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Enhancing Security Using Mobile Based Anomaly Detection in Cellular Mobile Networks

Enhancing Security Using Mobile Based Anomaly Detection in Cellular Mobile Networks. Bo Sun, Fei Yu, KuiWu , Yang Xiao, and Victor C. M. Leung. Presented by Anil Karamchandani. Introduction. Importance of Cellular phones.

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Enhancing Security Using Mobile Based Anomaly Detection in Cellular Mobile Networks

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  1. Enhancing Security Using Mobile Based Anomaly Detection in Cellular Mobile Networks Bo Sun, Fei Yu, KuiWu, Yang Xiao, and Victor C. M. Leung. Presented by Anil Karamchandani

  2. Introduction • Importance of Cellular phones. • Due to the open radio transmission environment and the physical vulnerability of mobile devices , security is a cause of concern. • 2 Approaches to protect a system • Prevention based approach • Detection based approach

  3. Prevention and Detection Based Approach Prevention based approach : Encryption and authentication – Thus allows legitimate users from entering the system. Detection based approach: IDS ( Intrusion detection systems) Misuse based detection – used to detect known used patters Anomaly based detection – 1.Used to detect known and unknown patterns. 2.Creates a profile for user behavior and path and compares it with the current activity . 3.Deviation observed is reported

  4. Goal ! • To design a mobility based anomaly detection scheme • To provide an optional service to end users. • A useful administration tool to service providers.

  5. Assumptions • There exists a mobility database for each mobile user that describes it normal activities. • Once the device has been compromised all the security details are available to the attacker . • All users have got a regular itinerary .

  6. Mobility Based Anomaly Detection Schemes • LZ Based Intrusion detection : • Markov-Based Anomaly Detection. • LZ Based Intrusion detection : • 1.Feature Extraction • 2.Optimised data compression • 3.Probability Calculation – Markov model is used . • 4.Anomaly detection algorithm

  7. LZ Based Intrusion Detection • Feature Extraction : General pattern of the cellular mobile network is formed for each user.( without data compression) • Maintenance of Data Dictionary. • Explain Data Compression. • Probability Calculation. M=1 M>1 Next event Next event depends on the only depends multiple M events in the past. on the last event in the past. • Anomaly detection algorithm. • Integration of EWMA into mobile tire.( changed frequency)

  8. Algorithm for Data Dictionary and Compression

  9. Anomaly detection algorithm

  10. Markov Based Anomaly Detection • P(X(t+1)) = N(j)/N • X(t) = state visited by the user or the users activity at time t . • N is the total number of observations • N(j) total number of observations of destination . • Eg abc – bade go from a to e = 1/1.

  11. Difference between Markov and LZ based algorithm • LZ • LZ has compression • Has EWMA • There exists a concept of Modified frequency • Markov • In Markov there is No compression • No EWMA • Only one frequency exists

  12. Conclusion (cont) • Detection Rate : • The detection rate of the LZ-based scheme is higher than those of Markov based schemes with different orders • Reason – Use of EWMA in LZ • Detection rate of all schemes increases with the increase in mobility. • Thus the detection rate is improved in case of mobility.

  13. Conclusion • False Alarm Rate : • False alarm rate of LZ is lower than that of Markov, this is due to EWMA used in LZ • As the mobility increases the false alarm rate decreases.

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