The integration of networks within an organization made many critical infrastructures (CI) and their underlying communication networks that were rather isolated in the past, accessible from outside via internet. CI heavily rely on the security of their supervisory control and data acquisition (SCADA) systems. As attackers are using ever more sophisticated technologies the threats are always increasing. Therefore it is important to detect attacks quickly and react efficiently to them, thus increasing reliability, security and resilience of the system. To specify a model of security events, attacks and vulnerabilities, we propose an ontology. The system logs provide the events, which the intrusion detection systems (IDS) may recognize as suspicious and could be part of an attack. With the help of data bases for known vulnerabilities together with the system model ongoing attacks may be identified. The ontology-framework together with a respective reasoning component forms the common ground for compliance monitoring and correlation of security events and serves as a basis for the specification and implementation of security data normalization. Then security policies (or goals) can be refined into implementable configurations on critical infrastructure network devices.
Fingerprint detection is one of the primary methods for identifying individuals. Gray Level Co-occurrence Matrix (GLCM) is the oldest and prominent statistical textual feature extraction method applied in many fields for texture analysis. GLCM holds the distribution of co-occurring intensity patterns at a given offset over a given image. However, images occupy excessive space in storage by its original sizes. Thus, Discrete Wavelet Transform (DWT) based compression has become popular especially for reducing the size of the fingerprint images. It is important to investigate whether GLCM-based classification can be utilized efficiently on DWT-compressed fingerprint images. In this paper, we analyze the performance of GLCM-based classification on DWT-compressed fingerprint images. We performed satisfying simulations for different levels of DWT-compressed images. Simulation results identify that classification performance sharply decreases by the increase of DWT-compression level. Besides, instead of utilizing all Haralick features, it is recognized that 8 of them are the most prominent ones that affect the accuracy performance of the classification.
According to the Symantec threat report the total number of new malware variants added in 2013 and 2014 were 252 millions and 317 millions (a 26% increase from 2013) respectively. Mobile malware development in 2013 and 2014 continues to focus exclusively (~99%) on the Android platform. For detecting malware, if parts of a malware family match parts of a program then this provides us a strong evidence that the program is/contain a malware. Based on this hypothesis, we propose DroidClone that exposes code clones (segments of code that are similar) in Android applications to help detect malware variants. DroidClone uses a new Malware Analysis and Intermediate Language (MAIL) for finding code clones in Android applications. MAIL helps DroidClone to use specific control flow patterns for reducing the effect of obfuscations and provides automation and platform independence. Unlike other works DroidClone is able to detect both bytecode and native code Android malware variants. When tested with traditional malware variants it achieves a detection rate (DR) of 97.85%, compared to the other two works DroidSim and NiCad that achieved a DR of 89.62% and 83.11% respectively.
The combination of smart cards and biometrics results in a strong 2-factor authentication. It facilitates a trusted credential for authenticating an individual's identity through one-to-one biometric verification using the smart card token. Fingerprint Authentication Systems (FASs) embedded in smart cards are gaining massive acceptance as it offers a superior level of security when compared to the conventional Match-on-Device (MOD) and Match-on-Server (MOS) technologies. The fundamental difference between the conventional FAS authentication process and FAS using smart card process is all about the authentication location. FAS authentication using smart card token is possible through four authentication approaches. They are the Template-on-Card (TOC), Match-on-Card (MOC), Work-Sharing On-Card (WSOC), and System-on-Card (SOC). Out of these four approaches, the SOC is considered as the most secure and expensive, whereas the TOC is considered as the least secure and least expensive. The MOC scheme offers a higher level of security than the TOC scheme at an affordable cost when comparing to SOC and is thus the most practical solution available today. Although this is the case, the MOC scheme is also susceptible to the inherent security vulnerabilities associated with biometric modalities in general. The front runner of the biometric vulnerabilities being the compromise of the original finger templates. This paper focuses on this critical vulnerability aspect and conceptualizes a novel Match-on-Card Fingerprint Authentication System. The proposed system does not need to store or transfer the original fingerprint template to/from the smart card and therefore facilitates an enhanced security than the conventional MOC approach.
