Intrusion detection techniques for mobile cloud computing in heterogeneous 5G

Security and Communication Networks

Volumn 9, Issue 16, 2016, Pages 3049-3058

  Gai, Keke ^a   Qiu, Meikang ^a   Tao, Lixin ^a   Zhu, Yongxin ^b  

^a PACE UNIVERSITY   (United States)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

heterogeneous 5G; intrusion detection; mobile cloud computing; security

Indexed keywords

5G MOBILE COMMUNICATION SYSTEMS; COPYRIGHTS; INTRUSION DETECTION; NETWORK SECURITY; WEB SERVICES; WIRELESS TELECOMMUNICATION SYSTEMS;

FIFTH GENERATION (5G); HETEROGENEOUS 5G; INTRUSION DETECTION SYSTEMS; MOBILE CLOUDS; MOBILE TECHNOLOGY; SECURITY; SECURITY ISSUES; WIRELESS COMMUNICATIONS;

MOBILE CLOUD COMPUTING;

EID: 84923009701     PISSN: 19390114     EISSN: 19390122     Source Type: Journal    
DOI: 10.1002/sec.1224     Document Type: Article

References (87)

1. Dihal S, Bouwman H, Reuver MD, Warnier M, Carlsson C. Mobile cloud computing: state of the art and outlook. Info, Emerald Group Publishing Limited 15 2013; (1): 4–16.
2. Wang W, Yu C, Rashid A, Chuang H. Toward the trend of cloud computing. Journal of Electronic Commerce Research 2011; 12(4): 238–242.
3. Ahujia SP, Rolli AC. Exploring the convergence of mobile computing with cloud computing. Network and Communication Technologies 2012; 1(1): 97–102.
4. Kumar K, Lu Y. Cloud computing for mobile users: can offloading computation save energy?Computer 2010; 43(4): 51–56.
5. Shiraz M, Gani A, Khokhar RH, Buyya R. A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing. IEEE Communications Surveys & Tutorials 2013; 15(3): 1294–1313.
6. Miettinen AP, Nurminen JK. Energy efficiency of mobile clients in cloud computing. Proceedings of the 2nd USENIX Workshop on Hot Topics in Cloud Computing, Boston, MA, 2010; 1–4.
7. Simoens P, Turck FD, Dhoedt B, Demeester P. Remote display solutions for mobile cloud computing. IEEE Internet Computing 2011; 13(5): 46–53.
8. Qiu M, Su H, Chen M, Ming Z, Yang LT. Balance of security strength and energy for a PMU monitoring system in smart grid. IEEE Communications Magazine 50 2012; (5): 142–149.
9. Qiu M, Gao W, Chen M, Niu J, Zhang L. Energy efficient security algorithm for power grid wide area monitoring system. IEEE Transactions on Smart Grid 2011; 2(4): 715–723.
10. Qiu M, Zhang L, Ming Z, Chen Z, Qin X, Yang LT. Security-aware optimization for ubiquitous computing systems with SEAT graph approach. Journal of Computer and System Sciences 2013; 79(5): 518–529.
11. Asrani P. Mobile cloud computing. International Journal of Engineering and Advanced Technology 2013; 2(4): 606–609.
12. Fernando N, Loke SW, Rahayu W. Mobile cloud computing: a survey. Journal of Future Generation Computer Systems 29 2013; (1): 84–106.
13. Latif SA, Hakak S, Amin G. A review on mobile cloud computing and issues in it. International Journal of Computer Applications 75 2013; (11): 1–4.
14. Satyanarayanan M. Mobile computing: the next decade. ACM SIGMOBILE Mobile Computing and Communications Review 2011; 15(2): 2–10.
15. Khalifa I, El-Sayed H, El-Gaber IA. Enhanced mobile cloud computing platform. International Journal of Computer Applications 2013; 63(7): 12–18.
16. Christensen JH. Using RESTful web-services and cloud computing to create next generation mobile applications. Proceedings of the 24th ACM SIGPLAN Conference Companion on Object Oriented Programming Systems Languages and Applications, Orlando, Florida, USA, 2011; 627–634.
17. Cuddy C. Mobile computing. Journal of Electronic Resources in Medical Libraries 6 2009; (1): 64–68.
18. Rubin Y, Guy N, Shachor G, Kallner S, Ben-Harrush I. Puremeap - a mobile enterprise application platform: a bird's-eye view of the software architecture. Proceedings of the 2013 ACM Workshop on Mobile Development Lifecycle, Indianapolis, Indiana, USA; 17–18, 2013.
