Showing posts with label Location Aware Systems. Show all posts
Showing posts with label Location Aware Systems. Show all posts

Cyber Security - Information Management System Challenges.

 



In the beginning, IMSs were primarily used to manage and store commercial data in enterprises and public organizations such as government agencies and universities. 

IMSs' emphasis and capabilities have shifted as software and hardware technologies have evolved. 

Computer and cyber security are two terms that are often used interchangeably. 

These systems now offer services that are used not just by businesses or government agencies, but also by individuals wherever and at any time. 

IMSs should now evolve to: Gather and handle large volumes of heterogeneous information. 

Evaluate and protect the privacy of sensitive pieces of information. 

Consider independent administrators distributed across different organizations. 

Manage diverse components with different requirements and locations in order to provide these services to this wide range of users. 

These facts have added to the complexity of information management procedures, necessitating further study into novel management methods that take into account the prior requirements. 

These techniques should be as automated as feasible to enable for dynamic identification of occurrences that need management process reconfiguration. 

Furthermore, automation procedures assist to decrease the complexity of managing dispersed heterogeneous components by avoiding delays in management operations caused by human errors or misconfigurations. 

 

What the Future Holds For Context & Location Aware Systems.


The complexity of the information management processes done by IMSs has expanded as technology has progressed. 

IMSs now in use handle enormous amounts of heterogeneous data, secure the privacy of sensitive data, enable many administrators to manage resources, and take into account dispersed circumstances. 

The bulk of IMSs are now consumed by individuals, businesses, and government agencies at any time and from any location. 

As a result of this fact, the location of users has become a highly significant piece of information for providing services near to the users. 

With the inclusion of location, the ubiquitous and context-aware paradigms have added additional bits of information about the environment or context in which users are, such as places, activities, identities, time, emotional states, or any other environmental data. 

The complexity of prior management procedures has risen as a result of this new heterogeneous information, influencing the birth of new automated management methods. 

Controlling the behavior of system resources, as well as managing and securing users' information in IMSs that take into account contextual data, are still unresolved concerns that need to be addressed. 

Administrators of IMSs systems should be able to take contextual information into account throughout management operations in order to make judgments about how system resources should behave. 

Furthermore, IMS users should determine and manage what information they wish to expose, as well as where, when, and with whom that information will be shared. 

In context-aware systems, semantic web approaches provide a potential solution to handle and safeguard contextual and personal information. 

This technology enables the formal modeling of data, the exchange of data across independent systems, the definition of privacy regulations to secure data, and the inference of new knowledge based on the data and policies. 

In this regard, the state-of-the-art context-aware solutions that allow for the protection of sensitive data as well as the management of system resource behavior have been discussed in this chapter. 

Following that, we used semantic web approaches to examine location-based and context-aware systems in charge of transmitting and preserving users' information in intra- and inter-context situations. 

Finally, we looked at location-based and context-aware systems for managing network resources securely, taking into account factors like QoS, energy economy, and performance. 

When administrators manage system resources, it is necessary to consider the privacy of users' information and circumstances as future work. 

Allowing users to specify the level of granularity at which they wish to divulge their position to network administrators while they are operating the network infrastructure while taking into consideration the distance and location of devices is an example of this reality. 

In terms of network administration, combining technologies such as SDN and Network Functions Virtualization (NFV) may make it easier to manage network infrastructure and services. 

In this way, the Network Slicing approach may integrate the preceding technologies to manage network resources and services based on the needs of contemporary networks. 

These slices, as well as their resources, should be handled automatically, taking into account the context.





~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram


You may also want to read and learn more Technology and Engineering here.

You may also want to read and learn more Cyber Security Systems here.




References & Further Reading:



1. OSI. Information Processing Systems-Open System Inteconnection-Systems Management Overview. ISO 10040, 1991.

2. Jefatura del Estado. Ley Orgánica de Protección de Datos de Carácter Personal. www.boe.es/boe/dias/1999/12/14/pdfs/A43088-43099.pdf.

3. D. W. Samuel, and D. B. Louis. The right to privacy. Harvard Law Review, 4(5): 193–220, 1890.

4. A. Westerinen, J. Schnizlein, J. Strassner, M. Scherling, B. Quinn, S. Herzog, A. Huynh, M. Carlson, J. Perry, and S. Waldbusser. Terminology for Policy-Based Management. IETF Request for Comments 3198, November 2001.

