水闸防侵入探测报警驱离系统

水闸管理区内频繁进入运输船舶,停靠,上、卸物资,威胁水工建筑物安全。利用漫反射传感器和红外对射传感器探测进入禁区的船舶,启动声光警报,驱离侵入船舶,以达到保护水工建筑物安全的目的。

防侵入探测器电路设计

防侵入探测器在安全监控中有着广泛的应用,一般通过红外传感器检测。本文介绍了一种通过检测人体-感应板电容变化而有效感知人体靠近的一种防侵入探测器电路。该电路采用一块敷铜板作为电容器的一个极板构成感应面。当人体靠近时成为另一个极板,产生一个随人体接近而成比例增加的电容值(2-5pF)。当人体靠近到一定程度时,便会产生报警信号。

侵入探测安全集装箱通过首次商用测试

日前，全球首屈一指的IT服务及技术公司Unisys，作为系统集成商及观察员，协助美国通用电气公司下属安全业务部门完成了“侵入探测安全集装箱”(TESC)的首次商业应用实地测试。

一种用于电气作业的智能保护装置

传统电气作业现场保护设施缺乏智能化的监管和预警。基于主动红外侵入探测、热释红外和超声波测距、ZigBee无线通信等电路设计了一种模块化的智能保护装置,整套装置具有预报告警和越线告警功能。热释红外与超声波实现对作业区域外来因素误入的预判,主动红外探测构成越线报警,结合RFID技术,工作人员可实时管理作业现场。提出的方案符合实时性控制要求,装置能可靠、有效地保护电气作业区域人员安全,可实现现场作业保护装置的智能化。

Intrusion detection using rough set classification

Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

A graph based system for multi-stage attacks recognition

Building attack scenario is one of the most important aspects in network security.This paper pro-posed a system which collects intrusion alerts,clusters them as sub-attacks using alerts abstraction,ag-gregates the similar sub-attacks,and then correlates and generates correlation graphs.The scenarios wererepresented by alert classes instead of alerts themselves so as to reduce the required rules and have the a-bility of detecting new variations of attacks.The proposed system is capable of passing some of the missedattacks.To evaluate system effectiveness,it was tested with different datasets which contain multi-stepattacks.Compressed and easily understandable Correlation graphs which reflect attack scenarios were gen-erated.The proposed system can correlate related alerts,uncover the attack strategies,and detect newvariations of attacks.

INTRUSION DETECTION BASED ON THE SECOND-ORDER STOCHASTIC MODEL

This paper presents a new method based on a second-order stochastic model for computer intrusion detection.The results show that the performance of the second-order stochastic model is better than that of a first-order stochastic model.In this study,different window sizes are also used to test the performance of the model.The detection results show that the second-order stochastic model is not so sensitive to the window size,comparing with the first-order stochastic model and other previous researches.The detection result of window sizes 6 and 10 is the same.

An entropy-based unsupervised anomaly detection pattern learning algorithm

Currently, most anomaly detection pattern learning algorithms require a set of purely normal data from which they train their model. If the data contain some intrusions buried within the training data, the algorithm may not detect these attacks because it will assume that they are normal. In reality, it is very hard to guarantee that there are no attack items in the collected training data. Focusing on this problem, in this paper, firstly a new anomaly detection measurement is proposed according to the probability characteristics of intrusion instances and normal instances. Secondly, on the basis of anomaly detection measure, we present a clustering-based unsupervised anomaly detection patterns learning algorithm, which can overcome the shortage above. Finally, some experiments are conducted to verify the proposed algorithm is valid.

Real-Time Distributed Fiber Optic Sensor for Security Systems： Performance, Event Classification and Nuisance Mitigation

The success of any perimeter intrusion detection system depends on three important performance parameters： the probability of detection （POD）, the nuisance alarm rate （NAR）, and the false alarm rate （FAR）. The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system, and the reliability of the sensing equipment. The suppression of nuisance alarms without degrading sensitivity in fiber optic intrusion detection systems is key to maintaining acceptable performance. Signal processing algorithms that maintain the POD and eliminate nuisance alarms are crucial for achieving this. In this paper, a robust event classification system using supervised neural networks together with a level crossings （LCs） based feature extraction algorithm is presented for the detection and recognition of intrusion and non-intrusion events in a fence-based fiber-optic intrusion detection system. A level crossings algorithm is also used with a dynamic threshold to suppress torrential rain-induced nuisance alarms in a fence system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100mm/hr with the simultaneous detection of intrusion events. The use of a level crossing based detection and novel classification algorithm is also presented for a buried pipeline fiber optic intrusion detection system for the suppression of nuisance events and discrimination of intrusion events. The sensor employed for both types of systems is a distributed bidirectional fiber-optic Mach-Zehnder （MZ） interferometer.