2016
DOI: 10.1017/s0269964816000140
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Detecting Network-Unfriendly Mobiles With the Random Neural Network

Abstract: Mobile networks are universally used for personal communications, but also increasingly used in the Internet of Things and machine-to-machine applications in order to access and control critical services. However, they are particularly vulnerable to signaling storms, triggered by malfunctioning applications, malware or malicious behavior, which can cause disruption in the access to the infrastructure. Such storms differ from conventional denial of service attacks, since they overload the control plane rather t… Show more

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Cited by 4 publications
(7 citation statements)
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“…The two proposed attack detection mechanisms were implemented in a simulation environment and their evaluation showed satisfactory results of 95% true positive and 0.04% false positive detection. Recent work has used the Random Neural Network [13] for attack detection [2] and we expect that further results will become available with similar machine learning techniques.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The two proposed attack detection mechanisms were implemented in a simulation environment and their evaluation showed satisfactory results of 95% true positive and 0.04% false positive detection. Recent work has used the Random Neural Network [13] for attack detection [2] and we expect that further results will become available with similar machine learning techniques.…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, while work in [21,33] focuses on a general defensive approach against DoS attacks in future networks, signalling storm specific research can roughly be categorised in the following groups: problem definition and attacks classification [5,30,31,41]; measurements in real operating networks [11,40]; modelling and simulation [1,27]; impact of attacks on energy consumption [10,12]; attacks detection and mitigation, using counters [19,20,38], change-point detection techniques [32,42], IP packet analysis [28], randomisation in RRC's functions [45], software changes in the mobile terminal [8,34], monitoring terminal's bandwidth usage [39], and detection using techniques from Artificial Intelligence [2]. As we look to the future, such as the Internet of Things (IoT), various forms of attacks will also have to be considered [6,9].…”
Section: Introductionmentioning
confidence: 99%
“…The two proposed attack detection mechanisms were implemented in a simulation environment and their evaluation showed satisfactory results of 95% true positive and 0.04% false positive detection. Recent work has used the Random Neural Network [12] for attack detection [2] and we expect that further results will become available with similar machine learning techniques.…”
Section: Discussionmentioning
confidence: 99%
“…Network security is ranked as one of the top priorities for future self-aware networks [17], which is why there is well established research in the field. Furthermore, while work in [20,31] focuses on a general defensive approach against DoS attacks in future networks, signalling storm specific research can roughly be categorised in the following groups: problem definition and attacks classification [4,28,29,39]; measurements in real operating networks [38], [10]; modelling and simulation [1,24]; impact of attacks on energy consumption [9,11]; attacks detection and mitigation, using counters [18,19,36], change-point detection techniques [30,40], IP packet analysis [26], randomisation in RRC's functions [41], software changes in the mobile terminal [7,32], monitoring terminal's bandwidth usage [37], and detection using techniques from Artificial Intelligence [2]. As we look to the future, such as the Internet of Things (IoT), various forms of attacks will also have to be considered [5,8].…”
Section: Introductionmentioning
confidence: 99%
“…Previous studies [10,11] examine the RRC state machine and its interaction with cellular traffic for cellular networks. Also, some efforts [4,[12][13][14][15][16][17] measured various network performance metrics. Those studies investigated various aspects, such as crowed events, queue delay, and so on.…”
Section: Related Workmentioning
confidence: 99%