5. B. Moore. Policy Core Information Model (PCIM) Extensions. IETF Request for Comments 3460, January 2003.

6. S. Godik, and T. Moses. OASIS EXtensible Access Control Markup Language (XACML). OASIS Committee Specification, 2002.

7. A. Dardenne, A. Van Lamsweerde and S. Fickas. Goal-directed requirements acquisition. Science of Computer Programming, 20(1–2): 3–50, 1993.

8. F. L. Gandon, and N. M. Sadeh. Semantic web technologies to reconcile privacy and context awareness. Web Semantics: Science, Services and Agents on the World Wide Web, 1(3): 241–260, April 2004.

9. I. Horrocks. Ontologies and the semantic web. Communications ACM, 51(12): 58–67, December 2008.

10. R. Boutaba and I. Aib. Policy-based management: A historical perspective. Journal of Network and Systems Management, 15(4): 447–480, 2007.

11. P. A. Carter. Policy-Based Management, In Pro SQL Server Administration, pages 859–886. Apress, Berkeley, CA, 2015.

12. D. Florencio, and C. Herley. Where do security policies come from? In Proceedings of the 6th Symposium on Usable Privacy and Security, pages 10:1–10:14, 2010.

13. K. Yang, and X. Jia. DAC-MACS: Effective data access control for multi-authority Cloud storage systems, IEEE Transactions on Information Forensics and Security, 8(11): 1790–1801, 2014.

14. B. W. Lampson. Dynamic protection structures. In Proceedings of the Fall Joint Computer Conference, pages 27–38, 1969.

15. B. W. Lampson. Protection. ACM SIGOPS Operating Systems Review, 8(1): 18–24, January 1974.

16. D. E. Bell and L. J. LaPadula. Secure Computer Systems: Mathematical Foundations. Technical report, DTIC Document, 1973.

17. D. F. Ferraiolo, and D. R. Kuhn. Role-based access controls. In Proceedings of the 15th NIST-NCSC National Computer Security Conference, pages 554–563, 1992.

18. V. P. Astakhov. Surface integrity: Definition and importance in functional performance, In Surface Integrity in Machining, pages 1–35. Springer, London, 2010.

19. K. J. Biba. Integrity Considerations for Secure Computer Systems. Technical report, DTIC Document, 1977.

20. M. J. Culnan, and P. K. Armstrong. Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1): 104–115, 1999.

21. A. I. Antón, E. Bertino, N. Li, and T. Yu. A roadmap for comprehensive online privacy policy management. Communications ACM, 50(7): 109–116, July 2007.

22. J. Karat, C. M. Karat, C. Brodie, and J. Feng. Privacy in information technology: Designing to enable privacy policy management in organizations. International Journal of Human Computer Studies, 63(1–2): 153–174, 2005.

23. M. Jafari, R. Safavi-Naini, P. W. L. Fong, and K. Barker. A framework for expressing and enforcing purpose-based privacy policies. ACM Transaction Information Systesms Security, 17(1): 3:1–3:31, August 2014.

24. G. Karjoth, M. Schunter, and M. Waidner. Platform for enterprise privacy practices: Privacy-enabled management of customer data, In Proceedings of the International Workshop on Privacy Enhancing Technologies, pages 69–84, 2003.

25. S. R. Blenner, M. Kollmer, A. J. Rouse, N. Daneshvar, C. Williams, and L. B. Andrews. Privacy policies of android diabetes apps and sharing of health information. JAMA, 315(10): 1051–1052, 2016.

26. R. Ramanath, F. Liu, N. Sadeh, and N. A. Smith. Unsupervised alignment of privacy policies using hidden Markov models. In Proceedings of the Annual Meeting of the Association of Computational Linguistics, pages 605–610, June 2014.

27. J. Gerlach, T. Widjaja, and P. Buxmann. Handle with care: How online social network providers’ privacy policies impact users’ information sharing behavior. The Journal of Strategic Information Systems, 24(1): 33–43, 2015.

28. O. Badve, B. B. Gupta, and S. Gupta. Reviewing the Security Features in Contemporary Security Policies and Models for Multiple Platforms. In Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security, pages 479–504. IGI Global, Hershey, PA, 2016.

