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Cloud-Based Intrusion Detection and Response System: Open Research Issues, and Solutions

  • Review Article - Computer Engineering and Computer Science
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Abstract

Mobile cloud computing (MCC) allows smart mobile devices (SMD) to access the cloud resources in order to offload data from smartphones and to acquire computational services for application processing. A distinctive factor in accessing cloud resources is the communication link. However, the communication links between SMD and cloud resources are weak, which allows intruders to perform malicious activities by exploiting their vulnerabilities. This makes security a key challenge in the MCC environment. Several intrusion detection and response systems (IDRSs) are adapted to address the exploitation of vulnerabilities that affect smartphones, communication links between cloud resources and smartphones, as well as cloud resources. In this article, we discuss the cloud-based IDRS in the context of SMD and cloud resources in the MCC infrastructure. The stringent security requirements are provided as open issues along with possible solutions. The article aims at providing motivations for researchers, academicians, security administrators, and cloud service providers to discover mechanisms, frameworks, standards, and protocols to address the challenges faced by cloud-based IDRS for SMD.

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References

  1. Research, A.: https://www.abiresearch.com/. Accessed September (2015)

  2. Research, J.: http://www.juniperresearch.com/press-release/cloud-computing-pr1. Accessed June (2015)

  3. Ahuja, S.P.; Rolli, A.C.: Exploring the convergence of mobile computing with cloud computing. Netw. Commun. Technol. 1(1), p97 (2012)

    Google Scholar 

  4. Fernando, N.; Loke, S.W.; Rahayu, W.: Mobile cloud computing: a survey. Future Gen. Comput. Syst. 29(1), 84–106 (2013)

    Article  Google Scholar 

  5. Sy, B.K.: Integrating intrusion alert information to aid forensic explanation: an analytical intrusion detection framework for distributive IDS. Inf. Fusion 10(4), 325–341 (2009)

    Article  MathSciNet  Google Scholar 

  6. Chang, R.-S.; et al.: Mobile cloud computing research-issues, challenges and needs. In: 2013 IEEE 7th International Symposium on Service Oriented System Engineering (SOSE). IEEE (2013)

  7. Khan, A.N.; et al.: Towards secure mobile cloud computing: a survey. Future Gener. Comput. Syst. 29(5), 1278–1299 (2013)

    Article  Google Scholar 

  8. Feizollah, A.; et al.: A review on feature selection in mobile malware detection. Digit. Investig. 13, 22–37 (2015)

    Article  Google Scholar 

  9. Liu, F.; Ren, L.; Bai, H.: Mitigating cross-VM side channel attack on multiple tenants cloud platform. J. Comput. 9(4), 1005–1013 (2014)

    Google Scholar 

  10. Enck, W.; et al.: TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones. ACM Trans. Comput. Syst. TOCS 32(2), 5 (2014)

    Google Scholar 

  11. Shabtai, A.; Kanonov, U.; Elovici, Y.: Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method. J. Syst. Softw. 83(8), 1524–1537 (2010)

    Article  Google Scholar 

  12. Houmansadr, A.; Zonouz, S.A.; Berthier, R.: A cloud-based intrusion detection and response system for mobile phones. In: 2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops (DSN-W). IEEE (2011)

  13. Modi, C.; et al.: A survey of intrusion detection techniques in cloud. J. Netw. Comput. Appl. 36(1), 42–57 (2013)

    Article  MathSciNet  Google Scholar 

  14. Shameli-Sendi, A.; et al.: Intrusion response systems: survey and taxonomy. SIGMOD Rec. 12, 1–14 (2012)

    Google Scholar 

  15. Patel, A.; et al.: An intrusion detection and prevention system in cloud computing: a systematic review. J. Netw. Comput. Appl. 36(1), 25–41 (2013)

    Article  Google Scholar 

  16. Wu, J.; et al.: C2detector: a covert channel detection framework in cloud computing. Secur. Commun. Netw. 7(3), 544–557 (2014)

    Article  Google Scholar 

  17. Slaviero, M.: BlackHat presentation demo vids: Amazon. [Online]. http://www.sensepost.com/blog/3797.html (2009)

  18. Ismail, M.N.; et al.: Detecting flooding based DoS attack in cloud computing environment using covariance matrix approach. In: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication. ACM (2013)

