Search results for: Intrusion Detection Information Exchange

Authors: Wong Hock Tsen

Abstract:

This study aims to investigate the impact of exchange rate volatility on real export in Malaysia. The moving standard deviation with order three (MSD(3)) is used for the measurement of exchange rate volatility. The conventional and partially asymmetric autoregressive distributed lag (ARDL) models are used in the estimations. This study finds exchange rate volatility to have significant impact on real total export and some sub-categories of real total export. Moreover, this study finds that the positive or negative exchange rate volatility tends to have positive or negative impact on real export. Exchange rate volatility can be harmful to export of Malaysia.

Keywords: Exchange rate volatility, autoregressive distributed lag, export, Malaysia.

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Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

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In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of WorldView-2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows with accuracy of 94% effectively and automatically. Furthermore, the new shadow detection index improved road extraction from 82% to 93%.

Keywords: Spectral index, shadow detection, remote sensing images, WorldView-2.

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Authors: Guezouli Larbi, Belkacem Soundes

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The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered as a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy.

Keywords: Text detection, CNN, PZM, deep learning.

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Authors: Natarajan Venkatachalam, Subrahmanya V. R. K. Rao, Vijay Karthikeyan Dhandapani, Swaminathan Saravanavel

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The quantum communication technology is an evolving design which connects multiple quantum enabled devices to internet for secret communication or sensitive information exchange. In future, the number of these compact quantum enabled devices will increase immensely making them an integral part of present communication systems. Therefore, safety and security of such devices is also a major concern for us. To ensure the customer sensitive information will not be eavesdropped or deciphered, we need a strong authentications and encryption mechanism. In this paper, we propose a mutual authentication scheme between these smart quantum devices and server based on the secure exchange of information through quantum channel which gives better solutions for symmetric key exchange issues. An important part of this work is to propose a secure mutual authentication protocol over the quantum channel. We show that our approach offers robust authentication protocol and further our solution is lightweight, scalable, cost-effective with optimized computational processing overheads.

Keywords: Quantum cryptography, quantum key distribution, wireless quantum communication, authentication protocol, quantum enabled device, trusted third party.

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Authors: Roohi Shabrin S., Devi Prasad B., Prabu D., Pallavi R. S., Revathi P.

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Due to memory leaks, often-valuable system memory gets wasted and denied for other processes thereby affecting the computational performance. If an application-s memory usage exceeds virtual memory size, it can leads to system crash. Current memory leak detection techniques for clusters are reactive and display the memory leak information after the execution of the process (they detect memory leak only after it occur). This paper presents a Dynamic Memory Monitoring Agent (DMMA) technique. DMMA framework is a dynamic memory leak detection, that detects the memory leak while application is in execution phase, when memory leak in any process in the cluster is identified by DMMA it gives information to the end users to enable them to take corrective actions and also DMMA submit the affected process to healthy node in the system. Thus provides reliable service to the user. DMMA maintains information about memory consumption of executing processes and based on this information and critical states, DMMA can improve reliability and efficaciousness of cluster computing.

Keywords: Dynamic Memory Monitoring Agent (DMMA), Cluster Computing, Memory Leak, Fault Tolerant Framework, Dynamic Memory Leak Detection (DMLD).

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Authors: Berna Ulutas

Abstract:

Single Minute Exchange of Dies (SMED) mainly focuses on recognition of internal and external activities. It is concerned particularly with transferring internal activities into external ones in as many numbers as possible, by also minimizing the internal ones. The validity of the method and procedures are verified by an application a Styrofoam manufacturing process where setup times are critical for time reduction. Significant time savings have been achieved with minimum investment. Further, the issues related with employer safety and ergonomics principles during die exchange are noted.

Keywords: Die exchange, internal-external set-up, lean manufacturing, single minute die exchange.

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Authors: Saad M. Al-Garni, Adel A. Abdennour

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Vehicle detection is the critical step for highway monitoring. In this paper we propose background subtraction and edge detection technique for vehicle detection. This technique uses the advantages of both approaches. The practical applications approved the effectiveness of this method. This method consists of two procedures: First, automatic background extraction procedure, in which the background is extracted automatically from the successive frames; Second vehicles detection procedure, which depend on edge detection and background subtraction. Experimental results show the effective application of this algorithm. Vehicles detection rate was higher than 91%.