29. K. Zkik, G. Orhanou, and S. El Hajji. Secure mobile multi cloud architecture for authentication and data storage. International Journal of Cloud Applications and Computing 7(2): 62–76, 2017.

30. C. Stergiou, K. E. Psannis, B. Kim, and B. Gupta. Secure integration of IoT and cloud computing. In Future Generation Computer Systems, 78(3): 964–975, 2018.

31. D. C. Verma. Simplifying network administration using policy-based management. IEEE Network, 16(2): 20–26, March 2002.

32. D. C. Verma. Policy-Based Networking: Architecture and Algorithms. New Riders Publishing, Thousand Oaks, CA, 2000.

33. J. Rubio-Loyola, J. Serrat, M. Charalambides, P. Flegkas, and G. Pavlou. A methodological approach toward the refinement problem in policy-based management systems. IEEE Communications Magazine, 44(10): 60–68, October 2006.

34. F. Perich. Policy-based network management for next generation spectrum access control. In Proceedings of International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pages 496–506, April 2007.

35. S. Shin, P. A. Porras, V. Yegneswaran, M. W. Fong, G. Gu, and M. Tyson. FRESCO: Modular composable security services for Software-Defined Networks. In Proceedings of the 20th Annual Network and Distributed System Security Symposium, pages 1–16, 2013.

36. K. Odagiri, S. Shimizu, N. Ishii, and M. Takizawa. Functional experiment of virtual policy based network management scheme in Cloud environment. In International Conference on Network-Based Information Systems, pages 208–214, September 2014.

37. M. Casado, M. J. Freedman, J. Pettit, J. Luo, N. McKeown, and S. Shenker. Ethane: Taking control of the enterprise. In Proceedings of Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pages 1–12, August 2007.

38. M. Wichtlhuber, R. Reinecke, and D. Hausheer. An SDN-based CDN/ISP collaboration architecture for managing high-volume flows. IEEE Transactions on Network and Service Management, 12(1): 48–60, March 2015.

39. A. Lara, and B. Ramamurthy. OpenSec: Policy-based security using Software-Defined Networking. IEEE Transactions on Network and Service Management, 13(1): 30–42, March 2016.

40. W. Jingjin, Z. Yujing, M. Zukerman, and E. K. N. Yung. Energy-efficient base stations sleep-mode techniques in green cellular networks: A survey. IEEE Communications Surveys Tutorials, 17(2): 803–826, 2015.

41. G. Auer, V. Giannini, C. Desset, I. Godor, P. Skillermark, M. Olsson, M. A. Imran, D. Sabella, M. J. Gonzalez, O. Blume, and A. Fehske. How much energy is needed to run a wireless network?IEEE Wireless Communications, 18(5): 40–49, 2011.

42. W. Yun, J. Staudinger, and M. Miller. High efficiency linear GaAs MMIC amplifier for wireless base station and Femto cell applications. In IEEE Topical Conference on Power Amplifiers for Wireless and Radio Applications, pages 49–52, January 2012.

43. M. A. Marsan, L. Chiaraviglio, D. Ciullo, and M. Meo. Optimal energy savings in cellular access networks. In IEEE International Conference on Communications Workshops, pages 1–5, June 2009.

44. H. Claussen, I. Ashraf, and L. T. W. Ho. Dynamic idle mode procedures for femtocells. Bell Labs Technical Journal, 15(2): 95–116, 2010.

45. L. Rongpeng, Z. Zhifeng, C. Xianfu, J. Palicot, and Z. Honggang. TACT: A transfer actor-critic

learning framework for energy saving in cellular radio access networks. IEEE Transactions on Wireless Communications, 13(4): 2000–2011, 2014.

46. G. C. Januario, C. H. A. Costa, M. C. Amarai, A. C. Riekstin, T. C. M. B. Carvalho, and C. Meirosu. Evaluation of a policy-based network management system for energy-efficiency. In IFIP/IEEE International Symposium on Integrated Network Management, pages 596–602, May 2013.

47. C. Dsouza, G. J. Ahn, and M. Taguinod. Policy-driven security management for fog computing: Preliminary framework and a case study. In Conference on Information Reuse and Integration, pages 16–23, August 2014.

48. H. Kim and N. Feamster. Improving network management with Software Defined Networking. IEEE Communications Magazine, 51(2): 114–119, February 2013.

