1,315 research outputs found

    Artificial neural networks in geospatial analysis

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    Artificial neural networks are computational models widely used in geospatial analysis for data classification, change detection, clustering, function approximation, and forecasting or prediction. There are many types of neural networks based on learning paradigm and network architectures. Their use is expected to grow with increasing availability of massive data from remote sensing and mobile platforms

    An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

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    With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide the comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network). The proposed IoT-IDCS-CNN makes use of high-performance computing that employs the robust Compute Unified Device Architectures (CUDA) based Nvidia GPUs (Graphical Processing Units) and parallel processing that employs high-speed I9-core-based Intel CPUs. In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. All subsystems were developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset, which includes all the key attacks in IoT computing. The simulation results demonstrated a greater than 99.3% and 98.2% cyber-attack classification accuracy for the binary-class classifier (normal vs. anomaly) and the multiclass classifier (five categories), respectively. The proposed system was validated using a K-fold cross-validation method and was evaluated using the confusion matrix parameters (i.e., true negative (TN), true positive (TP), false negative (FN), false positive (FP)), along with other classification performance metrics, including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning-based IDCS systems in the same area of study

    A Survey of Prediction and Classification Techniques in Multicore Processor Systems

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    In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

    Detection of encrypted traffic generated by peer-to-peer live streaming applications using deep packet inspection

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    The number of applications using the peer-to-peer (P2P) networking paradigm and their popularity has substantially grown over the last decade. They evolved from the le-sharing applications to media streaming ones. Nowadays these applications commonly encrypt the communication contents or employ protocol obfuscation techniques. In this dissertation, it was conducted an investigation to identify encrypted traf c ows generated by three of the most popular P2P live streaming applications: TVUPlayer, Livestation and GoalBit. For this work, a test-bed that could simulate a near real scenario was created, and traf c was captured from a great variety of applications. The method proposed resort to Deep Packet Inspection (DPI), so we needed to analyse the payload of the packets in order to nd repeated patterns, that later were used to create a set of SNORT rules that can be used to detect key network packets generated by these applications. The method was evaluated experimentally on the test-bed created for that purpose, being shown that its accuracy is of 97% for GoalBit.A popularidade e o número de aplicações que usam o paradigma de redes par-a-par (P2P) têm crescido substancialmente na última década. Estas aplicações deixaram de serem usadas simplesmente para partilha de ficheiros e são agora usadas também para distribuir conteúdo multimédia. Hoje em dia, estas aplicações têm meios de cifrar o conteúdo da comunicação ou empregar técnicas de ofuscação directamente no protocolo. Nesta dissertação, foi realizada uma investigação para identificar fluxos de tráfego encriptados, que foram gerados por três aplicações populares de distribuição de conteúdo multimédia em redes P2P: TVUPlayer, Livestation e GoalBit. Para este trabalho, foi criada uma plataforma de testes que pretendia simular um cenário quase real, e o tráfego que foi capturado, continha uma grande variedade de aplicações. O método proposto nesta dissertação recorre à técnica de Inspecção Profunda de Pacotes (DPI), e por isso, foi necessário 21nalisar o conteúdo dos pacotes a fim de encontrar padrões que se repetissem, e que iriam mais tarde ser usados para criar um conjunto de regras SNORT para detecção de pacotes chave· na rede, gerados por estas aplicações, afim de se poder correctamente classificar os fluxos de tráfego. Após descobrir que a aplicação Livestation deixou de funcionar com P2P, apenas as duas regras criadas até esse momento foram usadas. Quanto à aplicação TVUPlayer, foram criadas várias regras a partir do tráfego gerado por ela mesma e que tiveram uma boa taxa de precisão. Várias regras foram também criadas para a aplicação GoalBit em que foram usados quatro cenários: com e sem encriptação usando a opção de transmissão tracker, e com e sem encriptação usando a opção de transmissão sem necessidade de tracker (aqui foi usado o protocolo Kademlia). O método foi avaliado experimentalmente na plataforma de testes criada para o efeito, sendo demonstrado que a precisão do conjunto de regras para a aplicação GoallBit é de 97%.Fundação para a Ciência e a Tecnologia (FCT