Keywords: Image processing, Automatic background extraction, Moving vehicle detection.

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Authors: Ivan Lovrinović, Gordana Kordić, Martina Nakić

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There are little subjects in macroeconomics that are so widely discussed, but at the same time controversial and without a clear solution such as the choice of exchange rate regime. National authorities need to take into consideration numerous fundamentals, trying to fulfil goals of economic growth, low and stable inflation and international stability. This paper focuses on the countries of ex- Yugoslavia and their exchange rate history as independent states. We follow the development of the regimes in 6 countries during the transition through the financial crisis of the second part of the 2000s to the prospects of their final goal: full membership in the European Union. Main question is to what extent has the exchange regime contributed to their economic success, considering other objective factors.

Keywords: European Union, exchange rate regime, ex- Yugoslavia countries

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Authors: Dong-Woo Lim, Chang-Jae Chun, Hyung-Myung Kim

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We consider a two-way relay network where two sources exchange information. A relay helps the two sources exchange information using the decode-and-XOR-forward protocol. We investigate the power minimization problem with minimum rate constraints. The system needs two time slots and in each time slot the required rate pair should be achievable. The power consumption is minimized in each time slot and we obtained the closed form solution. The simulation results confirm that the proposed power allocation scheme consumes lower total power than the conventional schemes.

Keywords: Decode-and-XOR-forward, power minimization, two-way relay

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Authors: Hussin K. Ragb, Vijayan K. Asari

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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: Pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor.

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Authors: Piotr Prewysz-Kwinto

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A dynamic development of financial market is accompanied by the emergence of new products on stock exchanges which give absolutely new possibilities of investing money. Currently, the most innovative financial instruments offered to investors are exchange traded products (ETP). They can be defined as financial instruments whose price depends on the value of the underlying instrument. Thus, they offer investors a possibility of making a profit that results from the change in value of the underlying instrument without having to buy it. Currently, the Warsaw Stock Exchange offers many types of ETPs. They are investment products with full or partial capital protection, products without capital protection as well as leverage products, issued on such underlying instruments as indices, sector indices, commodity indices, prices of energy commodities, precious metals, agricultural produce or prices of shares of domestic and foreign companies. This paper presents the mechanism of functioning of ETP available on the Warsaw Stock Exchange and the results of the analysis of statistical data on these financial instruments.

Keywords: Exchange traded products, financial market, investment, stock exchange.

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Authors: Vladimir Simovic, Matija Varga, Predrag Oreski

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XHTML and XBRL are the standard languages for creating a database for the purpose of displaying financial statements on web applications. Today, XBRL is one of the most popular languages for business reporting. A large number of countries in the world recognize the role of XBRL language for financial reporting and the benefits that the reporting format provides in the collection, analysis, preparation, publication and the exchange of data (information) which is the positive side of this language. Here we present all advantages and opportunities that a company may have by using the XBRL format for business reporting. Also, this paper presents XBRL and other languages that are used for creating the database, such XML, XHTML, etc. The role of the AJAX complex model and technology will be explained in detail, and during the exchange of financial data between the web client and web server. Here will be mentioned basic layers of the network for data exchange via the web.

Keywords: XHTML, XBRL, XML, JavaScript, AJAX technology, data exchange.

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Authors: Nazar Zaki, Safaai Deris

Abstract:

The amount of the information being churned out by the field of biology has jumped manifold and now requires the extensive use of computer techniques for the management of this information. The predominance of biological information such as protein sequence similarity in the biological information sea is key information for detecting protein evolutionary relationship. Protein sequence similarity typically implies homology, which in turn may imply structural and functional similarities. In this work, we propose, a learning method for detecting remote protein homology. The proposed method uses a transformation that converts protein sequence into fixed-dimensional representative feature vectors. Each feature vector records the sensitivity of a protein sequence to a set of amino acids substrings generated from the protein sequences of interest. These features are then used in conjunction with support vector machines for the detection of the protein remote homology. The proposed method is tested and evaluated on two different benchmark protein datasets and it-s able to deliver improvements over most of the existing homology detection methods.

Keywords: Protein homology detection; support vectormachine; string kernel.

