Повышение эффективности управления в условиях изменения психофизиологического состояния персонала

An approach to improving the methodological apparatus determining psychophysiological operators’ state by information from multimodal automated workstations’ input interface is represented. As a unique behavioral person’s characteristics, reflecting his psychophysiological state, it is proposed to use the jitter of the pitch period of the speech signal, the duration and period of pressing buttons on the keyboard, and the duration of the period of holding the left button "mouse", and its movement signal. The opportunity to combine partial estimates of operator’s psychophysiological state, determined as the proportion of the analyzed signal frames, on which the random jitter’s absolute value exceeds a threshold value, based on Harrington’s generalized function is showed. An example of increasing the efficiency of optimization the restraining engineering and manufacturing functions for operators on 1,6-6,8% in comparison with the known solution and the ability to automate the process of human resource management in gas production and gas transmission companies is showed.Представлен подход к совершенствованию научно-методического аппарата определения психофизиологического состояния операторов по информации от многомодального входного интерфейса автоматизированных рабочих мест. В качестве уникальной поведенческой характеристики человека, отражающей его психофизиологическое состояние, предложено использовать джиттер периода основного тона речевого сигнала, длительности и периода нажатия кнопок на клавиатуре, длительности и периода нажатия левой клавиши «мыши», а также сигнала ее перемещения. Показана возможность объединения частных оценок психофизиологического состояния оператора, определяемых как доля кадров анализируемого сигнала, на которых абсолютное значение случайного джиттера превышает пороговое значение, на основе обобщенной функции Харрингтона.  в условиях изменения ПФС операторов, оцененного на основе разработанного инструментария, является адекватным задаче управления персоналом. Продемонстрирован пример повышения эффективности оптимизации закрепления производственно-технологических функций за операторами на 1,6-6,8% по сравнению с известным решением и возможность автоматизации процесса управления персоналом газодобывающих и газотранспортных предприятий

Fusing multi-layer metrics for detecting security attacks in 802.11 networks

Computer networks and more specifically wireless communication networks are increasingly becoming susceptible to more sophisticated and untraceable attacks. Most of the current Intrusion Detection Systems either focus on just one layer of observation or use a limited number of metrics without proper data fusion techniques. However, the true status of a network, is rarely accurately detectable by examining only one network layer or metric. Ideally, a synergistic approach would require knowledge from various layers to be fused and, collectively, an ultimate decision to be taken. To this aim, the Dempster-Shafer (D-S) approach is examined as a data fusion algorithm that combines beliefs of multiple metrics across multiple layers. This paper describes the methodology of using metrics from multiple layers of wireless communication networks for detecting wireless security breaches. The metrics are analysed and compared to historical data and each gives a belief of whether an attack takes place or not. The beliefs from different metrics are fused with the D-S technique with the ultimate goal of limiting false alarms by combining beliefs from various network layers. The results show that cross-layer techniques and data fusion perform more efficiently in a variety of situations compared to conventional methods

Detecting misbehaviour in WiFi using multi-layer metric data fusion

One of the main problems in open wireless networks is the inability of authenticating the identity of a wireless client or Access Point (AP). This issue is a concern because, a malicious entity could masquerade as the legal AP and entice a wireless client to establish a connection with a Rogue AP. Previous work by the authors has developed the algorithms used in this work but, in contrast to prior work, there was no analysis or experimentation with Rogue AP attacks. Our purpose in this work is to detect injection type of Rogue AP activity by identifying whether a frame is genuinely transmitted by the legal AP or not. To this end, an identity profile for the legal AP is built by fusing multi-layer metrics, using the Dempster-Shafer algorithm. The results show high detection results with low false alarms for detecting Rogue AP attacks without requiring configuration from an administrator. © 2013 IEEE

Dempster-Shafer for Anomaly Detection

In this paper, we implement an anomaly detection system using the Dempster-Shafer method. Using two standard benchmark problems we show that by combining multiple signals it is possible to achieve better results than by using a single signal. We further show that by applying this approach to a real-world email dataset the algorithm works for email worm detection. Dempster-Shafer can be a promising method for anomaly detection problems with multiple features (data sources), and two or more classes

A multi-layer data fusion system for Wi-Fi attack detection using automatic belief assignment

Wireless networks are increasingly becoming susceptible to more sophisticated threats. An attacker may spoof the identity of legitimate users before implementing more serious attacks. Most of the current Intrusion Detection Systems (IDS) that employ multi-layer approach to help towards mitigating network attacks, offer high detection accuracy rate and low numbers of false alarms. Dempster-Shafer theory has been used with the purpose of combining beliefs of different metric measurements across multiple layers. However, an important step to be investigated remains open; this is to find an automatic and self-adaptive process of Basic Probability Assignment (BPA). This paper describes a novel BPA methodology able to automatically adapt its detection capabilities to the current measured characteristics, with a light weight process of generating a baseline profile of normal utilisation and without intervention from the IDS administrator. We have developed a multi-layer based application able to classify individual network frames as normal or malicious

