Author Details

Scroll

Refine your search

Collections

Engineering Collection

Co-Authors

Journals

International Journal of Computer Networks and Applications

Year

Authors

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All

Das, Resul

A Novel Approach for Data Privacy Using Attribute Based Scheme Algorithm for Cloud Computing

Abstract Views :209 | PDF Views:0

Authors

A. Nithya ¹, B. Ramakrishnan ², Resul Das ³

Affiliations
1 S.T. Hindu College, Nagercoil, Tamilnadu, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
3 Department of Software Engineering, Firat University, 23119, Elazig, TR

Source

International Journal of Computer Networks and Applications, Vol 3, No 4 (2016), Pagination: 70-77

Abstract

Cloud computing is the mass storage area that helps the user to access the data anywhere. There are so many platforms provided by the cloud service provider. They are SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service) etc. Though security is not fully provided by the cloud service provider to reshape the advances in information technology, cloud computing is expected as an updated technology. The data was securely stored in the cloud and if it is corrupted then the proxy is implemented to regenerate the corrupted data in the cloud. Thus security and integrity is successfully achieved. This is further extended by implementing efficient file fetching by the third party user. To maintain efficient file fetching system Multi authority cloud model is proposed. The model is continuing with the proposed entities such as Attribute authority (AA), Certificate Authority (CA) and Third party end user. The data is encrypted by the owner and stored in the cloud server. CA is used to delegate the Secret Key (SK) to AA and Public Kay (PK) to user. After Checking the authentication of the owner CA provides PK to the owner only then the owner is allowed to upload the data in cloud, the data is encrypted and outsource to the cloud server. Using SK the third party user is allowed to view the data from the cloud. If the user enter the wrong key or misuse the data, user will be revoked. If the User needs to download or update or delete the data in the cloud the user need to send a Data Access Privilege (DAP) request to the respective owner. Certificate authority is responsible to generate a key to the entities such as User, Data Owner and attributes.

Keywords

Cloud Computing, Security, Third Party Auditor (TPA), Proxy, RSA Algorithm, Regeneration, Multiuser Authentication.

Full Text

A Novel Routing Scheme to Avoid Link Error and Packet Dropping in Wireless Sensor Networks

Abstract Views :213 | PDF Views:4

Authors

M. Kavitha ¹, B. Ramakrishnan ², Resul Das ³

Affiliations
1 S.T. Hindu College, Nagercoil-2, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil-2, IN
3 Department of Software Engineering, Firat University, 23119, Elazig, TR

Source

International Journal of Computer Networks and Applications, Vol 3, No 4 (2016), Pagination: 86-94

Abstract

Packet loss is a major issue in Wireless Sensor Network (WSN) data transmission which is caused by malicious packet dropping and link error. In conventional methods, the malicious dropping may result in a packet loss rate that is comparable to normal channel losses, the stochastic processes that characterizes the two phenomena exhibited in different correlation which would affect the network performance i.e. detection accuracy. By detecting the correlations between lost packets, we will decide whether the packet loss is purely due to regular link errors, or is a combined effect of link error and malicious drop. In order to overcome these issues, we have proposed a HLA (Homomorphic Linear Authentication) based on routing protocol which is a collusion proof mechanism and resolves the public auditing problem. Here, the proposed technique will be implemented in OLSR (Optimized Link State Routing) Protocol. The actual status of each packet transmission i.e., the packet loss information can be described by our technique. The network simulation results describe the performance of the proposed method in terms of detection accuracy in low computation complexity. Our HLA based OLSR protocol is compared with existing AODV, RIP and other protocols.

Keywords

WSN, AODV, OLSR, HLA, malicious node attack, Link Error.

Full Text

Design and Implementation of a Smart Home for the Elderly and Disabled

Abstract Views :89 | PDF Views:1

Authors

Resul Das ¹, Gurkan Tuna ², Ayse Tuna ³

Affiliations
1 Department of Software Engineering, Firat University, 23119, Elazig, TR
2 Department of Computer Programming, Trakya University, 22020, Edirne, TR
3 School of Foreign Languages, Trakya University, 22030, Edirne, TR

Source

International Journal of Computer Networks and Applications, Vol 2, No 6 (2015), Pagination: 242-246

Abstract

Different from the past, in recent years, an increasing number of technology solutions have been started to be designed for the elderly and disabled due to the aging population and increasing awareness about common problems of the disabled. In smart homes, a group of smart, networked devices and sensors can help make the elderly and disabled more independent by letting family members, relatives and doctors keep tabs from afar. Because there is significant interest from care providers who prefer to keep the elderly and disabled in their houses rather than caring them in assisted-living centers. Accordingly, in this study, a sample application of smart home technology is presented. In the presented application, a group of sensors are used to build an autonomous smart home. Although the presented application is just a simple example of how smart homes can be used, it has the potential of affecting all areas improving day to day life of the elderly and disabled.