19. Jararweh Y, Tawalbeh L, Ababneh F, Khreishah A, Dosari F. Scalable cloudlet-based mobile computing model. Proceedings of the 9th International Conference on Future Networks and Communications (FNC'14)/The 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC'14)/Affiliated Workshops, Niagara Falls, Ontario, Canada; 434–441, 2014.
20. Deepak G, Pradeep BS. Challenging issues and limitations of mobile computing. International Journal of Computer Technology and Applications 2012; 3(1): 177–181.
21. Kaikkonen A. Mobile Internet: past, present, and the future. International Journal of Mobile Human Computer Interaction 2009; 1:29–45.
22. Chinnici R, Moreau JJ, Ryman A, Weerawarana S. Web services description language (WSDL) version 2.0 part 1: core language. W3C, 2007. http://www.w3.org/TR/2006/CR-wsdl20-20060327/wsdl20-z.pdf [Accessed on December 2014].
23. Messaoud EHB, Diouri O. Web service security: overview, analysis and challenges. International Journal of Computer Science Issues 2014; 11(2): 124–138.
24. Mahanti A, Sen S. High-performance mobile internet. IEEE Internet Computing 2014; 18(1): 8–11.
25. Hu F, Qiu M, Li J, Grant T, Taylor D, McCaleb S. et al. A review on cloud computing: design challenges in architecture and security. Journal of Computing and Information Technology 2011; 19(1): 25–55.
26. Gai K, Li S. Towards cloud computing: a literature review on cloud computing and its development trends. 2012 Fourth International Conference on Multimedia Information Networking and Security, Nanjing, China. IEEE; 142–146, 2012.
27. Marozzo F, Talia D, Trunfio P. P2P-MapReduce: parallel data processing in dynamic cloud environments. Journal of Computer and System Sciences 2012; 78(5): 1382–1402.
28. Masdari M, Zebardast B, Lotfi Y. Towards virtualization in cloud computing. International Journal of Advanced Research in Computer Science 2013; 4(3): 96–101.
29. Wang Y, Sun W, Zhou S, Pei X, Li X. Key technologies of distributed storage for cloud computing. Journal of Software 2012; 23(4): 232–242.
30. Zhang S, Yan H, Chen X, Research on key technologies of cloud computing. In Physics Procedia. Elsevier: Beijing, China, 2012; 1791–1797.
31. Baroncelli F, Martini B, Castoldi P. Network virtualization for cloud computing. Annals of Telecommunications 2010; 65(11–12): 713–721.
32. Ma Z, Sheng Z, Gu L. DVM: a big virtual machine for cloud computing. IEEE Transactions on Computers 2013; 63(9): 2245–2258.
33. Luo Y. Network I/O virtualization for cloud computing. IT Professional Magazine 2010; 12(5): 36–41.
34. Chaudhary D, Chhillar RS. Reverse host allocation approach for virtual machine cloud computing environment. International Journal of Computer Applications 2013; 72(17): 45–49.
35. Langer SG, French T. Virtual machine performance benchmarking. Journal of Digital Imaging 2011; 24(5): 883–889.
36. Bright PP, Bijolin EE. Energy efficient virtual machine monitoring architecture for green cloud computing. International Journal of Computer Applications 2013; 65(3): 15–18.
37. Lovsz G, Niedermeier F, Meer HD. Performance tradeoffs of energy-aware virtual machine consolidation. Cluster Computing 2013; 16(3): 481–496.
38. Zhang F, Chen H. Security-preserving live migration of virtual machine in the cloud. Journal of Network and Systems Management 2013; 21:562–587.
39. Wang Z, Liu M, Zhang S, Qiu M. Sensor virtualization for underwater event detection. Journal of Systems Architecture 2014; 60(8): 619–629.
40. Modi C, Patel D, Borisaniya B, Patel H. A survey of instrusion detection techniques in cloud. Journal of Network and Computer Applications 2013; 36:42–57.
41. Liao H, Lin CR, Lin Y, Tung K. Intrusion detection system: a comprehensive review. Journal of Network and Computer Applications 2013; 36:16–24.
42. Zhang W, Liu J, Liu C, Zheng Q, Zhang W. Workload modeling for virtual machine-hosted application. Expert Systems with Applications 2014; 42(4): 1835–1844.
