Signalling storms in 3G mobile networks

Abdelrahman, Omer H.; Gelenbe, Erol

doi:10.1109/icc.2014.6883453

Cited by 34 publications

(23 citation statements)

References 18 publications

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“…Then we discuss the results of applying the algorithm on the dataset produced by the simulator. Since the impact of signaling storms on mobile networks has been analyzed extensively in [4,21,40], the objective of the present simulation setup is to evaluate the performance of our detection algorithm, and therefore only a small scenario has been considered. In particular, we simulated 200 3G/UMTS UEs in an area of 2 × 2 km 2 which is covered by 7 base stations connected to a single radio network controller (RNC).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…It also interacts with existing network management systems to reduce computational overhead, storage requirements and false alarm rate. The use of a supervised learning RNN is motivated by its capability of classifying known patterns such as signaling storms whose characteristics and root causes are well understood [4,5,21,40], and also its previous success in detecting traditional DoS attacks in the Internet [35,52].…”

Section: Contributions Of the Papermentioning

confidence: 99%

“…While operators benefit from such growth in data and billing-related signaling, since it directly correlates to their increased revenues and can be handled effectively through capacity engineering, they are struggling with RRC-based signaling storms caused by malfunctioning applications, malware and malicious behavior. Such storms can cause disruption in the access to the infrastructure, through sudden overload in the signaling backbone of mobile networks [4,40] and potentially the wireless bandwidth of users, and may…”

Section: Introductionmentioning

confidence: 99%

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Detecting Network-Unfriendly Mobiles With the Random Neural Network

Abdelrahman

2016

Prob. Eng. Inf. Sci.

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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 than the data plane, rendering traditional detection techniques ineffective. Thus, in this paper we describe the manner in which storms happen and their causes, and propose a detection framework that utilizes traffic measurements and key performance indicators to identify in real-time misbehaving mobile devices. The detection algorithm is based on the random neural network which is a probabilistic computational model with efficient learning algorithms. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Contributions Of the Papermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting Network-Unfriendly Mobiles With the Random Neural Network

Abdelrahman

2016

Prob. Eng. Inf. Sci.

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“…This approach was also evaluated on a large network test-bed as a means to detect worm attacks and react to them by sending the users' traffic on routes that avoid the infected nodes [197,200]. His work on security continued with more algorithmic issues [204], but recently moved to the analysis of signalling storms in mobile networks [7,169,170] and is currently one of the main centers of his attention.…”

Section: Network Securitymentioning

confidence: 99%

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

Caglayan¹

2016

Prob. Eng. Inf. Sci.

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We focus on Erol Gelenbe's scientific and technical contributions to probability models in the computer and information sciences, but limit our survey to the last fifteen years. We start with a brief overview of his work as a single author, as well as his work in collaboration with over 200 co-authors. We discuss some of his recent and innovative work regarding a new probability model that represents Intermittent Energy Sources for Computing and Communications, introducing Energy Packet Networks which are a probabilistic representation of the flow, storage and consumption of electrical energy at the microscopic level (in electronic chips), and at the macroscopic level (e.g. in buildings or data centers) and for its routing and dynamic usage by consuming units (such as computer elements, chips or machines). We next discuss his work on designing computer and communication systems that parsimoniously use energy in order to achieve a satisfactory level of quality of service (QoS). Trade-offs between system QoS and energy consumption are also considered. Then we turn to Prof. Gelenbe's pioneering work on Autonomic Communications and the design and implementation of CPN, the Cognitive Packet Network, and we also briefly review his spiking random neural network that was used in CPN. This is followed by a brief review of work that he conducted since 1999 on human evacuation from dangerous or catastrophic environments, and the design of technology driven Emergency Management Systems. His research since the late 2000s on Gene Regulatory Networks is then covered together with its application to the detecting possible disease from microarray data. Finally, we briefly discuss some novel analytical models that he developed in this period with publications appearing in journals of physics and applied mathematics.

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“…This type of behavior on wireless networks can result in abusive bandwidth occupancy, excessive signaling at the mobile operator [2,22], battery dissipation at mobile devices [14], and extra energy consumption in base stations and backbone networks [18,17,34]. If mobile technology is exploited in cyber-physical infrastructures such as the smart grid, or for the Internet of Things (IoT) including vehicular technologies, smart homes, and emergency management systems [20], such signaling storm effects can delay or impair communications which are of vital importance.…”

Section: Introductionmentioning

confidence: 99%

Countering Mobile Signaling Storms with Counters

Gelenbe

Abdelrahman

2016

Internet of Things. IoT Infrastructures

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Abstract. Mobile Networks are subject to signaling storms launched by misbehaving applications or malware, which result in bandwidth overload at the cell level and excessive signaling within the mobile operator, and may also deplete the battery power of mobile devices. This paper reviews the causes of signaling storms and proposes a novel technique for storm detection and mitigation. The approach is based on counting the number of successive signaling transitions that do not utilize allocated bandwidth, and temporarily blocking mobile devices that exceed a certain threshold to avoid overloading the network. Through a mathematical analysis, we derive the optimum value of the counter's threshold, which minimizes both the number of misbehaving mobiles and the signaling overload in the network. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

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Signalling storms in 3G mobile networks

Cited by 34 publications

References 18 publications

Detecting Network-Unfriendly Mobiles With the Random Neural Network

Detecting Network-Unfriendly Mobiles With the Random Neural Network

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

Countering Mobile Signaling Storms with Counters

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