  19. Bahram, S.; et al.: Dksm: Subverting virtual machine introspection for fun and profit. In: 2010 29th IEEE Symposium on Reliable Distributed Systems. IEEE (2010)

  20. Zhang, Y.; et al.: Cross-VM side channels and their use to extract private keys. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security. ACM (2012)

  21. Manjunath, V.: Reverse Engineering of Malware on Android. SANS Institute InfoSec Reading Room (2011)

  22. La Polla, M.; Martinelli, F.; Sgandurra, D.: A survey on security for mobile devices. IEEE Commun. Surv. Tutor. 15(1), 446–471 (2013)

    Article  Google Scholar 

  23. Felt, A.P.; et al.: A survey of mobile malware in the wild. In: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices. ACM (2011)

  24. Bickford, J.; et al.: Rootkits on smart phones: attacks, implications and opportunities. In: Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications. ACM (2010)

  25. Schlegel, R.; K.Z.; Zhou, X.; Intwala, M.; Kapadia, A.; Wang, X.: Soundminer: a stealthy and context-aware sound Trojan for Smartphones. In: NDSS (2011)

  26. Kaspersky: http://www.kaspersky.com/about/news/virus/2016/Kaspersky-Lab-Discovers-Triada. Accessed Nov (2016)

  27. Xu, N.; et al.: Stealthy video capturer: a new video-based spyware in 3g smartphones. In: Proceedings of the Second ACM Conference on Wireless Network Security. ACM (2009)

  28. Castillo, C.A.: https://secureduniverse.com/images/android-malware-past-present-future-wp.pdf (2011)

  29. Cloud Security Alliance, Top Threats to Mobile Computing, Cloud Security Alliance (2012)

  30. Fogla, P.; et al.: Polymorphic blending attacks. In: USENIX Security (2006)

  31. Vigna, G.; Robertson, W.; Balzarotti, D.: Testing network-based intrusion detection signatures using mutant exploits. In: Proceedings of the 11th ACM Conference on Computer and communications security. ACM (2004)

  32. Mutz, D.; Vigna, G.; Kemmerer, R.: An experience developing an IDS stimulator for the black-box testing of network intrusion detection systems. In: Proceedings of the 19th Annual Computer Security Applications Conference. IEEE (2003)

  33. Rubinstein, B.I.; et al.: Antidote: understanding and defending against poisoning of anomaly detectors. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference. ACM (2009)

  34. Tsyrklevich, E.: Attacking host intrusion prevention systems. In: Black Hat USA, (2004). http://www.blackhat.com/presentations/bh-usa-04/bh-us-04-tsyrklevich.pdf.

  35. Lee, W.; et al.: Toward cost-sensitive modeling for intrusion detection and response J. Comput. Secur. 10(1–2), 5–22 (2002)

  36. Zhang, Y.; Lee, W.; Huang, Y.-A.: Intrusion detection techniques for mobile wireless networks. Wirel. Netw. 9(5), 545–556 (2003)

    Article  Google Scholar 

  37. Khan, N.; et al.: Cloud computing: architecture for efficient provision of services. In: NBiS (2012)

  38. Shiraz, M.; et al.: A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing. IEEE Commun. Surv. Tutor. 15(3), 1294–1313 (2013)

    Article  Google Scholar 

  39. Marinelli, E.E.: Hyrax: cloud computing on mobile devices using MapReduce. DTIC Document (2009)

  40. Dinh, H.T.; et al.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 13(18), 1587–1611 (2013)

    Article  Google Scholar 

  41. Liu, L.; Moulic, R.; Shea, D.: Cloud service portal for mobile device management. In: 2010 IEEE 7th International Conference on E-Business Engineering (ICEBE). IEEE (2010)

  42. Cloud Security, A.: Top Threats to Cloud Computing. Cloud Security Alliance. http://www.cloudsecurityalliance.org/csaguide.pdf, V. 1.0 (2010)