Teledentistry is a combination of dentistry science, Information Technology and Telecommunication services. This science involves the exchange of clinical information and image for distance places by using internet to achieve consulting and treatment plan. Teledentistry enables us to improve our access to oral health delivery services and also it will help to reduce the costs. Teledentistry could be resolved the disparities between urban and rural areas in oral health. In this article, we decided to evaluate the impact of information technology on dentistry in our country Iran. Teledentistry will also review in terms of its innovative ideas and its future in providing dental care. As most of time accepting new technology is difficult for people, we decide to simulate the trend of teledentistry acceptance versus traditional method. For this we use MATLAB software to simulate the Dental Network with Poisson degree distribution and getting numerical and analytical result. This simulation guide us to use some politicians to content dentist to use teledentistry.
This paper presents an approach for designing of JPEG2000 video encoder board based on firmware programmable SRAM-style hardware CODEC. Typical, mostly used, electro optical sensor output signal and its basic characteristics are given and then signal-data size relationships are explained. Later, some fundamental information about JPEG2000 standard is summarized. System design blocks and implementation details are examined. Finally; analysis of real time behavior of such a system on combat system video network (CSVN) evaluated.
Traditional video surveillance takes a huge amount of space storage. Recording everything captured by a surveillance camera consumes the storage devices used by the system. Extracting useful and meaningful information from surveillance videos is a time consuming process due to the long time of the recorded videos. These drawbacks limit the effectiveness of traditional video surveillance systems. In this paper, we propose and elaborate on a compression method which investigates the fact that surveillance videos may last for a long time with no changes in the scene it monitors. Using this fact, a new compression technique that reduces the size of the videos dramatically was developed. We also present a dataset for low quality surveillance videos which can be used by researchers for applying different algorithms and techniques in the field of surveillance videos.
Path planning problem (PPP) is a challenging problem in several real-life applications and needs to be developed and implemented in a suitable manner. For that purpose we designed an efficient Genetic Algorithm (GA) for modeling and solving PPP. The proposed GA exploits an adequate solution representation and a smart crossover operator, and introduces a significant fitness function involving several criteria of robot motion. We conducted a comprehensive experiments of the proposed algorithm and we compared it against two different approaches of the literature: Genetic Algorithm and Tabu search algorithms. Simulation results show the efficiency and effectiveness of our genetic algorithm in comparison with the other planners in term of solution quality and execution time.
mHealth is new horizons for health that offers healthcare services by utilizing the mobile devices and communication technologies. In health care services, blood donation is a complex process and consumes time to find some donor who has the compatibility of blood group with the patient. We developed android based blood donation application as mHealth solutions to establish a connection between the requester and donor at anytime and anywhere. The objective of this application is to provide the information about the requested blood and number of available donors around those localities. It assists the requester to broadcast the message across the maintained volunteer blood donor network by our application and update the requester at the same time who is willing to donate the requested blood. To evaluate our application, we created requester-donor profiles and analysed that it will help to improve the timely access of the information and rapid response in emergency situation.
In a sophisticated and coordinated cyber-attack $100 million has been stolen from Bangladesh's account. Attackers introduced malicious code remotely into the Bangladesh Bank's server, which allowed them to process and authorize the transactions. Advanced attack techniques poses threats to all web application systems. Cross Site Scripting (XSS) and Cross Site Request Forgery (CSRF) are two vulnerabilities which have techniques that are similar to those of the Bangladesh Bank heist. XSS and CSRF are third and eighth of the top ten web application vulnerabilities on OWASP list from 2013 till now. Both these attacks violate the users trust for the websites and web browsers. Because of the severity of these vulnerabilities, security specialists have always shared their concern and warned the web developers. Yet Bangladesh government's and developers' reluctance to address the severity of the attacks resulted in Bangladesh Bank heist. In this paper, we aim to study and conduct an investigation of the vulnerabilities of similar attacks as these of the Bangladesh Bank heist on web applications of Bangladesh. We would focus on XSS and CSRF vulnerabilities due to their high ranking on the OWASP list. We analyze the data collected during the investigation and provide a summary of the current state and a guideline for the future web developers.