Keywords

Smart Homes, Networked Sensors, Intelligent Monitoring, Accessibility, Prototype Smart Home.

Full Text

Wireless Sensor Network-Based Health Monitoring System for the Elderly and Disabled

Abstract Views :92 | PDF Views:0

Authors

Gurkan Tuna ¹, Resul Das ², Ayse Tuna ³

Affiliations
1 Department of Computer Programming, Trakya University, 22020, Edirne, TR
2 Department of Software Engineering, Firat University, 23119, Elazig, TR
3 School of Foreign Languages, Trakya University, 22030, Edirne, TR

Source

International Journal of Computer Networks and Applications, Vol 2, No 6 (2015), Pagination: 247-253

Abstract

Even if the elderly and disabled need the assistance of their families, parents, and healthcare providers, they prefer to live in their homes instead of assisted-living centers. Therefore, their health and activities must be remotely monitored so that in case of an urgent unexpected situation, immediate help can be provided. In this respect, this paper proposes a wireless sensor network-based health monitoring system for the elderly and disabled, and focuses on its development steps. The proposed system is composed of low-cost off-the-shelf components and enables the monitoring of important health parameters of the elderly and disabled. Since it is a wireless and portable health monitoring solution, it can be a valuable remote monitoring tool for health care service providers by reducing the cost of their services. It can be combined with data mining solutions and/or machine learning techniques to offer novel features such as pattern extraction and behavior analysis.

Keywords

Wireless Sensor Network, Health Monitoring, the Elderly, the Disabled, Intelligent Monitoring.

Full Text

A Survey on Potential Applications of Honeypot Technology in Intrusion Detection Systems

Abstract Views :178 | PDF Views:0

Authors

Muhammet Baykara ¹, Resul Das ¹

Affiliations
1 Firat University, Software Engineering Department, 23119, Elazig, TR

Source

International Journal of Computer Networks and Applications, Vol 2, No 5 (2015), Pagination: 203-211

Abstract

Information security in the sense of personal and institutional has become a top priority in digitalized modern world in parallel to the new technological developments. Many methods, tools and technologies are used to provide the information security of IT systems. These are considered, encryption, authentication, firewall, and intrusion detection and prevention systems. Moreover, honeypot systems are proposed as complementary structures. This paper presents the overall view of the publications in IDS, IPS and honeypot systems. Recently, honeypot systems are anymore used in connection with intrusion detection systems. So this paper describes possible implementation of honeypot technologies combined with IDS/IPS in a network. Studies in the literature have shown intrusion detection systems cannot find the 0-day vulnerabilities. The system provided by the honeypots and intrusion detection systems in the network, might detect new exploit and hacker attempt.

Keywords

Information Security, Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Honeypot, Network Security.

Full Text

Machine-to-Machine Communications for Smart Homes

Big Data Analysis for M2M Networks: Research Challenges and Open Research Issues

Abstract Views :122 | PDF Views:0

Authors

Gurkan Tuna ¹, Resul Das ², B. Ramakrishnan ³, Yilmaz Kilicaslan ⁴

Affiliations
1 Department of Computer Programming, Trakya University, Edirne, 22020, TR
2 Department of Software Engineering, Firat University, Elazig, 23119, TR
3 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
4 Department of Computer Engineering, Adnan Menderes University, Aydin, 09010, TR

Source

International Journal of Computer Networks and Applications, Vol 4, No 1 (2017), Pagination: 27-34

Abstract

In recent years, solutions based on machine-to-machine (M2M) communications have started to support us in many areas of our life and work. However, the amount of data collected by M2M has increased tremendously and surpassed our expectations. This makes it necessary to investigate data mining methodologies and machine learning techniques in order to efficiently utilize large amounts of data gathered by M2M devices. In this paper, we first review existing data mining and machine-learning techniques specifically designed and proposed for M2M networks. Then, we discuss Big Data concept, investigate Big Data analysis techniques, and the importance of Big Data for M2M networks. Finally, we investigate research challenges and open research issues in M2M to provide an insight into future research opportunities.