43. Lin Q, Qi Z, Wu J, Dong Y, Guan H. Optimizing virtual machines using hybrid virtualization. Journal of Systems and Software 2012; 85:2593–2603.
44. Liao X, Li H, Jin H, Hou H, Jiang Y, Liu H. VMStore: distributed storage system for multiple virtual machines. Science China Information Sciences 2011; 54(6): 1104–1118.
45. Chockler G, Guerraoui R, Keidar I, Vukolic M. Reliable distributed storage. IEEE Computer 2009; 42(4): 60–67.
46. Gani A, Nayeem GM, Shiraz M, Sookhak M, Whaiduzzaman M, Khan S. A review on interworking and mobility techniques for seamless connectivity in mobile cloud computing. Journal of Network and Computer Applications 2014; 43:84–102.
47. Tapkire MD, Patil BM, Chandode VM. Parallel data processing in the cloud using nephele. International Journal of Computer Applications 2013; 69(16): 1–8.
48. Dobre C, Xhafa F. Parallel programming paradigms and frameworks in big data era. International Journal of Parallel Programming 2014; 42(5): 710–738.
49. Jasim AA, Mawjoud SA. LTE heterogeneous network: a case study. International Journal of Computer Applications 2013; 61(8): 1–6.
50. Lei W, Hai W, Yu Y, Fei Z. Heterogeneous network in LTE-advanced system. Proceedings of 2010 IEEE International Conference on Communication Systems, Singapore; 2010; 156–160.
51. Tian Y, Lin C, Li K. Managing performance and power consumption tradeoff for multiple heterogeneous servers in cloud computing. Cluster Computing 2014; 17:943–955.
52. Barbu OE, Fratu O. Anenabler of interoperability in heterogeneous wireless networks. 2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology, Chennai; 2011; 1–5.
53. Patil AA, Patil SR. Intrusion detection system using traffic prediction model. International Journal of Computer Applications 2014; 95(23): 25–28.
54. Ochieng N, Mwangi W, Ategy I. A tour of the computer worm detection space. International Journal of Computer Applications 2014; 104(1): 29–33.
55. Cao D, Qiu M, Chen Z, Hu F, Zhu Y, Wang B. Intelligent fuzzy anomaly detection of malicious software. International Journal of Advanced Intelligence 2012; 4(1): 69–86.
56. Catania CA, Garino CG. Automatic network intrusion detection: current techniques and open issues. Computers and Electrical Engineering 2012; 38:1062–1072.
57. Qiu M, Wu J, Huang H, Li W. Hardware Controlled Systematic Approach to Detect and Prevent Virus, Handbook of Security and Networks, 2011, 285–299.
58. Stamouli I, Argyroudis PG, Tewari H. Real-time intrusion detection for ad hoc networks. Proceedings of the sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks, Oak Ridge National Laboratory, TN, USA, 2005; 374–380.
59. Ocampo F, Castillo T, Gomez M. Automated signature creator for a signature based intrusion detection system with network attack detection capabilities. International Journal of Cyber-Security and Digital Forensics 2013; 2:25–35.
60. Anbar M, Manickam S, Hosam A, Chai K, Baklizi M, Almomani A. Behaviour based worm detection and signature automation. Journal of Computer Science 2011; 7:1724–1728.
61. Cherniak A, Zadorozhny VI. Signature-based detection of notable transitions in numeric data streams. IEEE Transactions on Knowledge and Data Engineering 2013; 25(12): 2867–2879.
62. Meng W, Li W, Kwok L. EFM: enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism. Computers & Security 2014; 43:189–204.
63. Meng W, Li W, Kwok L. EFM: Enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism. Computers & Security 2014; 43189–204.
64. Nagarajan N. An anomaly-based network intrusion detection system using fuzzy logic. International Journal of Computer Science and Information Security 2010; 8:185–193.
65. Nagarajan N. An anomaly-based network intrusion detection system using fuzzy logic. International Journal of Computer Science and Information Security 2010; 8:185–193.