An automatic and self-adaptive multi-layer data fusion system for WiFi attack detection

Wireless networks are becoming susceptible to increasingly more sophisticated threats. Most of the current intrusion detection systems (IDSs) that employ multi-layer techniques for mitigating network attacks offer better performance than IDSs that employ single layer approach. However, few of the current multi-layer IDSs could be used off-the-shelf without prior thorough training with completely clean datasets or a fine tuning period. Dempster-Shafer theory has been used with the purpose of combining beliefs of different metric measurements across multiple layers. However, an important step to be investigated remains open; this is to find an automatic and self-adaptive process of basic probability assignment (BPA). This paper describes a novel BPA methodology able to automatically adapt its detection capabilities to the current measured characteristics, without intervention from the IDS administrator. We have developed a multi-layer-based application able to classify individual network frames as normal or malicious with perfect detection accuracy. Copyright © 2013 Inderscience Enterprises Ltd

Collaborative Intrusion Detection in Federated Cloud Environments

Moving services to the Cloud is a trend that has steadily gained popularity over recent years, with a constant increase in sophistication and complexity of such services. Today, critical infrastructure operators are considering moving their services and data to the Cloud. Infrastructure vendors will inevitably take advantage of the benefits Cloud Computing has to offer. As Cloud Computing grows in popularity, new models are deployed to exploit even further its full capacity, one of which is the deployment of Cloud federations. A Cloud federation is an association among different Cloud Service Providers (CSPs) with the goal of sharing resources and data. In providing a larger-scale and higher performance infrastructure, federation enables on-demand provisioning of complex services. In this paper we convey our contribution to this area by outlining our proposed methodology that develops a robust collaborative intrusion detection methodology in a federated Cloud environment. For collaborative intrusion detection we use the Dempster-Shafer theory of evidence to fuse the beliefs provided by the monitoring entities, taking the final decision regarding a possible attack. Protecting the federated Cloud against cyber attacks is a vital concern, due to the potential for significant economic consequences

In-Process Laser Welding Monitoring by Fusing the Uncertain Signal Information of Multi-Photodiode Sensors

Department of System Design & Control EngineeringRemote laser welding is an emerging joining technology to meet the increasing demand of corrosion resistance, fast, non-contacted and single sided joining for automotive body-in-white assemblies. However, the quality of laser welding has been a critical issue in the popularization of this technology. Traditionally, various stochastic detection methods have been developed for in-process weld defect detection by monitoring and classifying various weld signals. The main objective of this thesis is to develop an in-process welding monitoring system including(i) a novel defect detection algorithm based on a multi-sensor fusion technique, (ii) a new optical sensor configuration to capture in-process weld signal, and (iii) an offline weld signal analysis/training module and an user interactive online detection module. The three weld signals are monitored: weld pool temperature, plasma intensity, and back reflected laser intensity. Their nominal trends are identified by estimating a probability distribution function for the signals and appropriate thresholds are specified by the standard statistical analysis of the residuals at the confidence interval of 95%. We propose a probability assignment function, characterized by shape controllability with respect to the extracted thresholds. We can analyze the in-tolerance defect problems by the proposed probability assignment function that can deal with the decision uncertainty near the thresholds. The individual sensor information is utilized to identify the probability of the normal state. The probabilities are aggregated by using the combination rule of the Dempster-Shafer theory. The performance of the developed detection method is evaluated by the statistical comparison with conventional visual inspection results.clos

A Dempster-Shafer Method for Multi-Sensor Fusion

The Dempster-Shafer Theory, a generalization of the Bayesian theory, is based on the idea of belief and as such can handle ignorance. When all of the required information is available, many data fusion methods provide a solid approach. Yet, most do not have a good way of dealing with ignorance. In the absence of information, these methods must then make assumptions about the sensor data. However, the real data may not fit well within the assumed model. Consequently, the results are often unsatisfactory and inconsistent. The Dempster-Shafer Theory is not hindered by incomplete models or by the lack of prior information. Evidence is assigned based solely on what is known, and nothing is assumed. Hence, it can provide a fast and accurate means for multi-sensor fusion with ignorance. In this research, we apply the Dempster-Shafer Theory in target tracking and in gait analysis. We also discuss the Dempster-Shafer framework for fusing data from a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) sensor unit for precise local navigation. Within this application, we present solutions where GPS outages occur