Comparison Of Supervised Machine Learning Algorithms On Detection Of Signaling Dos Attack To The 3g (umts) Mobile Network-in The Case Of Ethiotelecom

Mobile communication technology evolves overtime by introducing new architectures, interfacesrnand protocols, providing unified services with higher capacity of packet based data transmission.rnThis helps different organizations to facilitate their services using these networks. However, thesernchanges has also opened new vulnerabilities to the mobile networks including the vulnerability ofrn3G network to signaling DoS attack, which is considered as one of the most dangerous type ofrnattacks. It is a type of attack that overload mobile network elements by creating a significantrnamount of signalling messages initiated by a wake up packet sent from an attacker device. rnThe existing rule based prevention mechanisms and programed tools failed to fully protect fromrnthe type of attack considered here. Researchers propose an intrusion detection system (IDS) basedrnon cumulative sum method to detect 3G signalling DoS attack by testing the signalling rate of eachrnMS and triggers an alarm if it is above some fixed threshold. However, such a simple and fixedrnfor all thresholds could wrongly classify a heavy user as an attacker. Machine learning (ML)rntechniques have a promising capability in such regard by avoiding the rigidity of traditionalrnconfigured and programmed tools by adapting their behavior based on their inputs. Many studiesrnhave used ML approaches and compare different algorithms for the detection of diverse kinds ofrnDoS attacks towards the IP and cellular networks. Their result as well as nature of dataset used forrntheir study and methodology differ from one to the other. However, comparing different algorithmsrnfor the detection of 3G signaling DoS attack based on realistic dataset were not considered. rnThe aim of this study is to compare the performance of three supervised ML algorithms towardsrndetecting the 3G signalling DoS attack. For this purpose, three ML algorithms together with fourrnperformance metrics and data collected from the real et 3G production network were used. Thernresult shows that J48 record the best performance with an accuracy of 96.6% while RepeatedrnIncremental Pruning to Produce Error Reduction (RIPPER) deliver the second best performancernwith 95.96% of accuracy. Multilayer Perceptron’s (MLP) performance was relatively lower withrn82.39% of accuracy. All algorithms except MLP classify the provided dataset with an acceptablernperiod of time. Overall, the study shows ML techniques are effective in detecting 3G signallingrnDoS attack.

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