Keywords

Machine-to-Machine (M2M), Machine Learning, Data Mining, Big Data.

Full Text

References

J. N. Al-Karaki, K. -C. Chen, G. Morabito and J. de Olieveira, “From M2M communications to the Internet of Things: Opportunities and challenges,” Ad Hoc Networks, vol. 18, pp. 1-2, 2014.

G. Wu, S. Talwar, K. Johnsson, N. Himayat, and K. D. Johnson, “M2M: From Mobile to Embedded Internet,” IEEE Communications Magazine, vol. 49, no. 4, pp. 36-43, April 2011.

J. Holler, V. Tsiatsis, C. Mulligan, S. Avesand, S. Karnouskos and D. Boyle, “From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence,” Academic Press: MA, USA, 2014.

A. Biral, M. Centenaro, A. Zanella, L. Vangelista, and M. Zorzi, “The challenges of M2M massive access in wireless cellular networks,” Digital Communications and Networks, vol. 1, no. 1, pp. 1-19, February 2015.

ETSI, “Machine to Machine Communications,” TS 102 689.

D. Boswarthick, O. Elloumi, and O. Hersent (Eds), M2M Communications: A Systems Approach, Wiley: West Sussex, UK, 2012.

Z. Fan, Q. Chen, G. Kalogridis, S. Tan, and D. Kaleshi, “The power of data: Data analytics for M2M and smart grid,” 2012 3rd IEEE PES international conference and exhibition on Innovative Smart Grid Technologies (ISGT Europe), pp. 1-8, 2012.

G. Suciu, A. Vulpe, A. Martian, S. Halunga, and D. N. Vizireanu, “Big Data Processing for Renewable Energy Telemetry Using a Decentralized Cloud M2M System,” Wireless Personal Communications, 2015.

G. Suciu, V. Suciu, A. Martian, R. Craciunescu, A. Vulpe, I. Marcu, S. Halunga, and O. Fratu, “Big Data, Internet of Things and Cloud Convergence – An Architecture for Secure E-Health Applications,” Journal of Medical Systems, vol. 39, no. 141, 2015.

A. J. Jara, D. Genoud and Y. Bocchi, “Big Data for Cyber Physical Systems: An Analysis of Challenges, Solutions and Opportunities,” 2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 376-380, 2014.

I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan, “The rise of "big data" on cloud computing: Review and open research issues,” Information Systems, vol. 47, pp. 98-115, 2015.

M.-S. Chen, J. Han, and P. S. Yu, “Data Mining: An Overview from a Database Perspective,” IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866-883, December 1996.

D. Talia, P. Trunfio, and F. Marozzo, “Introduction to Data Mining,” in Data Analysis in the Cloud, pp. 1-25, 2016.

I. Kononenko and M. Kukar, “Machine Learning Basics,” in Machine Learning and Data Mining, pp. 59-105, 2007.

T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An Efficient k-Means Clustering Algorithm: Analysis and Implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, July 2002.

G. Holmes, A. Donkin, and I. H. Witten, “WEKA: a machine learning workbench,” Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, pp. 357-371, 1994.

Z. He, “An overview of data mining,” in Data Mining for Bioinformatics Applications, pp. 1-10, 2015.

A. Lausch, Andreas Schmidt, and Lutz Tischendorf, “Data mining and linked open data – New perspectives for data analysis in environmental research,” Ecological Modelling, vol. 295, pp. 5-17, 2015.

C. C. Aggarwal, Data Mining, Springer International Publishing: New Delhi, India, May 2015. ISBN: 978-3-319-14141-1.

Sapphire: Large Scale Data Mining and Pattern Recognition. Available at: http:// computation.llnl.gov/casc/sapphire/overview/overview.html. Accessed: October 7, 2015.

S. M. Weiss and C. A. Kulikowski, Computer Systems that Learn. Morgan Kaufmann: San Mateo, CA, 1991.

R. Duda and P. Hart, Pattern Recognition and Scene Analysis. Wiley, New York, 1973.

R. E. Abdel-Aal, “Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks,” Neural Computing & Applications, vol. 11, no. 1, pp. 17-29, 2002.