Graph matching is a fundamental graph theory problem which has a broad application range including information retrieval, pattern recognition, graph partitioning, chemical structure analysis, protein function prediction, backup placement and cellular coverage. This problem has gained attention in distributed computing as there are distributed matching algorithms with asymptotically guaranteed time bounds and approximation ratios. On the other side, we do not know the practical performance of these algorithms. In this paper, we provide a detailed performance evaluation of asynchronous distributed maximum weighted matching (MWM) algorithms. We assume a message-passing system in CONGEST model in which the message size is limited to O(log n) where n is the number of nodes. This model is popular for energy-efficient networks such as wireless sensor networks. We used a discrete event simulator, SimPy, to model the assumed network structures. We provide the implementations of Watthenhofer and Wattenhofer's algorithm, Hoepman's algorithm, Lotker et al.'s algorithm and Lotker et al.'s improvement algorithm. The results show that the greedy algorithm of Hoepman performed best in approximating the optimum result in all types of networks, even achieving an approximation ratio of 0.99 in some instances. To the best of our knowledge this is the first study which provides an extensive performance evaluation of distributed MWM algorithms.
Regular physical activity are vital for prevention and treatment of chronic diseases, which are highly prevalent among older workers aged 50 years old and above. However, maintaining active lifestyle was challenging for older workers due to prolong hours of sedentary behaviour at workplace. In this study, we attempt to identify design requirements of a persuasive smartphone application aiming to encourage physical activity at workplace among older workers. We applied a triangulation of user-centred design techniques that includes think-aloud, parallel prototyping and participatory design methods aiming to identify any usability problems, functionality issues, privacy concern, information sharing and visualization. Our older workers indicate that contextual awareness, visibility of cause-and-effect relationship, accuracy of collected data, information sharing and privacy control are highly imperative to be integrated into the design of smartphone-based intervention targeting at enhancing their physical activity behaviour in workplace environment.
This paper describes a study that investigated the use of d-touch markers in designing learning content for knowledge sharing activities. It looked into design aspects children takes into account in ensuring that the learning content would be understood by their peers. It is believed that emerging design syntax, placement and accompanying visual cues within the markers helped in perceiving the learning content. This paper demonstrates children's view on the use of d-touch markers where they designed and used them within a Malaysian elementary school learning environment
The fast and wide advancement of the Internet of Things (IoT) boosted by the remarkable development of sensor networks, RFID, software platforms and the Internet as a global container of IoT, have opened the appetite of several vertical industries to include IoT in their strategic vision for the coming years. Academic institutions have also established projects to vigorously investigate the potential of IoT and develop new protocols in all layers to suit IoT applications and/or adapt the current IP protocols to suit it. Some organizations have already implemented IoT and others have plans to implement it according to Gartner Inc. study. Healthcare industry is no exception, have already adopted IoT, and are planning to use it extensively in the following years for the benefits of patients, elderly people and caregivers. In this research paper, we develop a system architecture to manage IoT for the smart healthcare. For IoT in healthcare to succeed, it should be reliable, effective, well performing, secure and profitable for caregivers. This will guarantee continued operation and service delivery for patients and elderly to avoid any health risks.
Aiming to solve the track refined tracking problem of the formation targets with the multi-sensor detections, intensive analysis on target echo characteristics of the formation targets, the centralized multi-sensor particle filter algorithm of formation targets are advanced. The algorithm set up a shape vector, which can represent the graph of each target in formation using shape and azimuth descriptor, then based on the measurement set to fall within the formation each target door of the waves, that associated to all graphics within each target formation might be associated measurement configuration for the object, through establish similarity model through space and shape similarity measurement step with the target state prediction value distance, at the same time, using the idea of selecting the master station to remove redundant images. Finally, achieve each formation target status updating, which based on particle filter using measurement set and the corresponding weight set. The analysis results of the simulation data show, this algorithm has obvious advantages in tracking accuracy, real-time performance and effective tracking rate three aspects, which can meet the engineering requirement of the track refined tracking of the formation targets with multi-sensor detections very well, is better than the algorithm based on data compression centralized multi-sensor multi-algorithm hypothesis which has superior performance in the traditional multi-sensor multi-target tracking algorithm.