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Authors: Mohamed E. Salem Abozaed

Abstract:

Detection and classification of power quality (PQ) disturbances is an important consideration to electrical utilities and many industrial customers so that diagnosis and mitigation of such disturbance can be implemented quickly. S-transform algorithm and continuous wavelet transforms (CWT) are time-frequency algorithms, and both of them are powerful in detection and classification of PQ disturbances. This paper presents detection and classification of PQ disturbances using S-transform and CWT algorithms. The results of detection and classification, provides that S-transform is more accurate in detection and classification for most PQ disturbance than CWT algorithm, where as CWT algorithm more powerful in detection in some disturbances like notching

Keywords: CWT, Disturbances classification, Disturbances detection, Power quality, S-transform.

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Authors: P. Latha, L. Ganesan, N. Ramaraj, P. V. Hari Venkatesh

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Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, Rough Motion Detection, Network Formation and Training, Object Tracking. This paper can be used to verify real time detections in such a way that it can be used in defense applications, bio-medical applications and robotics. This can also be used for obtaining detection information related to the size, location and direction of motion of moving objects for assessment purposes. The time taken for video tracking by this Neural Network is only few seconds.

Keywords: Frame separation, Correlation Network, Neural network training, Radial Basis Function, object tracking, Motion Detection.

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Authors: Nasser Mozayani, Maryam Barzegar, Hoda Madani

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Vehicular communications play a substantial role in providing safety in transportation by means of safety message exchange. Researchers have proposed several solutions for securing safety messages. Protocols based on a fixed key infrastructure are more efficient in implementation and maintain stronger security in comparison with dynamic structures. These protocols utilize zone partitioning to establish distinct key infrastructure under Certificate Authority (CA) supervision in different regions. Secure anonymous broadcasting (SAB) is one of these protocols that preserves most of security aspects but it has some deficiencies in practice. A very important issue is region change of a vehicle for its mobility. Changing regions leads to change of CA and necessity of having new key set to resume communication. In this paper, we propose solutions for informing vehicles about region change to obtain new key set before entering next region. This hinders attackers- intrusion, packet loss and lessons time delay. We also make key request messages secure by confirming old CA-s public key to the message, hence stronger security for safety message broadcasting is attained.

Keywords: Secure broadcasting, Certificate authority (CA), Key exchange, Vehicular network.

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Authors: L. Basha, E. Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable on one country's competitiveness, trade and current account, inflation, wages, domestic economic activity and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021 and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables in the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: Exchange rate, Random Forest, time series, Machine Learning, forecasting.

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Authors: Chau Nguyen Viet, Ian Marshall

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This paper presents a new classification algorithm using colour and texture for obstacle detection. Colour information is computationally cheap to learn and process. However in many cases, colour alone does not provide enough information for classification. Texture information can improve classification performance but usually comes at an expensive cost. Our algorithm uses both colour and texture features but texture is only needed when colour is unreliable. During the training stage, texture features are learned specifically to improve the performance of a colour classifier. The algorithm learns a set of simple texture features and only the most effective features are used in the classification stage. Therefore our algorithm has a very good classification rate while is still fast enough to run on a limited computer platform. The proposed algorithm was tested with a challenging outdoor image set. Test result shows the algorithm achieves a much better trade-off between classification performance and efficiency than a typical colour classifier.

Keywords: Colour, texture, classification, obstacle detection.

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Authors: Kyogun Chang, Yoon Bok Lee

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Fault detection determines faultexistence and detecting time. This paper discusses two layered fault detection methods to enhance the reliability and safety. Two layered fault detection methods consist of fault detection methods of component level controllers and system level controllers. Component level controllers detect faults by using limit checking, model-based detection, and data-driven detection and system level controllers execute detection by stability analysis which can detect unknown changes. System level controllers compare detection results via stability with fault signals from lower level controllers. This paper addresses fault detection methods via stability and suggests fault detection criteria in nonlinear systems. The fault detection method applies tothe hybrid control unit of a military hybrid electric vehicleso that the hybrid control unit can detect faults of the traction motor.

Keywords: Two Layered Fault Detection, Stability Analysis, Fault-Tolerant Control

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Authors: Hsu-Yung Cheng, Chih-Chang Yu

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In this work, we present an automatic vehicle detection system for airborne videos using combined features. We propose a pixel-wise classification method for vehicle detection using Dynamic Bayesian Networks. In spite of performing pixel-wise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. The main novelty of the detection scheme is that the extracted combined features comprise not only pixel-level information but also region-level information. Afterwards, tracking is performed on the detected vehicles. Tracking is performed using efficient Kalman filter with dynamic particle sampling. Experiments were conducted on a wide variety of airborne videos. We do not assume prior information of camera heights, orientation, and target object sizes in the proposed framework. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging dataset.