P. D. Wasserman, Neural Computing: Theory and Practice. Van Nostrand Reinhold, New York, 1989.

G. Krempl, I. Žliobaite, D. Brzeziński, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S. Sievi, M. Spiliopoulou, and J. Stefanowski, “Open challenges for data stream mining research,” SIGKDD Explor. Newsl., vol. 16, no.1, pp. 1-10, September 2014.

H. Mannila. Methods and Problems in Data Mining. In Proceedings of the 6th International Conference on Database Theory (ICDT '97), F. N. Afrati and P.G. Kolaitis (Eds.). Springer-Verlag, London, UK, 1997. pp. 41-55.

C. E. Brodley, U. Rebbapragada, K. Small, and B. C. Wallace, “Challenges and Opportunities in Applied Machine Learning,” AI Magazine, vol. 33, no. 1, pp. 11-24, March 2012.

C. Parker “Unexpected challenges in large scale machine learning,” in Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine '12). ACM, New York, NY, USA, 2012. pp. 1-6.

R. Genuer, J.-M. Poggi, C. Tuleau-Malot, N. Villa-Vialaneix, “Random forests and big data,” 47`eme Journ´ees de Statistique de la SFdS, Jun 2015, Lille, France. 2015.

M. I. Jordan, “On statistics, computation and scalability,” Bernoulli, vol. 19, no. 4, pp. 1378-1390, 2013.

L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.

A. Verikas, A. Gelzinis, and M. Bacauskiene, “Mining data with random forests: a survey and results of new tests,” Pattern Recognition, vol. 44, no. 2, pp. 330-349, 2011.

A. Ziegler and I. R. König, “Mining data with random forests: current options for real-world applications,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 4, no. 1, pp. 55-63, 2014.

I. Arel, D. C. Rose, and T. P. Karnowski, “Research frontier: deep machine learning--a new frontier in artificial intelligence research,” IEEE Computational Intelligence Magazine, vol. 5, no. 4, pp. 13-18, November 2010.

C. L. Philip Chen and Chung-Yang Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Information Sciences, vol. 275, pp. 314-347, 10 August 2014.

Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, 2013.

D. Yu and L. Deng, “Deep learning and its applications to signal and information processing,” IEEE Signal Processing Magazine, vol. 28, no. 1, pp. 145-154, 2011.

J. X. Dong, A. Krzyzak, and C. Y. Suen, “Fast SVM training algorithm with decomposition on very large data sets,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, pp. 603-618, 2005.

P. Pääkkönen and D. Pakkala, “Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems,” Big Data Research, vol. 2, pp. 166-186, 2015.

X. Jin, B. W. Wah, X. Cheng, and Y. Wang, “Significance and Challenges of Big Data Research,” Big Data Research, vol. 2, pp. 166-186, 2015.

Y. Ma, H. Wu, L. Wang, B. Huang, R. Ranjan, A. Zomaya, and W. Jie, “Remote sensing big data computing,” Future Generation Computing Systems, vol. 51, pp. 45-60, October 2015.

J. Cervantes, F. G. Lamont, A. López-Chau, L. R. Mazahua, J. Sergio Ruíz, “Data selection based on decision tree for SVM classification on large data sets,” Applied Soft Computing, vol. 37, pp. 787-798, December 2015.

L. Bottou, “Large-Scale Machine Learning with Stochastic Gradient Descent,” in Proceedings of COMPSTAT'2010, pp. 177-186, 30 September 2010.

K. Sopyła and P. Drozda, “Stochastic Gradient Descent with Barzilai–Borwein update step for SVM,” Information Sciences, vol. 316, pp. 218-233, 2015.

T. D. Kulkarni, P. Kohli, J. B. Tenenbaum, and V. Mansinghka, “Picture: A probabilistic programming language for scene perception,” 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4390-4399, 2015.

K. Kumar and Yung-Hsiang Lu, “Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?” Computer, vol. 43, no. 4, pp. 51-56.

A. Botta, W.de Donato, V. Persico, and A. Pescapé, “Integration of Cloud computing and Internet of Things: A survey,” Future Generation Computer Systems, 2015.

Y. Yang and X. Liu, “A robust semi-supervised learning approach via mixture of label information,” Pattern Recognition Letters, vol. 68, part 1, pp. 15-21, 15 December 2015.

Y. Zhu, E. Zhong, Z. Lu, and Q. Yang, “Feature engineering for semantic place prediction,” Pervasive and Mobile Computing, vol. 9, no. 6, pp. 772-783, December 2013.

