlinear systems

In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, “approximators” such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as either the parameterized nonlinear controller or identifier model. In this paper, we present an algorithm to tune the adaptation gain for a gradient-based hybrid update law used for a class of nonlinear continuous-time systems in both direct and indirect cases. In our proposed algorithm, the adaptation gain is obtained by minimizing the instantaneous control energy. Finally, we will demonstrate the performance of the algorithm via a wing rock regulation example.     

Stable auto-tuning of adaptive fuzzy/neural controllers for nonlinear discrete-time systems

In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, "approximators" such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as either the parameterized nonlinear controller or identifier model. In this paper, we present algorithms to tune some of the parameters (e.g., the adaptation gain and the direction of descent) for a gradient-based approximator parameter update law used for a class of nonlinear discrete-time systems in both direct and indirect cases. In our proposed algorithms, the adaptation gain and the direction of descent are obtained by minimizing the instantaneous control energy. We will show that updating the adaptation gain can be viewed as a special case of updating the direction of descent. We will also compare the direct and indirect adaptive control schemes and illustrate their performance via a simple surge tank example.     

Supervision of nonlinear adaptive controllers based on fuzzy models

A novel approach for the supervision of fuzzy model on-line adaptation is proposed. A nonlinear predictive controller is designed based on a Takagi–Sugeno fuzzy model. By adapting the fuzzy model on-line, high control performance can be achieved even with time-variant process behaviour and changing unmodelled disturbances. A local weighted recursive least-squares algorithm exploits the local linearity of Takagi–Sugeno fuzzy models. In order to cope with problems resulting from insufficient excitation, a supervisory level is introduced. It comprises a variable forgetting factor and an additional adaptation model which makes the on-line adaptation robust and reliable. The effectiveness and real-world applicability of the proposed approach are demonstrated by application to temperature control of a heat exchanger.     

A two-stage design of adaptive fuzzy controllers for time-delay systems with unknown models

In this article, a systematic two-stage design method for adaptive fuzzy controllers is presented. The proposed control scheme has low computational complexity. Moreover, the exact mathematical model of the plant to be controlled is not required. The fuzzy controller under consideration is based on the proportional-derivative fuzzy control scheme and triangular membership functions. In the design procedure, the domain intervals of the input and output variables are selected with a heuristic approach to minimize a cost function under the constraint of tolerable overshoots in the response curve. A learning scheme is then proposed to automatically adjust the parameters in the fuzzy controller to reduce the error of the system. It can also be used adaptively to improve the system performance of a time-varying system. Simulations and comparisons are included to demonstrate the effectiveness of the proposed method.     

基于孤立点检测的自适应入侵检测技术研究

传统的入侵检测技术主要是从已知攻击数据中提取出每种具体攻击的特征规则模式,然后使用这些规则模式来进行匹配。然而基于规则的入侵检测的主要问题是现有的规则模式并不能有效应对持续变化的新型入侵攻击。针对这一问题,基于数据挖掘的入侵检测方法成为了入侵检测技术新的研究热点。本文提出了一种基于孤立点挖掘的自适应入侵检测框架,首先,基于相似系数寻找孤立点,然后对孤立点集合进行聚类,并使用改进的关联规则算法来从孤立点聚类结果中提取出各类入侵活动的潜在特征模式,然后生成可使用的匹配规则模式来添加到现有的规则模式中去,进而达到自适应的目的。本文使用KDD99的UCI数据集进行孤立点挖掘,然后使用IDS Snort的作为实验平台,使用IDS Informer模拟攻击工具进行测试,这两个实验结果表明了本文所提出算法的有效性。     

Fault detection and isolation based on fuzzy automata

Fuzzy automata are proposed for fault diagnosis. The output of the monitored system is partitioned into linear segments which in turn are assigned to pattern classes (templates) with the use of membership functions. A sequence of templates is generated and becomes input to fuzzy automata which have transitions that correspond to the templates of the properly functioning system. If the automata reach their final states, i.e. the input sequence is accepted by the automata with a membership degree that exceeds a certain threshold, then normal operation is deduced, otherwise, a failure is diagnosed. Fault diagnosis of a DC motor and detection of abnormalities in the ECG signal are used as case studies.     

一种进化半监督式模糊聚类的入侵检测算法

在入侵检测系统中,未知标签数据容易获得,标签数据较难获得,对此提出了一种基于进化半监督式模糊聚类入侵检测算法。算法利用标签数据信息担任染色体的角色,引导非标签数据每个模糊分类的进化过程,能够使用少量的标签数据和大量未知标签数据生成入侵检测系统分类器,可处理模糊类标签,不易陷入局部最优,适合并行结构的实现。实验结果表明,算法有较高的检测率。     

Schedulability analysis of fixed-priority systems using timed automata

In classic scheduling theory, real-time tasks are usually assumed to be periodic, i.e. tasks are released and computed with fixed rates periodically. To relax the stringent constraints on task arrival times, we propose to use timed automata to describe task arrival patterns. In a previous work, it is shown that the general schedulability checking problem for such models is a reachability problem for a decidable class of timed automata extended with subtraction. Unfortunately, the number of clocks needed in the analysis is proportional to the maximal number of schedulable task instances associated with a model, which is in many cases huge. In this paper, we show that for fixed-priority scheduling strategy, the schedulability checking problem can be solved using standard timed automata with two   extra clocks in addition to the clocks used in the original model to describe task arrival times. The analysis can be done in a similar manner to response time analysis in classic Rate-Monotonic Analysis (RMA). The result is further extended to systems with data-dependent control, in which the release time of a task may depend on the time-point at which other tasks finish their execution. For the case when the execution times of tasks are constants, we show that the schedulability problem can be solved using n+1$n+1$ extra clocks, where n$n$ is the number of tasks. The presented analysis techniques have been implemented in the Times tool. For systems with only periodic tasks, the performance of the tool is comparable with tools implementing the classic RMA technique based on equation-solving, without suffering from the exponential explosion in the number of tasks.     