  43. Kholidy, H.A.; Baiardi, F.: CIDS: a framework for intrusion detection in cloud systems. In: 2012 Ninth International Conference on Information Technology: New Generations (ITNG). IEEE (2012)

  44. Scarfone, K.; Mell, P.: Guide to intrusion detection and prevention systems (idps. NIST Spec. Publ. 800(2007), 94 (2007)

    Google Scholar 

  45. Anwar, S.; et al.: Response option for attacks detected by intrusion detection system. In: 2015 4th International Conference on Software Engineering and Computer Systems (ICSECS). IEEE (2015)

  46. Somayaji, A.; Forrest, S.: Automated response using system-call delay. In: Proceedings of the 9th USENIX Security Symposium, Denver, Colorado, USA, 14–17 Aug 2000, pp. 185–198 (2000)

  47. Shabtai, A.; Elovici, Y.: Applying behavioral detection on android-based devices. In: Mobile Wireless Middleware, Operating Systems, and Applications, pp. 235–249. Springer (2010)

  48. Foo, B.; et al.: ADEPTS: adaptive intrusion response using attack graphs in an e-commerce environment. In: Proceedings of the International Conference on Dependable Systems and Networks. DSN 2005. IEEE (2005)

  49. Bonifacio, J.; et al.: An adaptive intrusion detection system using neural networks. In: Proceedings of the 14th International Information Security Conference (IFIP/Sec’98, Part of the 15th IFIP World Computer Congress), pp. 276–280. Austrian Computer Society, Vienna (1998)

  50. Vigna, G.; Kemmerer, R.A.: NetSTAT: s network-based intrusion detection system. J. Comput. Secur. 7(1), 37–71 (1999)

    Article  Google Scholar 

  51. Nadeem, A.; Howarth, M.: Protection of MANETs from a range of attacks using an intrusion detection and prevention system. Telecommun. Syst. 52(4), 2047–2058 (2013)

    Article  Google Scholar 

  52. Wai, F.H.; Y.N.A.; James, N.H.: Intrusion Detection in Wireless Ad-Hoc Networks. www.projapps.com/CS4274.pdf (2003)

  53. Nadeem, A.; Howarth, M.P.: An intrusion detection & adaptive response mechanism for MANETs. Ad Hoc Netw. 13, 368–380 (2014)

    Article  Google Scholar 

  54. Inayat, Z.; et al.: Intrusion response systems: foundations, design, and challenges. J. Netw. Comput. Appl. 62, 53–74 (2016)

    Article  Google Scholar 

  55. Stakhanova, N.; Basu, S.; Wong, J.: A taxonomy of intrusion response systems. Int. J. Inf. Comput. Secur. 1(1), 169–184 (2007)

    Google Scholar 

  56. Butun, I.; Morgera, S.D.; Sankar, R.: A survey of intrusion detection systems in wireless sensor networks. IEEE Commun. Surv. Tutor. 16(1), 266–282 (2014)

    Article  Google Scholar 

  57. Asosheh, A.; Ramezani, N.: A comprehensive taxonomy of DDOS attacks and defense mechanism applying in a smart classification. WSEAS Trans. Comput. 7(7), 281–290 (2008)

    Google Scholar 

  58. Shameli-Sendi, A.; Cheriet, M.; Hamou-Lhadj, A.: Taxonomy of intrusion risk assessment and response system. Comput. Secur. 45, 1–16 (2014)

    Article  Google Scholar 

  59. Zonouz, S.; et al.: Secloud: a cloud-based comprehensive and lightweight security solution for smartphones. Comput. Secur. 37, 215–227 (2013)

    Article  Google Scholar 

  60. Kitanov, S.; Davcev, D.: Mobile cloud computing environment as a support for mobile learning. In: Cloud Computing 2012, the Third International Conference on Cloud Computing, GRIDs, and Virtualization (2012)

  61. Jamaluddin, J.; et al.: Mobile phone vulnerabilities: a new generation of malware. In: 2004 IEEE International Symposium on Consumer Electronics. IEEE (2004)

  62. Khune, R.; Thangakumar, J.: A cloud-based intrusion detection system for Android smartphones. In: International Conference on Radar, Communication and Computing, India (2012)