R. Hu, B. M. Namee, and S. J. Delany, “Active learning for text classification with reusability,” Expert Systems with Applications, vol. 45, pp. 438-449, 1 March 2016.

H. Rajaona, F. Septier, P. Armand, Y. Delignon, C. Olry, A. Albergel, and J. Moussafir, “An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release,” Atmospheric Environments, vol. 122, pp. 748-762, December 2015.

N. V. Chawla, Data mining for imbalanced datasets: An overview. In Data Mining and Knowledge Discovery Handbook, Springer US, 2010, pp. 875-886.

J. Gama, R. Sebastião, and P. P. Rodrigues, “Issues in evaluation of stream learning algorithms,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, June 2009, pp. 329-338.

A. Ahmad, A. Paul, and M. M. Rathore, “An efficient divide-and-conquer approach for big data analytics in machine-to-machine communication,” Neurocomputing, vol 174, part A, pp. 439-453, 22 January 2016.

G. Suciu, A. Vulpe, O. Fratu, and V. Suciu, “M2M remote telemetry and cloud IoT big data processing in viticulture,” 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), 2015, pp. 1117-1121.

R. T. Kouzes, G. A. Anderson, S. T. Elbert, I. Gorton, and D. K. Gracio, “The changing paradigm of data-intensive computing,” Computer, vol. 42, no. 1, pp. 26-34, 2009.

A Novel Hybrid Approach for Detection of Web-Based Attacks in Intrusion Detection Systems

Abstract Views :201 | PDF Views:1

Authors

Muhammet Baykara ¹, Resul Das ¹

Affiliations
1 Department of Software Engineering, Firat University, Elazig, 23119, TR

Source

International Journal of Computer Networks and Applications, Vol 4, No 2 (2017), Pagination: 62-76

Abstract

Importance of information security systems is increasing in parallel with the rapid developments in information technology. The development of new technologies brings new security weaknesses in corporate and personal meaning can lead to unavoidable losses. For this reason, many researches have been performed in order to ensure the security of information systems. In today's world, the concept of information has been moved to the digital size from conventional size. Protection of the data stored in the digital archive and is easily accessibility at any time have become a quite important phenomenon. In this concept, intrusion detection and prevention systems as security tools are widely used today. In this paper, a hybrid real time intrusion and prevention system approach has been proposed for web applications security. The proposed system uses rule-based misuse detection and anomaly detection as intrusion detection method and uses network packets as data source. The system is real-timed with accordance to data process time, centralized with accordance to architecture, and server-based with accordance to system it protects. The developed system has been tested on the current web attacks determined by OWASP (The Open Web Application Security Project) and provides a very high success rate.

Keywords

Web Attacks, Intrusion Detection And Prevention Systems, Information Security, Network Analysis.

Full Text

References

Baykara, M., Das, R., Karadogan, I., "Bilgi Guvenligi Sistemlerinde Kullanilan Araclarin Incelenmesi", 1st International Symposium on Digital Forensics and Security, pp. 231-239, 20-22 May. 2013, Firat University, Elazig-Turkey.

Razzaq, A., Hur, A., Masood, M., Latif, K., Ahmad, H.F., Takahashi, H., "Foundation of Semantic Rule Engine to Protect Web Application Attacks," 10th International Symposium on Autonomous Decentralized Systems (ISADS), pp. 95-102, 23-27 March 2011.

Lounis, O., Bouhouita Guermeche, S.E., Saoudi, L., Benaicha, S.E., "A new algorithm for detecting SQL injection attack in Web application," Science and Information Conference (SAI), pp. 589-594, 27-29 Aug. 2014.

Liang Guangmin, "Modeling Unknown Web Attacks in Network Anomaly Detection," Third International Conference on Convergence and Hybrid Information Technology, ICCIT '08, vol.2, no., pp. 112-116, 11-13 Nov. 2008.

Ludinard, R., Totel, E., Tronel, F., Nicomette, V., Kaaniche, M., Alata, E., Akrout, R., Bachy, Y., "Detecting attacks against data in web applications", 7th International Conference on Risk and Security of Internet and Systems (CRiSIS), pp. 1-8, 10-12 Oct. 2012.

Zolotukhin, M., Hamalainen, T., Kokkonen, T., Siltanen, J., "Analysis of HTTP Requests for Anomaly Detection of Web Attacks," 12th International Conference on Dependable, Autonomic and Secure Computing (DASC), IEEE, pp. 406,411, 24-27 Aug. 2014.