Keywords: Vehicle Detection, Airborne Video, Tracking, Dynamic Bayesian Networks

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: Cyber-security, Intrusion Detection Systems, Temporal Graph Network, Anomaly Detection.

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Authors: Ahmed Arara

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Information sharing and exchange, rather than information processing, is what characterizes information technology in the 21st century. Ontologies, as shared common understanding, gain increasing attention, as they appear as the most promising solution to enable information sharing both at a semantic level and in a machine-processable way. Domain Ontology-based modeling has been exploited to provide shareability and information exchange among diversified, heterogeneous applications of enterprises. Contextual ontologies are “an explicit specification of contextual conceptualization". That is: ontology is characterized by concepts that have multiple representations and they may exist in several contexts. Hence, contextual ontologies are a set of concepts and relationships, which are seen from different perspectives. Contextualization is to allow for ontologies to be partitioned according to their contexts. The need for contextual ontologies in enterprise modeling has become crucial due to the nature of today's competitive market. Information resources in enterprise is distributed and diversified and is in need to be shared and communicated locally through the intranet and globally though the internet. This paper discusses the roles that ontologies play in an enterprise modeling, and how ontologies assist in building a conceptual model in order to provide communicative and interoperable information systems. The issue of enterprise modeling based on contextual domain ontology is also investigated, and a framework is proposed for an enterprise model that consists of various applications.

Keywords: Contextual ontologies, Enterprise model, domainontology.

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Authors: F. Rahnamay Roodposhti, M. Falah Shams, H. Kordlouie

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The aim of the article is extending and developing econometrics and network structure based methods which are able to distinguish price manipulation in Tehran stock exchange. The principal goal of the present study is to offer model for approximating price manipulation in Tehran stock exchange. In order to do so by applying separation method a sample consisting of 397 companies accepted at Tehran stock exchange were selected and information related to their price and volume of trades during years 2001 until 2009 were collected and then through performing runs test, skewness test and duration correlative test the selected companies were divided into 2 sets of manipulated and non manipulated companies. In the next stage by investigating cumulative return process and volume of trades in manipulated companies, the date of starting price manipulation was specified and in this way the logit model, artificial neural network, multiple discriminant analysis and by using information related to size of company, clarity of information, ratio of P/E and liquidity of stock one year prior price manipulation; a model for forecasting price manipulation of stocks of companies present in Tehran stock exchange were designed. At the end the power of forecasting models were studied by using data of test set. Whereas the power of forecasting logit model for test set was 92.1%, for artificial neural network was 94.1% and multi audit analysis model was 90.2%; therefore all of the 3 aforesaid models has high power to forecast price manipulation and there is no considerable difference among forecasting power of these 3 models.

Keywords: Price Manipulation, Liquidity, Size of Company, Floating Stock, Information Clarity

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Authors: Zulkiflee M., Robiah Y., Nur Azman Abu, Shahrin S.

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Malware is software which was invented and meant for doing harms on computers. Malware is becoming a significant threat in computer network nowadays. Malware attack is not just only involving financial lost but it can also cause fatal errors which may cost lives in some cases. As new Internet Protocol version 6 (IPv6) emerged, many people believe this protocol could solve most malware propagation issues due to its broader addressing scheme. As IPv6 is still new compares to native IPv4, some transition mechanisms have been introduced to promote smoother migration. Unfortunately, these transition mechanisms allow some malwares to propagate its attack from IPv4 to IPv6 network environment. In this paper, a proof of concept shall be presented in order to show that some existing IPv4 malware detection technique need to be improvised in order to detect malware attack in dual-stack network more efficiently. A testbed of dual-stack network environment has been deployed and some genuine malware have been released to observe their behaviors. The results between these different scenarios will be analyzed and discussed further in term of their behaviors and propagation methods. The results show that malware behave differently on IPv6 from the IPv4 network protocol on the dual-stack network environment. A new detection technique is called for in order to cater this problem in the near future.