  63. Test, A.: The best antivirus software for android. http://www.av-test.org/en/antivirus/mobile-devices/ (2015)

  64. Biever, C.: Phone viruses: how bad is it? http://www.newscientist.com/article.ns?id=dn7080 (2005)

  65. Boukerche, A.; Annoni Notare, M.S.M.: Behavior-based intrusion detection in mobile phone systems. J. Parallel Distrib. Comput. 62(9), 1476–1490 (2002)

    Article  MATH  Google Scholar 

  66. Hamad, H.; Al-Hoby, M.: Managing intrusion detection as a service in cloud networks. Int. J. Comput. Appl. 41(1), 35–40 (2012)

  67. Bugiel, S.; et al.: Xmandroid: a new android evolution to mitigate privilege escalation attacks. Technische Universität Darmstadt, Technical Report TR-2011-04 (2011)

  68. Oberheide, J.; et al.: Virtualized in-cloud security services for mobile devices. In: Proceedings of the First Workshop on Virtualization in Mobile Computing. ACM (2008)

  69. Portokalidis, G.; et al.: Paranoid android: versatile protection for smartphones. In: Proceedings of the 26th Annual Computer Security Applications Conference. ACM (2010)

  70. Modi, C.; et al.: Bayesian Classifier and Snort based network intrusion detection system in cloud computing. In: 2012 Third International Conference on Computing Communication & Networking Technologies (ICCCNT). IEEE (2012)

  71. Anwar, S.; et al.: A review paper on botnet and botnet detection techniques in cloud computing. ISCI (2014)

  72. Cheng, J.; et al.: Smartsiren: virus detection and alert for smartphones. In: Proceedings of the 5th International Conference on Mobile Systems, Applications and Services. ACM (2007)

  73. Bose, A.; et al.: Behavioral detection of malware on mobile handsets. In: Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services. ACM (2008)

  74. Burguera, I.; Zurutuza, U.; Nadjm-Tehrani, S.: Crowdroid: behavior-based malware detection system for android. In: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices. ACM (2011)

  75. Wu, Z.; Zhou, X.; Xu, J.: A result fusion based distributed anomaly detection system for android smartphones. J. Netw. 8(2), 273–282 (2013)

    Google Scholar 

  76. AVG Mobilation, A.F.; https://play.google.com/store/apps/details?id=com.antivirus&hl=en. Accessed 30 Aug (2016)

  77. Bakshi, A.; Yogesh, B.: Securing cloud from DDOS attacks using intrusion detection system in virtual machine. In: Second International Conference on Communication Software and Networks. ICCSN’10. IEEE (2010)

  78. Khan, S.; et al.: A comprehensive review on adaptability of network forensics frameworks for mobile cloud computing. Sci. World J. 2014, 547062 (2014). doi:10.1155/2014/547062

  79. Zissis, D.; Lekkas, D.: Addressing cloud computing security issues. Future Gener. Comput. Syst. 28(3), 583–592 (2012)

    Article  Google Scholar 

  80. Forrest, S.; Hofmeyr, S.; Somayaji, A.: The evolution of system-call monitoring. In: 2008 Annual Computer Security Applications Conference. ACSAC 2008. IEEE (2008)

  81. Dass, M.; Cannady, J.; Potter, W.D.: LIDS: learning intrusion detection system. In: FLAIRS Conference (2003)

  82. Miettinen, M.; Halonen, P.; Hatonen, K.: Host-based intrusion detection for advanced mobile devices. In: 20th International Conference on Advanced Information Networking and Applications. AINA 2006. IEEE (2006)

  83. Lee, J.-H.; et al.: Multi-level intrusion detection system and log management in cloud computing. In: 2011 13th International Conference on Advanced Communication Technology (ICACT). IEEE (2011)

  84. Nadeem, A.; Howarth, M.: Adaptive intrusion detection & prevention of denial of service attacks in MANETs. In: Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly. ACM (2009)

  85. Patcha, A.; Park, J.-M.: An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007)

    Article  Google Scholar 

  86. Bass, T.: Intrusion detection systems and multisensor data fusion. Commun. ACM 43(4), 99–105 (2000)

    Article  Google Scholar 

  87. Snapp, S.R.; et al.: DIDS (distributed intrusion detection system)-motivation, architecture, and an early prototype. In: Proceedings of the 14th National Computer Security Conference. Citeseer (1991)