66. Sha W, Zhu Y, Huang T, Qiu M, Zhu Y, Zhang Q. Amulti-order markov chain based scheme for anomaly detection. 2013 IEEE 37th Annual Computer Software and Applications Conference Workshops (COMPSACW), IEEE, Japan. 2013; 83–88.
67. Kheyri D, Karami M. A comprehensive survey on anomaly-based intrusion detection in MANET. Computer and Information Science 2012; 5(4): 132–139.
68. Chora M, Saganowski L, Renk R, Houbowicz W. Statistical and signal based network traffic recognition for anomaly detection. Expert Systems. 2012; 29(3): 232–245.
69. George A. Anomaly detection based on machine learning: dimensionality reduction using PCA and classification using SVM. International Journal of Computer Applications 2012; 47(21): 5–8.
70. Wagh SK, Pachghare VK, Kolhe SR. Survey on intrusion detection system using machine learning techniques. International Journal of Computer Applications 2013; 78(16): 30–37.
71. Omar S, Ngadi A, Jebur HH. Machine learning techniques for anomaly detection: an overview. International Journal of Computer Applications 2013; 79(2): 33–41.
72. Shakhatreh AYI, Bakar KA. A review of clustering techniques based on machine learning approach in intrusion detection systems. International Journal of Computer Science Issues 2011; 8(5): 373–381.
73. Shakhatreh AYI, Bakar KA. A review of clustering techniques based on machine learning approach in intrusion detection systems. International Journal of Computer Science Issues 2011; 8(5): 373–381.
74. Stakhanova N, Basu S, Wong J. On the symbiosis of specification-based and anomaly-based detection. Computer & Security 2010; 29:253–268.
75. Stakhanova N, Basu S, Wong J. On the symbiosis of specification-based and anomaly-based detection. Computer & Security 2010; 29:253–268.
76. Whitman ME, Mattord HJ. Principles of Information Security 4th edn. Information Security Professionals: Boston, MA, 2009.
77. Frederick KK. Network intrusion detection signatures, part five. Symantec, November 2010. http://www.symantec.com/connect/articles/network-intrusion-detection-signatures-part-five [Accessed on December 2014].
78. Scarfone K, Mell P. Guide to Intrusion Detection and Prevention Systems (IDPS), National Institute of Standards and Technology: Gaithersburg, MD, 2007. http://csrc.nist.gov/publications/nistpubs/800-94/SP800-94.pdf [Accessed on December 2014].
79. Gogoi P, Bhattacharyya DK, Borah B, Kalita K. MLH-IDS: a multi-level hybrid intrusion detection method. The Computer Journal 2014; 57:602–623.
80. Nadeem A, Howarth M. Protection of MANETs from a range of attacks using an intrusion detection and prevention system. Telecommunication Systems 2013; 52(4): 2047–2058.
81. Altinkemer K, Tawei W. Cost and benefit analysis of authentication systems. Decision Support System 2011; 51(3): 394–404.
82. Li J, Ding Y, Xiong Z, Liu S. An improved biometric-based user authentication scheme for C/S system. International Journal of Distributed Sensor Networks 2014; 2014:1–9.
83. Teh PS, Teoh ABJ, Yue S. A survey of keystroke dynamics biometrics. The Scientific World Journal 2013; 2013:1–24.
84. Bhattacharyya D, Ranjan R, Alisherov FA, Choi M. Biometric authentication: a review. International Journal of U- and E-Service, Science andTechnology 2009; 2(3): 13–27.
85. Babaeizadeh M, Bakhtiari M, Maarof MA. Keystroke dynamic authentication in mobile cloud computing. International Journal of Computer Applicatios 2014; 90(1): 29–36.
86. Frank M, Biedert R, Ma E, Martinovic I, Song D. Touchalytics: on the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Transactions on Information Forensics and Security 2014; 8(1): 136–148.
87. Shao Z, Xue C, Zhuge Q, Qiu M, Xiao B, Sha E. Security protection and checking for embedded system integration against buffer overflow attacks via hardware/software. IEEE Transactions on Computers 2006; 55(4): 443–453.