Takci, H., Akyuz, T., & Sogukpinar, I., "Web Ataklari Icin Metin Tabanli Anormallik Tespiti (Wamtat)" Journal of The Faculty of Engineering and Architecture of Gazi University, Vol: 22, No: 2, pp. 247-253, 2007.

Sagiroglu, S., Guven, E.N., Yavanoglu, U., "Zeki Saldiri Tespit Sistemi Tasarimi ve Gercekleutirilmesi", Journal of The Faculty of Engineering and Architecture of Gazi University, Vol:26, No:2, pp. 325-340, 2011.

Sancak, S., "Saldiri Tespit Sistemi Tekniklerinin Karsilastirilmasi", Master Thesis, Gebze Technical University, 2008.

Baykara, M., "Design and Implementation of Intrusion Detection and Prevention Approaches for Information Systems", Ph.D Thesis, FÄ±rat University, Graduate School of Natural and Applied Sciences, Department of Software Engineering 2016.

Demirol D., Das R., Baykara M., "SQL Enjeksiyon Saldiri Uygulamasi ve Guvenlik Onerileri", 1st International Symposium on Digital Forensics and Security, 20-22 Mayis 2013, Firat University, Elazig.

Internet: Ar, I., "Nufuz Tespit Sistemleri", http://anibal.gyte.edu.tr/hebe/AblDrive/59669005/w/Storage/104_2010_2_673_59669005/Homeworks/lktan-ar-nufuz-tespit-sistemleri.pdf, (Access Date: 10.03.2017).

Vural, Y., Sagiroglu, S, "Kurumsal Bilgi Guvenligi ve Standartlari uzerine bir Inceleme", Journal of The Faculty of Engineering and Architecture of Gazi University, Vol: 23, No: 2, pp. 507-522, June 2008.

Ozhan, E., Paket ve Port Analizi Ile Ag Saldiri Tespit Sistemleri, Master Thesis, Trakya University, Graduate School of Natural and Applied Sciences, 2006.

Sazli, H., M., Tanrikulu, H., "Saldiri Tespit Sistemlerinde Yapay Sinir Aglarinin Kullanilmasi", XII. Turkiye'de Internet Konferansi, 8-10 Kasim, Ankara, 2007.

Huang at all, "A Multi-Agent-Based Distributed Intrusion Detection System", 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 2010.

Canbek, G., Sagiroglu, S., "Bilgisayar Sistemlerine Yapilan Saldirilar ve Turleri: Bir Inceleme", Erciyes University Journal of Institue of Science and Technology, 23(1-2), pp. 1-12, 2007.

Patcha, A., Park, J.M., "An overview of anomaly detection techniques: Existing solutions and latest technological trends", Computer Networks, 51(12): pp. 3448-3470, 2007.

Anderson, J.P., "Computer Security Threat Monitoring and Surveillance", Technical Report, James P. Anderson Co., Fort Washington, PA. 15 April 1980.

Aris, A., Oktug S. F. and Yalcin, S. B.O., "Nesnelerin Interneti Guvenligi: Servis Engelleme Saldirilari Internet-of-Things Security: Denial of Service Attacks", 2015.

Ogretmen, F. D., Aydin, M. A. and Ahmet Sertbas., "Saldiri Tespit Sisteminin Bulut Bilisimde Kullanimi ve Etkileri", ISC-Turkey, 30-31 October 2015.

Yavuz, G., Bektas, O., Soysal, M., and Yigit, S., "Sanal Ipv6 Balkupu Agi Altyapisi: Kovan", National IPv6 Conference 2011.

Lobato, A. G. P., da Rocha Figueiredo, U., & Duarte, O. C. M., "An Architecture for Intrusion Prevention using Software Defined Networks.", Universidade Federal do Rio de Janeiro-GTA/COPPE-Rio de Janeiro, Brazil.

Raza, S., Wallgren, L., Voight, T., "SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc networks", 11.8: pp. 2661-2674, 2013.

A. A. Gendreau and M. Moorman, "Survey of Intrusion Detection Systems towards an End to End Secure Internet of Things," IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), Vienna, pp. 84-90, 2016.