  88. Cuppens, F.; Miege, A.: Alert correlation in a cooperative intrusion detection framework. In: Proceedings 2002 IEEE Symposium on Security and Privacy. IEEE (2002)

  89. Kruegel C.; Valeur F.; Vigna G.: Intrusion Detection and Correlation. Challenges and Solutions. Advances in Information Security, vol. 14. Springer (2005)

  90. Sadoddin, R.; Ghorbani, A.: Alert correlation survey: framework and techniques. In: Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services. ACM (2006)

  91. cloud, S.: https://cloudsecurityalliance.org/.../SecaaS-Network-Security-Peer-Revie... (2014). Accessed 15 Dec 2014

  92. Maybury, M.; et al.: Analysis and detection of malicious insiders. DTIC Document (2005)

  93. Scott, S.J.; Snort Enterprise Implementation. http://www.superhac.com/docs/snort_enterprise.pdf (2003)

  94. Grobauer, B.; Walloschek, T.; Stöcker, E.: Understanding cloud computing vulnerabilities. IEEE Secur. Priv. 9(2), 50–57 (2011)

    Article  Google Scholar 

  95. Zawoad, S.; Hasan, R.: Cloud forensics: a meta-study of challenges, approaches, and open problems. arXiv preprint arXiv:1302.6312 (2013)

  96. Sommer, R.; Paxson, V.: Outside the closed world: On using machine learning for network intrusion detection. In: 2010 IEEE Symposium on Security and Privacy (SP). IEEE (2010)

  97. Lindqvist, U.; Jonsson, E.: How to systematically classify computer security intrusions. In: Proceedings of the 1997 IEEE Symposium on Security and Privacy. IEEE (1997)

  98. Tianfield, H.: Security issues in cloud computing. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE (2012)

  99. Arshad, J.; Townend, P.; Xu, J.: A novel intrusion severity analysis approach for Clouds. Future Gener. Comput. Syst. 29(1), 416–428 (2013)

    Article  Google Scholar 

  100. Visintine, V.: An Introduction to Information Risk Assessment. GSEC Practical, Version 1.4b. SANS Institute (2003)

  101. Savage, S.; et al.: Practical network support for IP traceback. In: Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, pp. 295–306. ACM, New York (2000)

  102. Duncan, A.J.; Creese, S.; Goldsmith, M.: Insider attacks in cloud computing. In: 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE (2012)

  103. Mazzariello, C.; Bifulco, R.; Canonico, R.: Integrating a network ids into an open source cloud computing environment. In: Sixth International Conference on Information Assurance and Security, USA, 23–25 Aug 2010, pp. 265–270 (2010). doi:10.1109/ISIAS.2010.5604069

  104. Valdes, A.; Skinner, K.: Probabilistic alert correlation. In: Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection, pp. 54–68. Springer, London (2001)

  105. Julisch, K.: Clustering intrusion detection alarms to support root cause analysis. ACM Trans. Inf. Syst. Secur. TISSEC 6(4), 443–471 (2003)

    Article  Google Scholar 

  106. Corona, I.; Giacinto, G.; Roli, F.: Adversarial attacks against intrusion detection systems: taxonomy, solutions and open issues. Inf. Sci. 239, 201–225 (2013)

    Article  Google Scholar 

  107. Morin, B.; Debar, H.: Correlation of intrusion symptoms: an application of chronicles. In: Vigna G., Kruegel C., Jonsson E. (eds.) Recent Advances in Intrusion Detection. RAID 2003. Lecture Notes in Computer Science, vol 2820, pp. 94–112. Springer, Berlin (2003)

  108. Zhang, Y.; Paxson, V.: Detecting Stepping Stones. In: USENIX Security Symposium (2000)

  109. Ning, P.; Cui, Y.; Reeves, D.S.: Constructing attack scenarios through correlation of intrusion alerts. In: Proceedings of the 9th ACM Conference on Computer and Communications Security, pp. 245–254. ACM (2002)