Verification of continuous dynamical systems by timed automata

This paper presents a method for abstracting continuous dynamical systems by timed automata. The abstraction is based on partitioning the state space of a dynamical system using positive invariant sets, which form cells that represent locations of a timed automaton. The abstraction is intended to enable formal verification of temporal properties of dynamical systems without simulating any system trajectory, which is currently not possible. Therefore, conditions for obtaining sound, complete, and refinable abstractions are set up.     

High-gain robust adaptive controllers for multivariable systems

This paper extends the switching free high-gain stabilizing adaptive control rules of Byrnes and Willems to a wide class of adaptive schemes capable of tolerating nonlinear state feedback perturbations.     

模糊逻辑理论在入侵检测系统中的应用研究

通过基于模糊逻辑的数据挖掘方法实现特征选择,使用模糊逻辑推理进行数据分析,以及支持响应回卷的模糊默认逻辑推理处理预警响应,使得入侵检测系统在特征选择和预警响应方面得到改善。实验结果显示,该检测方法能够有效检测入侵攻击,具有较低的误报率和漏报率。     

Recurrent learning algorithms for designing optimal controllers ofcontinuous systems

Proposes a recurrent learning algorithm for designing the controllers of continuous dynamical systems in optimal control problems. The controllers are in the form of unfolded recurrent neural nets embedded with physical laws from classical control techniques. The learning algorithm is characterized by a double forward-recurrent-loops structure for solving both temporal recurrent and structure recurrent problems. The first problem results from the nature of general optimal control problems, where the objective functions are often related to (evaluated at) some specific time steps or system states only, causing missing learning signals at some steps or states. The second problem is due to the high-order discretization of continuous systems by the Runge-Kutta method that we perform to increase accuracy. This discretization transforms the system into several identical interconnected subnetworks, like a recurrent neural net expanded in the time axis. Two recurrent learning algorithms with different convergence properties are derived; first- and second-order learning algorithms. Their computations are local and performed efficiently as net signal propagation. We also propose two new nonlinear control structures for the 2D guidance problem and the optimal PI control problem. Under the training of the recurrent learning algorithms, these controllers can be easily tuned to be suboptimal for given objective functions. Extensive computer simulations show the controllers' optimization and generalization abilities     

基于PSO模糊聚类算法的入侵检测研究

提出一种将粒子群优化（PSO）和FCM 相结合的聚类算法PSOFCA对入侵检测系统进行研究,克服FCM方法自身对初始值敏感、容易陷入局部最优等问题。最后对实验数据进行仿真实验,并将实验结果与其他算法结果相比较,结果表明PSOFCA算法在入侵检测中能获得较好的检测能力。     

基于规则插入的自适应模糊控制器的设计

提出一种为模糊控制器系统开发有效规则的自适应模糊控制器的设计方法.该方法首先基于两个基本的If-then模糊规则,然后插入多个新If_then模糊规则,其合成的模糊规则的输入_输出映射保持不变,且其隶属函数的参数在线约束为最小化成本函数,最后以最典型的非线性和延迟系统模型为例进行仿真实验,结果证明了该设计方法的有效性.     

A survey of timed automata for the development of real-time systems

Timed automata are a popular formalism to model real-time systems. They were introduced two decades ago to support formal verification. Since then they have also been used for other purposes and a large number of variants has been introduced to be able to deal with the many different kinds of requirements of real-time system development. This survey attempts to introduce a massive and complicated theoretical research area to a reader in an easy and compact manner. One objective of this paper is to inform a reader about the theoretical properties (or capabilities) of timed automata which are (or might be) useful for real-time model driven development. To achieve this goal, this paper presents a survey on semantics, decision problems, and variants of timed automata. The other objective of this paper is to inform a reader about the current state of the art of timed automata in practice. To achieve the second aim, this article presents a survey on timed automata’s implementability and tools.     

A method for the modular synthesis of controllers for timed discrete-event systems

A method for the modular supervisory control of timed discrete-event systems (TDES) is presented. The modular synthesis method is an extension of the centralized synthesis method proposed in our earlier work. We consider a state predicate specification as a conjunction of several state subpredicate specifications. The control problem is to synthesize a modular controller, the conjunction of all individual controllers, in such a way that the closed-loop behaviour of TDES satisfies the state predicate specification. Our modular synthesis method is developed based on the concept of state space of TDES, the notion of control-invariant state predicates for the TDES and a fixed point algorithm to calculate a control-invariant state subpredicate of a given state predicate. In addition, for the development of our modular synthesis method, we introduce the notion of control-invariance non-conflict among control-invariant state predicates, and the notion of forcing-non-conflict among controllers synthesized based on control-invariant state predicates which are control-invariance non-conflicting. The modular synthesis method in general offers better design flexibility and may require fewer computations than the centralized one. As in our centralized synthesis method, the proposed modular synthesis method does not require the construction and examination of complete sequences of event trajectories of the system. It is suggested that the computation of our proposed method of yielding solutions for a class of synthesis problems in TDES can be economical.