A Survey on the Internet of Things Solutions for the Elderly and Disabled:Applications, Prospects, and Challenges

Abstract Views :158 | PDF Views:1

Authors

Resul Das ¹, Ayse Tuna ², Senay Demirel ³, Meral Kayapınar Yurdakul ³

Affiliations
1 Department of Software Engineering, Firat University, Elazig-23119, TR
2 Trakya University, Edirne-22020, TR
3 NETAS A.S., Kurtkoy, Istanbul, TR

Source

International Journal of Computer Networks and Applications, Vol 4, No 3 (2017), Pagination: 84-92

Abstract

Advances in technology has not only led to the start of innovative solutions and new business opportunities in different sectors but also reduced manpower needs and operational costs. Furthermore, the quality of provided services has been improved. Therefore, recently, the Internet of Things (IoT) has gained a great momentum as a key enabling technology for a wide range of health care applications, especially for the elderly and disabled. Although, solutions based on IoT technology have started to support the elderly and disabled in many areas of their life and work and the IoT helps improve quality of life for the elderly and disabled, the amount of data collected by the IoT has increased tremendously and surpassed the expectations. This makes it necessary to investigate approaches and solutions in order to efficiently utilise large amounts of data, especially in health care applications. In this paper, we are first going to review existing approaches and IoT solutions specifically proposed and designed for the elderly and disabled. Then, we are going to investigate prospects and research challenges in the use of the IoT in the services designed for elderly people and people with disabilities to provide an insight into future research opportunities.

Keywords

The Internet of Things, Elderly People, People with Disabilities, Research Challenges, Future Research Directions.

Full Text

References

L. M. Orlov, Technology for Aging in Place: 2014 Market Overview, 2014. Available at: https://www.ageinplacetech.com/files/aip/Market%2520Overview%25201-20-2014%2520Combined.pdf

J. F. Coughlin and J. Pope, “Innovations in Health, Wellness, and Aging-in-Place: Development of a Consumer-Centered Approach to Intelligent Home Services,” IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, pp. 47-52, 2008. doi: 10.1109/MEMB.2008.919493

World Health Organization and the World Bank Group, World Report on Disability, 2008, Available at: https://www.unicef.org/protection/World_report_on_disability_eng.pdf

J. Sasaki, K.Yamada, M.Tanaka, H. Fujita, D. M. Pisanelli, R. Rasconi, L. Tiberio, and C. De Lazzari, “Improving Safety and Healthy Life of Elderly People: Italian and Japanese Experiences,” in Proceedings of the 2009 conference on New Trends in Software Methodologies, Tools and Techniques, The Netherlands, 2009, pp. 585-598.

B. Rechel, Y. Doyle, E. Grundy, and M. McKee, How can health systems respond to population ageing?, 2009. Available at: http://www.euro.who.int/_data/assets/pdf_file/0004/64966/E92560.pdf

L. R. Hudson, G. B. Hamar, P. M. Orr, J. H. Johnson, A. Neftzger, R. S. Chung, M. L. Williams, W. M. Gandy, A. Crawford, J. L. Clarke, and N. I. Goldfarb, “Remote physiological monitoring: Clinical, financial, and behavioural outcomes in a heart failure population,” Disease Management, vol. 8, no. 6, pp. 372-381, 2005.

H. Eriksson and T. Timpka, “The potential of smart homes for injury prevention among the elderly,” Injury Control and Safety Promotion, vol. 9, no. 2: 127-131, 2002.

S. Misbahuddin, H. Orabi, R. Fatta, M. Al-Juhany, and A. Almatrafi, “IoT Framework Based Health Care System for Elderly and Disabled People,” International Conference on Recent Advances in Computer Systems (RACS 2015), 2015, pp. 99-102.

I. Korhonen, J. Parkka, and M. Van Gils, “Health monitoring in the home of the future,” IEEE Engineering in Medicine and Biology Magazine, vol. 22, no. 3, pp. 66–73, 2003.

J. F. Coughlin, J. E. Pope, and B. R. Leedle, Jr., “Old age, new technology, and future innovations in disease management and home health care,” Home Health Care Management & Practice, vol. 18, no. 3, pp. 196-207, 2006.

P. Cheek, “Aging Well With Smart Technology,” Nursing Administration Quarterly, vol. 29, No. 4, pp. 329-338, 2005.

B. Celler, N. Lovell, and D. Chan. “The Potential Impact of Home Telecare of Clinical Practice”. The Medical Journal of Australia, pp. 518-521. 1999.