    Proceedings of the 2nd Computer Science Student Workshop: Microsoft Istanbul, Turkey, April 9, 2011

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    All-optical header processing in a 42.6Gb/s optoelectronic firewall

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    A novel architecture to enable future network security systems to provide effective protection in the context of continued traffic growth and the need to minimise energy consumption is proposed. It makes use of an all-optical pre-filtering stage operating at the line rate under software control to distribute incoming packets to specialised electronic processors. An experimental system that integrates software controls and electronic interfaces with an all-optical pattern recognition system has demonstrated the key functions required by the new architecture. As an example, the ability to sort packets arriving in a 42.6Gb/s data stream according to their service type was shown experimentally

    4Sensing - decentralized processing for participatory sensing data

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    Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática.Participatory sensing is a new application paradigm, stemming from both technical and social drives, which is currently gaining momentum as a research domain. It leverages the growing adoption of mobile phones equipped with sensors, such as camera, GPS and accelerometer, enabling users to collect and aggregate data, covering a wide area without incurring in the costs associated with a large-scale sensor network. Related research in participatory sensing usually proposes an architecture based on a centralized back-end. Centralized solutions raise a set of issues. On one side, there is the implications of having a centralized repository hosting privacy sensitive information. On the other side, this centralized model has financial costs that can discourage grassroots initiatives. This dissertation focuses on the data management aspects of a decentralized infrastructure for the support of participatory sensing applications, leveraging the body of work on participatory sensing and related areas, such as wireless and internet-wide sensor networks, peer-to-peer data management and stream processing. It proposes a framework covering a common set of data management requirements - from data acquisition, to processing, storage and querying - with the goal of lowering the barrier for the development and deployment of applications. Alternative architectural approaches - RTree, QTree and NTree - are proposed and evaluated experimentally in the context of a case-study application - SpeedSense - supporting the monitoring and prediction of traffic conditions, through the collection of speed and location samples in an urban setting, using GPS equipped mobile phones

    Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

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    [EN] The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-tra_ c networks with multiple nodes/ sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G o_ ers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.Najm, IA.; Hamoud, AK.; Lloret, J.; Bosch Roig, I. (2019). Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics. 8(6):1-23. https://doi.org/10.3390/electronics8060607S12386Rahem, A. A. T., Ismail, M., Najm, I. A., & Balfaqih, M. (2017). Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET. Telecommunication Systems, 65(4), 739-754. doi:10.1007/s11235-016-0242-7Aalsalem, M. Y., Khan, W. Z., Gharibi, W., Khan, M. K., & Arshad, Q. (2018). Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges. Journal of Network and Computer Applications, 113, 87-97. doi:10.1016/j.jnca.2018.04.004Sunny, A., Panchal, S., Vidhani, N., Krishnasamy, S., Anand, S. V. R., Hegde, M., … Kumar, A. (2017). A generic controller for managing TCP transfers in IEEE 802.11 infrastructure WLANs. Journal of Network and Computer Applications, 93, 13-26. doi:10.1016/j.jnca.2017.05.006Jain, R. (1990). Congestion control in computer networks: issues and trends. IEEE Network, 4(3), 24-30. doi:10.1109/65.56532Kafi, M. A., Djenouri, D., Ben-Othman, J., & Badache, N. (2014). Congestion Control Protocols in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 16(3), 1369-1390. doi:10.1109/surv.2014.021714.00123Floyd, S. (2000). Congestion Control Principles. doi:10.17487/rfc2914Qazi, I. A., & Znati, T. (2011). On the design of load factor based congestion control protocols for next-generation networks. Computer Networks, 55(1), 45-60. doi:10.1016/j.comnet.2010.07.010Katabi, D., Handley, M., & Rohrs, C. (2002). Congestion control for high bandwidth-delay product networks. ACM SIGCOMM Computer Communication Review, 32(4), 89-102. doi:10.1145/964725.633035Wang, Y., Rozhnova, N., Narayanan, A., Oran, D., & Rhee, I. (2013). An improved hop-by-hop interest shaper for congestion control in named data networking. ACM SIGCOMM Computer Communication Review, 43(4), 55-60. doi:10.1145/2534169.2491233Mirza, M., Sommers, J., Barford, P., & Zhu, X. (2010). A Machine Learning Approach to TCP Throughput Prediction. IEEE/ACM Transactions on Networking, 18(4), 1026-1039. doi:10.1109/tnet.2009.2037812Taherkhani, N., & Pierre, S. (2016). Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm. IEEE Transactions on Intelligent Transportation Systems, 17(11), 3275-3285. doi:10.1109/tits.2016.2546555Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems. IEEE Communications Surveys & Tutorials, 19(4), 2432-2455. doi:10.1109/comst.2017.2707140Gonzalez-Landero, F., Garcia-Magarino, I., Lacuesta, R., & Lloret, J. (2018). PriorityNet App: A Mobile Application for Establishing Priorities in the Context of 5G Ultra-Dense Networks. IEEE Access, 6, 14141-14150. doi:10.1109/access.2018.2811900Lloret, J., Parra, L., Taha, M., & Tomás, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks, 129, 340-351. doi:10.1016/j.comnet.2017.05.018Khan, I., Zafar, M., Jan, M., Lloret, J., Basheri, M., & Singh, D. (2018). Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems. Entropy, 20(2), 92. doi:10.3390/e20020092Elappila, M., Chinara, S., & Parhi, D. R. (2018). Survivable Path Routing in WSN for IoT applications. Pervasive and Mobile Computing, 43, 49-63. doi:10.1016/j.pmcj.2017.11.004Singh, K., Singh, K., Son, L. H., & Aziz, A. (2018). Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Computer Networks, 138, 90-107. doi:10.1016/j.comnet.2018.03.023Shelke, M., Malhotra, A., & Mahalle, P. N. (2017). Congestion-Aware Opportunistic Routing Protocol in Wireless Sensor Networks. Smart Innovation, Systems and Technologies, 63-72. doi:10.1007/978-981-10-5544-7_7Godoy, P. D., Cayssials, R. L., & García Garino, C. G. (2018). Communication channel occupation and congestion in wireless sensor networks. Computers & Electrical Engineering, 72, 846-858. doi:10.1016/j.compeleceng.2017.12.049Najm, I. A., Ismail, M., Lloret, J., Ghafoor, K. Z., Zaidan, B. B., & Rahem, A. A. T. (2015). Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications, 58, 119-129. doi:10.1016/j.jnca.2015.09.003Najm, I. A., Ismail, M., & Abed, G. A. (2014). High-Performance Mobile Technology LTE-A using the Stream Control Transmission Protocol: A Systematic Review and Hands-on Analysis. Journal of Applied Sciences, 14(19), 2194-2218. doi:10.3923/jas.2014.2194.2218Katuwal, R., Suganthan, P. N., & Zhang, L. (2018). An ensemble of decision trees with random vector functional link networks for multi-class classification. Applied Soft Computing, 70, 1146-1153. doi:10.1016/j.asoc.2017.09.020Gómez, S. E., Martínez, B. C., Sánchez-Esguevillas, A. J., & Hernández Callejo, L. (2017). Ensemble network traffic classification: Algorithm comparison and novel ensemble scheme proposal. Computer Networks, 127, 68-80. doi:10.1016/j.comnet.2017.07.018Hasan, M., Hossain, E., & Niyato, D. (2013). Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches. IEEE Communications Magazine, 51(6), 86-93. doi:10.1109/mcom.2013.6525600Liang, D., Zhang, Z., & Peng, M. (2015). Access Point Reselection and Adaptive Cluster Splitting-Based Indoor Localization in Wireless Local Area Networks. IEEE Internet of Things Journal, 2(6), 573-585. doi:10.1109/jiot.2015.2453419Park, H., Haghani, A., Samuel, S., & Knodler, M. A. (2018). Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accident Analysis & Prevention, 112, 39-49. doi:10.1016/j.aap.2017.11.025Shu, J., Liu, S., Liu, L., Zhan, L., & Hu, G. (2017). Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine. Chinese Journal of Electronics, 26(2), 377-384. doi:10.1049/cje.2017.01.013Riekstin, A. C., Januário, G. C., Rodrigues, B. B., Nascimento, V. T., Carvalho, T. C. M. B., & Meirosu, C. (2016). Orchestration of energy efficiency capabilities in networks. Journal of Network and Computer Applications, 59, 74-87. doi:10.1016/j.jnca.2015.06.015Adi, E., Baig, Z., & Hingston, P. (2017). Stealthy Denial of Service (DoS) attack modelling and detection for HTTP/2 services. Journal of Network and Computer Applications, 91, 1-13. doi:10.1016/j.jnca.2017.04.015Stimpfling, T., Bélanger, N., Cherkaoui, O., Béliveau, A., Béliveau, L., & Savaria, Y. (2017). Extensions to decision-tree based packet classification algorithms to address new classification paradigms. Computer Networks, 122, 83-95. doi:10.1016/j.comnet.2017.04.021Singh, D., Nigam, S. P., Agrawal, V. P., & Kumar, M. (2016). Vehicular traffic noise prediction using soft computing approach. Journal of Environmental Management, 183, 59-66. doi:10.1016/j.jenvman.2016.08.053Xia, Y., Chen, W., Liu, X., Zhang, L., Li, X., & Xiang, Y. (2017). Adaptive Multimedia Data Forwarding for Privacy Preservation in Vehicular Ad-Hoc Networks. IEEE Transactions on Intelligent Transportation Systems, 18(10), 2629-2641. doi:10.1109/tits.2017.2653103Tariq, F., & Baig, S. (2017). Machine Learning Based Botnet Detection in Software Defined Networks. International Journal of Security and Its Applications, 11(11), 1-12. doi:10.14257/ijsia.2017.11.11.01Wu, T., Petrangeli, S., Huysegems, R., Bostoen, T., & De Turck, F. (2017). Network-based video freeze detection and prediction in HTTP adaptive streaming. Computer Communications, 99, 37-47. doi:10.1016/j.comcom.2016.08.005Pham, T. N. D., & Yeo, C. K. (2018). Adaptive trust and privacy management framework for vehicular networks. Vehicular Communications, 13, 1-12. doi:10.1016/j.vehcom.2018.04.006Mohamed, M. F., Shabayek, A. E.-R., El-Gayyar, M., & Nassar, H. (2019). An adaptive framework for real-time data reduction in AMI. Journal of King Saud University - Computer and Information Sciences, 31(3), 392-402. doi:10.1016/j.jksuci.2018.02.012Louvieris, P., Clewley, N., & Liu, X. (2013). Effects-based feature identification for network intrusion detection. Neurocomputing, 121, 265-273. doi:10.1016/j.neucom.2013.04.038Verma, P. K., Verma, R., Prakash, A., Agrawal, A., Naik, K., Tripathi, R., … Abogharaf, A. (2016). Machine-to-Machine (M2M) communications: A survey. Journal of Network and Computer Applications, 66, 83-105. doi:10.1016/j.jnca.2016.02.016Hamoud, A. K., Hashim, A. S., & Awadh, W. A. (2018). Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 26. doi:10.9781/ijimai.2018.02.004Lavanya, D. (2012). Ensemble Decision Tree Classifier For Breast Cancer Data. International Journal of Information Technology Convergence and Services, 2(1), 17-24. doi:10.5121/ijitcs.2012.2103Polat, K., & Güneş, S. (2007). Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Applied Mathematics and Computation, 187(2), 1017-1026. doi:10.1016/j.amc.2006.09.022Cayirci, E., Tezcan, H., Dogan, Y., & Coskun, V. (2006). Wireless sensor networks for underwater survelliance systems. Ad Hoc Networks, 4(4), 431-446. doi:10.1016/j.adhoc.2004.10.008Mezzavilla, M., Zhang, M., Polese, M., Ford, R., Dutta, S., Rangan, S., & Zorzi, M. (2018). End-to-End Simulation of 5G mmWave Networks. IEEE Communications Surveys & Tutorials, 20(3), 2237-2263. doi:10.1109/comst.2018.282888
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