Rule-Based Anomaly Detection Model with Stateful Correlation Enhancing Mobile Network Security

The global Signalling System No. 7 (SS7) network protocol standard has been developed and regulated based only on trusted partner networks. SS7 by design neither secures the communication channel nor verifies entire peers. used in telecommunications deficiencies that include verification of actual subscribers, precise location, subscriber’s belonging to a network, absence illegitimate message filtering mechanism, configuration home routing Attackers can take advantage these exploit them impose threats such as subscriber or data disclosure, intercept mobile traffic, perform account frauds, track deny services. Existing methods are unable identify suspicious hosts they use minimal number parameters. So, there is vital need overcome detect abnormal behaviour users hence mitigate security attacks network. This research proposes model for anomaly detection networks Rule-based with stateful correlation. performance proposed method evaluated using synthetic datasets. Results show performs 0.37% better terms attack rate, 24.25% false alarm 31.45% true positive rate when compared existing pattern recognition Artificial Neural Network (ANN) algorithm.