Modeling and Analysis of RRC-Based Signalling Storms in 3G Networks

Abstract

Mobile networks are vulnerable to signaling attacks and storms that are caused by traffic patterns that overload the control plane, and differ from distributed denial of service attacks in the Internet since they directly affect the control plane, and also reserve wireless bandwidth and network resources without actually using them. Such storms can result from malware and mobile botnets, as well as from poorly designed applications, and can cause service outages in 3G and 4G networks, which have been experienced by mobile operators. Since the radio resource control (RRC) protocol in the 3G and 4G networks is particularly susceptible to such storms, we analyze their effect with a mathematical model that helps to predict the congestion that is caused by a storm. A detailed simulation model of a mobile network is used to better understand the temporal dynamics of user behavior and signaling in the network and to show how RRC-based signaling attacks and storms cause significant problems in both the control and user planes of the network. Our analysis also serves to identify how storms can be detected, and to propose how system parameters can be chosen to mitigate their effect.

Detecting Network-Unfriendly Mobiles With the Random Neural Network

“…In this section, we evaluate the performance of our detection technique using the mobile network simulator developed in [39,40]. We first present the traffic models that characterize the normal user behavior, and two attack models that represent both malicious and misbehaving UEs.…”

“…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).…”

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

“…Network worms were also considered, and CPN was used to reroute the users' traffic to avoid the infected nodes (Sakellari and Gelenbe [176], Sakellari, Hey, and Gelenbe [177]). Further research on network security can be found in (Gelenbe et al [87], Gorbil et al [151], Yu et al [185]). …”

Countering Mobile Signaling Storms with Counters

“…In this section we evaluate the performance of the joint detection and mitigation approach that we have proposed using the mobile network simulator described in [21,22]. We illustrate how the proposed scheme allows quick reaction to malicious signaling behaviors or to malfunctioning applications, by showing the temporal behavior of network signaling load and delay during normal operation and then during an attack which is being detected and mitigated with our approach.…”

“…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.…”