  110. Qin, X.; Lee, W.: Statistical causality analysis of infosec alert data. In: Proceedings of Recent Advances in Intrusion Detection (RAID). Springer, pp. 73–93 (2003)

  111. Bouzar-Benlabiod, L.; Benferhat, S.; Bouabana-Tebibel, T.: Instantiated First Order Qualitative Choice Logic for an efficient handling of alerts correlation. Intell. Data Anal. 19(1), 3–27 (2015)

    Google Scholar 

  112. Alsubhi, K.; Al-Shaer, E.; Boutaba, R.: Alert prioritization in intrusion detection systems. In: Network Operations and Management Symposium. NOMS 2008, pp. 33–40. IEEE (2008)

  113. Project, O.W.A.S.; Cloud-10 Multi Tenancy and Physical Security. https://www.owasp.org/index.php/Cloud-10_Multi_Tenancy_and_Physical_Security. Accessed 12 Feb 2015 (2015)

  114. Meng, Y.; Kwok, L.: Adaptive false alarm filter using machine learning in intrusion detection. In: Wang, Y., Li, T. (eds.) Practical Applications of Intelligent Systems. Advances in Intelligent and Soft Computing, vol. 124. Springer, Berlin, Heidelberg (2011)

  115. Tjhai G.C.; Papadaki M.; Furnell, S.M.; Clarke, N.L.: The problem of false alarms: evaluation with Snort and DARPA 1999 Dataset. In: Furnell, S., Katsikas, S.K., Lioy, A. (eds.) Trust, Privacy and Security in Digital Business. TrustBus 2008. Lecture Notes in Computer Science, vol. 5185, pp. 139–150. Springer, Berlin, Heidelberg (2008)

  116. Perdisci, R.; Giacinto, G.; Roli, F.: Alarm clustering for intrusion detection systems in computer networks. Eng. Appl. Artif. Intell. 19(4), 429–438 (2006)

    Article  Google Scholar 

  117. Heyman, T.; et al.: Improving intrusion detection through alert verification. In: WOSIS (2006)

  118. Kruegel, C.; Robertson, W.K.: Alert verification determining the success of intrusion attempts. In: DIMVA (2004)

  119. Eberle, W.; Graves, J.; Holder, L.: Insider threat detection using a graph-based approach. J. Appl. Secur. Res. 6(1), 32–81 (2010)

    Article  Google Scholar 

  120. Khorshed, M.T.; Ali, A.; Wasimi, S.A.: A survey on gaps, threat remediation challenges and some thoughts for proactive attack detection in cloud computing. Future Gener. Comput. Syst. 28(6), 833–851 (2012)

    Article  Google Scholar 

  121. Carey, N.; Clark, A.; Mohay, G.: IDS interoperability and correlation using IDMEF and commodity systems. In: Deng R., Bao F., Zhou J., Qing S. (eds.) Information and Communications Security. ICICS 2002. Lecture Notes in Computer Science, vol 2513, pp. 252–264. Springer, Berlin (2002)

  122. Garfinkel, T.; Rosenblum, M.: A virtual machine introspection based architecture for intrusion detection. In: NDSS (2003)

  123. Ioannis, K.; Dimitriou, T.; Freiling, F.C.: Towards intrusion detection in wireless sensor networks. In: Proceedings of the 13th European Wireless Conference (2007)

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Authors and Affiliations

  1. Center for Mobile Cloud Computing Research (C4MCCR), Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Zakira Inayat, Abdullah Gani & Nor Badrul Anuar

  2. Department of Computer Science, University of Engineering and Technology Peshawar, Peshawar, 2500, Pakistan

    Zakira Inayat

  3. Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300, Gambang, Malaysia

    Shahid Anwar

  4. Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh, Saudi Arabia

    Muhammad Khurram Khan

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  1. Zakira Inayat
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  2. Abdullah Gani
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  4. Shahid Anwar
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  5. Muhammad Khurram Khan
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Inayat, Z., Gani, A., Anuar, N.B. et al. Cloud-Based Intrusion Detection and Response System: Open Research Issues, and Solutions. Arab J Sci Eng 42, 399–423 (2017). https://doi.org/10.1007/s13369-016-2400-3

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