Keywords: Dual-Stack, Malware, Worm, IPv6;IDS

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Authors: Madihah Mohd Saudi, Andrea J Cullen, Mike E Woodward

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This paper presents a new STAKCERT KDD processes for worm detection. The enhancement introduced in the data-preprocessing resulted in the formation of a new STAKCERT model for worm detection. In this paper we explained in detail how all the processes involved in the STAKCERT KDD processes are applied within the STAKCERT model for worm detection. Based on the experiment conducted, the STAKCERT model yielded a 98.13% accuracy rate for worm detection by integrating the STAKCERT KDD processes.

Keywords: data mining, incident response, KDD processes, security metrics and worm detection.

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Authors: Awadhesh Pachauri, Manabendra Bhuyan

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Electrocardiogram (ECG) is considered to be the backbone of cardiology. ECG is composed of P, QRS & T waves and information related to cardiac diseases can be extracted from the intervals and amplitudes of these waves. The first step in extracting ECG features starts from the accurate detection of R peaks in the QRS complex. We have developed a robust R wave detector using wavelets. The wavelets used for detection are Daubechies and Symmetric. The method does not require any preprocessing therefore, only needs the ECG correct recordings while implementing the detection. The database has been collected from MIT-BIH arrhythmia database and the signals from Lead-II have been analyzed. MatLab 7.0 has been used to develop the algorithm. The ECG signal under test has been decomposed to the required level using the selected wavelet and the selection of detail coefficient d4 has been done based on energy, frequency and cross-correlation analysis of decomposition structure of ECG signal. The robustness of the method is apparent from the obtained results.

Keywords: ECG, P-QRS-T waves, Wavelet Transform, Hard Thresholding, R-wave Detection.

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Authors: Yasuomi D. Sato, Yasutaka Kuriya

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We here propose improved version of elastic graph matching (EGM) as a face detector, called the multi-scale EGM (MS-EGM). In this improvement, Gabor wavelet-based pyramid reduces computational complexity for the feature representation often used in the conventional EGM, but preserving a critical amount of information about an image. The MS-EGM gives us higher detection performance than Viola-Jones object detection algorithm of the AdaBoost Haar-like feature cascade. We also show rapid detection speeds of the MS-EGM, comparable to the Viola-Jones method. We find fruitful benefits in the MS-EGM, in terms of topological feature representation for a face.

Keywords: Face detection, Gabor wavelet based pyramid, elastic graph matching, topological preservation, redundancy of computational complexity.

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Authors: Souleymane Oumtanaga, Falilu Abudul Kadiri

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Internet is without any doubt the fastest and effective mean of communication making it possible to reach a great number of people in the world. It draws its base from exchange points. Indeed exchange points are used to inter-connect various Internet suppliers and operators in order to allow them to exchange traffic and it is with these interconnections that Internet made its great strides. They thus make it possible to limit the traffic delivered via the operators of transits. This limitation allows a significant improvement of the quality of service, a reduction in the latency time just as a reduction of the cost of connection for the final subscriber. Through this article we will show how the installation of an IXP allows an improvement and a diversification of the services just as a reduction of the Internet connection costs.

Keywords: Quality of service, Exchange point, interconnection, Service providers, bandwidth, traffic, routers, routing table.

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Authors: Kyi Lin Lin Kyaw

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Nowadays, we are facing with network threats that cause enormous damage to the Internet community day by day. In this situation, more and more people try to prevent their network security using some traditional mechanisms including firewall, Intrusion Detection System, etc. Among them honeypot is a versatile tool for a security practitioner, of course, they are tools that are meant to be attacked or interacted with to more information about attackers, their motives and tools. In this paper, we will describe usefulness of low-interaction honeypot and high-interaction honeypot and comparison between them. And then we propose hybrid honeypot architecture that combines low and high -interaction honeypot to mitigate the drawback. In this architecture, low-interaction honeypot is used as a traffic filter. Activities like port scanning can be effectively detected by low-interaction honeypot and stop there. Traffic that cannot be handled by low-interaction honeypot is handed over to high-interaction honeypot. In this case, low-interaction honeypot is used as proxy whereas high-interaction honeypot offers the optimal level realism. To prevent the high-interaction honeypot from infections, containment environment (VMware) is used.

Keywords: Low-interaction honeypot, High-interactionhoneypot, VMware, Proxy

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: Color space, neural network, random forest, skin detection, statistical feature.

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