Aiming to solve the track anti-bias refined correlation problem of the group targets, based on the characteristics of the group tracks and the fussy topology theory, an algorithm of track anti-bias refined correlation of group targets based on double fussy topology is proposed. In this algorithm, the group identification of each sensor is obtained based on the cyclic threshold model firstly. And then the group center is used to replace the whole group, the first fussy topology model is established with deeply analysis the effect of system error on the track of group center, that is to say the pre-interconnection of group tracks and alignment of common target tracks are completed. At last, the second fussy topology model is established by the topological relations between the pre-interconnection of group tracks and correlated tracks, in order to realize track refined correlation of the group targets. The analysis results of the simulation data shows that the general performance of this algorithm, which can meet the engineering requirement of the track anti-bias refined correlation of the group targets very well, is better than that of fuzzy track alignment-correlation algorithm based on target invariant ingroup, track alignment-correlation algorithm based on iterative closest track and modified weighted track correlation algorithm.
A number of automated techniques for classification of plants based on their leaves have been developed over the past few years. While each of those techniques have been individually implemented and evaluated, but there have been few studies which have made a direct comparison between the various techniques. In this paper we compared the three well-known techniques. We compared their ability to differentiate among plant species. Techniques which are evaluated are, Histogram of Oriented Gradient (HOG),Colour Scale Invariant Feature Transform (C-SIFT) and Maximally Stable Extremal Region (MSER).These techniques are evaluated against two kinds of leaf datasets, one is our personal built dataset and the other is famous Flavia dataset. The experimental results shows that HOG has an accuracy of 98% on our dataset and 97% for Flavia dataset. Moreover for C-Sift the accuracy for both datasets is 98% and for MSER the accuracy is 96% and 90% for our and Flavia dataset respectively.
Nowadays, map images are widely used for many applications. To be well exploited, theses maps generally need a contour extraction operation. However, gaps may result from this phase, which would give rise to the need for reconstructing the missing parts. Map-contour gaps can be caused from low-maps quality or from the contour extraction process. This problem cannot be dealt with simple traditional contour extractors when these gaps are relatively large. This paper proposes a discrete PSO for damaged contour matching in geographical maps. The proposed approach has been validated by applying it on a set of genuine maps.
For many years, dogs are used in the Search And Rescue (SAR) missions. The first recorded use of dogs in SAR missions was in the late 1700. Dogs are used in the SAR missions because their abilities to detect human scent. In many natural disasters or building collapses, the SAR dogs are used to reach and search places that are dangerous for people to be there. In these situations, the members of the SAR team face a problem in controlling the movements of dogs. They can not control the navigation and movements of dogs to direct them to search, reach, or explore specific areas. In this paper, a novel system for controlling dogs using a computer controlled laser system and four sensors is presented. The system depends on the ability of dogs with minor training to chase a laser beam and the abilities of the new emerging technologies of Internet of things. By controlling the orientation of the laser beam, the movements of dogs can be controlled so that the system facilitates and increases the efficiency of using dogs in several tasks.
Heterogeneous networks are expected to become prevalent in the near future due to introduction of close proximity communication in micro cells in conjunction with large-scale coverage by macro cells. In two-tier heterogeneous networks, it is important to analyze performance degradation in macro cells caused by micro cells. For that reason, we analyze the interference to macro cell caused by micro cell single-hop networks where micro cells have QoS requirement, i.e., minimum average requirement of each micro cell. The resource allocation policies considered here are joint power control and node scheduling for downlink and uplink scenarios. We assume that each micro cell operates in orthogonal channels, i.e., no inter-cell interference between micro cells. In this paper, we use extreme value theory to provide scaling laws for the interference to macro cell, when the number of nodes per micro cell N is allowed grows large. For downlink transmission, interference scales according to 1/log(N) rule. A key result is that 1/log(N) scaling behavior exhibited in downlink transmission is transformed into a 1/N behavior in uplink transmission due to additional diversity introduced in interference channel, i.e., the channel between transmitter and macro cell base station.