P. Shyamkumar, P. Rai, S. Oh, M. Ramasamy, R. Harbaugh, and V. Varadan, “Wearable Wireless Cardiovascular Monitoring Using Textile-Based Nanosensor and Nanomaterial Systems,” Electronics, vol. 3, no. 3, pp. 504-520, 2014. doi:10.3390/electronics3030504

S. M. R. Islam, D. Kwak, MD. H. Kabir, M. Hossain, and K.-S. Kwak, “The Internet of Things for Health Care: A Comprehensive Survey,” IEEE Access, vol. 3, pp. 678-708, 2015.

http://hitechhomepros.com/assistive-domotics-elderly-care-givers/

http://www.iat.uni-bremen.de/sixcms/detail.php?id=555

Z. Wang, Z. Yang, and T. Dong, “A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time,” Sensors, vol. 17, 341, 2017. doi:10.3390/s17020341

S. Rhee, B.-H. Yang, and H. Asada, “The ring sensor: A new ambulatory wearable sensor for twenty-four hour patient monitoring,” in Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1998, pp. 1906-1909.

T. Tamura, T. Togawa, M. Ogawa, and M. Yoda, “Fully automated health monitoring system in the home,” Medical Engineering & Physics, vol. 20, no. 8, pp. 573-579, 1998.

E. Domínguez-Rue, “SeniorTechs: Ageing and Technology from the Perspective of the Social Sciences,” The SIforAGE International Conference “Envisioning a New World” Social Innovation for Active and Healthy Ageing, Barcelona, 19-21 October 2016.

R. Das, G. Tuna, and A. Tuna, “Design and Implementation of a Smart Home for the Elderly and Disabled,” International Journal of Computer Networks and Applications (IJCNA), vol. 2, no. 6, pp. 242-246, 2015.

G. Tuna, R. Das, and A. Tuna, “Wireless sensor network-based health monitoring system for the elderly and disabled,” International Journal of Computer Networks and Applications (IJCNA), vol. 2, no. 6, pp. 247-253, 2015.

F. Fernandez and G. Pallis, “Opportunities and challenges of the Internet of Things for healthcare: Systems engineering perspective,” 2014 EAI 4th International Conference on Wireless Mobile Communication and Healthcare (MobiHealth 2014), 3-5 Nov. 2014, pp. 263-266.

S. S. Intille, “A new research challenge: Persuasive technology to motivate healthy aging,” IEEE Transactions on Information Technology in Biomedicine, vol. 8, no. 3, pp. 235–237, 2004.

S. Peppet, “Regulating the Internet of Things: first steps toward managing discrimination, privacy, security and consent,” Texas Law Review, vol. 93, no.1, pp. 85-176, 2014.

M. Z. Gunduz and R. Daş, “Nesnelerin Interneti: Gelisimi, Bilesenleri ve Uygulama Alanlari - Internet of Things (IoT): Evolution, Components and Applications Fields”, Pamukkale Universitesi, Muhendislik Bilimleri Dergisi, 2017. doi: 10.5505/pajes.2017.89106

J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami. “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645-1660, 2013. doi:10.1016/j.future.2013.01.010

O. Vermesan and P. Friess, Internet of Things: From research and innovation to market deployment. River, 2014.

Username
Password
Remember me

Informatics Publishing Limited

Author Details

Das, Resul

A Novel Approach for Data Privacy Using Attribute Based Scheme Algorithm for Cloud Computing

Authors

Source

Abstract

Keywords

Full Text

A Novel Routing Scheme to Avoid Link Error and Packet Dropping in Wireless Sensor Networks

Authors

Source

Abstract

Keywords

Full Text

Design and Implementation of a Smart Home for the Elderly and Disabled

Authors

Source

Abstract

Keywords

Full Text

Wireless Sensor Network-Based Health Monitoring System for the Elderly and Disabled

Authors

Source

Abstract

Keywords

Full Text

A Survey on Potential Applications of Honeypot Technology in Intrusion Detection Systems

Authors

Source

Abstract

Keywords

Full Text

Machine-to-Machine Communications for Smart Homes

Authors

Source

Abstract

Keywords

Full Text

Big Data Analysis for M2M Networks: Research Challenges and Open Research Issues

Authors

Source

Abstract

Keywords

Full Text

References

A Novel Hybrid Approach for Detection of Web-Based Attacks in Intrusion Detection Systems

Authors

Source

Abstract

Keywords

Full Text

References

A Survey on the Internet of Things Solutions for the Elderly and Disabled:Applications, Prospects, and Challenges

Authors

Source

Abstract

Keywords

Full Text

References