49. O. Gaddour, A. Koubaa, and M. Abid. Quality-of-service aware routing for static and mobile IPv6-based low-power and loss sensor networks using RPL. Ad Hoc Networks, 33: 233–256, 2015.

50. Q. Zhao, D. Grace, and T. Clarke. Transfer learning and cooperation management: Balancing the quality of service and information exchange overhead in cognitive radio networks. Transactions on Emerging Telecommunications Technologies, 26(2): 290–301, 2015.

51. M. Charalambides, P. Flegkas, G. Pavlou, A. K. Bandara, E. C. Lupu, A. Russo, N. Dulav, M. Sloman, and J. Rubio-Loyola. Policy conflict analysis for quality of service management. In Proceedings of the 6th IEEE International Workshop on Policies for Distributed Systems and Networks, pages 99–108, June 2005.

52. M. F. Bari, S. R. Chowdhury, R. Ahmed, and R. Boutaba. PolicyCop: An autonomic QoS policy enforcement framework for software defined networks. In 2013 IEEE SDN for Future Networks and Services, pages 1–7, November 2013.

53. C. Bennewith and R. Wickers. The mobile paradigm for content development, In Multimedia and E-Content Trends, pages 101–109. Vieweg+Teubner Verlag, 2009.

54. I. A. Junglas, and R. T. Watson. Location-based services. Communications ACM, 51(3): 65–69, March 2008.

55. M. Weiser. The computer for the 21st century. Scientific American, 265(3): 94–104, 1991.

56. G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P. Steggles. Towards a better understanding of context and context-awareness. In Handheld and Ubiquitous Computing, pages 304–307, September 1999.

57. B. Schilit, N. Adams, and R. Want. Context-aware computing applications. In Proceeding of the 1st Workshop Mobile Computing Systems and Applications, pages 85–90, December 1994.

58. N. Ryan, J. Pascoe, and D. Morse. Enhanced reality fieldwork: The context aware archaeological assistant. In Proceedings of the 25th Anniversary Computer Applications in Archaeology, pages 85–90, December 1997.

59. A. K. Dey. Context-aware computing: The CyberDesk project. In Proceedings of the AAAI 1998 Spring Symposium on Intelligent Environments, pages 51–54, 1998.

60. P. Prekop and M. Burnett. Activities, context and ubiquitous computing. Computer Communications, 26(11): 1168–1176, July 2003.

61. R. M. Gustavsen. Condor-an application framework for mobility-based context-aware applications. In Proceedings of the Workshop on Concepts and Models for Ubiquitous Computing, volume 39, September 2002.

62. C. Tadj and G. Ngantchaha. Context handling in a pervasive computing system framework. In 

Proceedings of the 3rd International Conference on Mobile Technology, Applications and Systems, 

pages 1–6, October 2006.

63. S. Dhar and U. Varshney. Challenges and business models for mobile location-based services and advertising. Communications ACM, 54(5): 121–128, May 2011.

64. F. Ricci, L. Rokach, and B. Shapira. Recommender Systems: Introduction and Challenges, pages In Recommender Systems Handbook, pages 1–34. Springer, Boston, MA, 2015.

65. J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative Filtering Recommender Systems, In The Adaptive Web, pages 291–324. Springer, Berlin, Heidelberg, 2007.

66. P. Lops, M. de Gemmis, and G. Semeraro. Content-Based Recommender Systems: State of the Art and Trends, In Recommender Systems Handbook, pages 73–105. Springer, Boston, MA, 2011.

67. D. Slamanig and C. Stingl. Privacy aspects of eHealth. In Proceedings of Conference on Availability, Reliability and Security, pages 1226–1233, March 2008.

68. C. Wang. Policy-based network management. In Proceedings of the International Conference on Communication Technology, volume 1, pages 101–105, 2000.

69. R. Want, A. Hopper, V. Falcao, and J. Gibbons. The active badge location system. ACM Transactions on Information Systems, 10(1): 91–102, January 1992.

70. K. R. Wood, T. Richardson, F. Bennett, A. Harter, and A. Hopper. Global teleporting with Java: Toward ubiquitous personalized computing. Computer, 30(2): 53–59, February 1997.

71. C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos. Context aware computing for the Internet of Things: A survey. IEEE Communications Surveys Tutorials, 16(1): 414–454, 2014.

72. B. Guo, L. Sun, and D. Zhang. The architecture design of a cross-domain context management system. In Proceedings of Conference Pervasive Computing and Communications Workshops, pages 499–504, April 2010.

73. A. Badii, M. Crouch, and C. Lallah. A context-awareness framework for intelligent networked embedded systems. In Proceedings of Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services, pages 105–110, August 2010.

74. S. Pietschmann, A. Mitschick, R. Winkler, and K. Meissner. CroCo: Ontology-based, crossapplication context management. In Proceedings of Workshop on Semantic Media Adaptation and Personalization, pages 88–93, December 2008.

75. T. Gu, X. H. Wang, H. K. Pung, and D. Q. Zhang. An ontology-based context model in intelligent environments. In Proceedings of Communication Networks and Distributed Systems Modeling and Simulation Conference, pages 270–275, January 2004.

76. H. Chen, T. Finin, and A. Joshi. An ontology for context-aware pervasive computing environments. The Knowledge Engineering Review, 18(03): 197–207, September 2003.

77. D. Ejigu, M. Scuturici, and L. Brunie. CoCA: A collaborative context-aware service platform for pervasive computing. In Proceedings of Conference Information Technologies, pages 297–302, April 2007.

78. R. Yus, E. Mena, S. Ilarri, and A. Illarramendi. SHERLOCK: Semantic management of location based services in wireless environments. Pervasive and Mobile Computing, 15: 87–99, 2014.

79. L. Tang, Z. Yu, H. Wang, X. Zhou, and Z. Duan. Methodology and tools for pervasive application development. International Journal of Distributed Sensor Networks, 10(4): 1–16, 2014.

80. B. Bertran, J. Bruneau, D. Cassou, N. Loriant, E. Balland, and C. Consel. DiaSuite: A tool suite to develop sense/compute/control applications. Science of Computer Programming, 79: 39–51, 2014.

81. P. Jagtap, A. Joshi, T. Finin, and L. Zavala. Preserving privacy in context-aware systems. In Proceedings of Conference on Semantic Computing, pages 149–153, September 2011.

82. V. Sacramento, M. Endler, and F. N. Nascimento. A privacy service for context-aware mobile computing. In Proceedings of Conference on Security and Privacy for Emergency Areas in Communication Networks, pages 182–193, September 2005.

83. A. Huertas Celdrán, F. J. García Clemente, M. Gil Pérez, and G. Martínez Pérez. SeCoMan: A 

semantic-aware policy framework for developing privacy-preserving and context-aware smart applications. IEEE Systems Journal, 10(3): 1111–1124, September 2016.

84. J. Qu, G. Zhang, and Z. Fang. Prophet: A context-aware location privacy-preserving scheme in location sharing service. Discrete Dynamics in Nature and Society, 2017, 1–11, Article ID 6814832, 2017.

85. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. PRECISE: Privacy-aware recommender based on context information for Cloud service environments. IEEE Communications Magazine, 52(8): 90–96, August 2014.

86. S. Chitkara, N. Gothoskar, S. Harish, J.I. Hong, and Y. Agarwal. Does this app really need my location? Context-aware privacy management for smartphones. In Proceedings of the ACM Interactive Mobile, Wearable and Ubiquitous Technologies, 1(3): 42:1–42:22, September 2017.

87. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. What private information are you disclosing? A privacy-preserving system supervised by yourself. In Proceedings of the 6th International Symposium on Cyberspace Safety and Security, pages 1221–1228, August 2014.

88. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. MASTERY: A multicontext-aware system that preserves the users’ privacy. In IEEE/IFIP Network Operations and Management Symposium, pages 523–528, April 2016.

89. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. Preserving patients’ privacy in health scenarios through a multicontext-aware system. Annals of Telecommunications, 72(9–10): 577–587, October 2017.

90. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. Policy-based management for green mobile networks through software-defined networking. Mobile Networks and Applications, In Press, 2016.

91. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. Enabling highly dynamic mobile scenarios with software defined networking. IEEE Communications Magazine, Feature Topics Issue on SDN Use Cases for Service Provider Networks, 55(4): 108–113, April 2017. 










What Is Artificial General Intelligence?

Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the ...