Botnets in 4G cellular networks: Platforms to launch DDoS attacks against the air interface

Khosroshahy, Masood; Qiu, Dongyu; Ali, M. K.

doi:10.1109/mownet.2013.6613793

Cited by 15 publications

(3 citation statements)

References 10 publications

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“…Another major category of DoS a acks arise from SMS-capable interfaces from IMS domain and from public Internet where operators have least control [48,54,173,175]. Similarly, cellular botnets [97,174] (including SMS-botnets [67,185]) can launch DoS a acks from a large army of compromised UEs.…”

Section: Impactsmentioning

confidence: 99%

Threat modeling framework for mobile communication systems

Rao¹,

Holtmanns²,

Aura³

2020

Preprint

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Due to the complex nature of mobile communication systems, most of the security e orts in its domain are isolated and sca ered across underlying technologies. is has resulted in an obscure view of the overall security. In this work, we a empt to x this problem by proposing a domain-speci c threat modeling framework. By gleaning from a diverse and large body of security literature, we systematically organize the a acks on mobile communications into various tactics and techniques. Our framework is designed to model adversarial behavior in terms of its a ack phases and to be used as a common taxonomy matrix. We also provide concrete examples of using the framework for modeling the a acks individually and comparing them with similar ones. CCS CONCEPTS•Security and privacy → Security requirements; Mobile and wireless security; •Networks → Mobile networks;

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Section: Impactsmentioning

confidence: 99%

Threat modeling framework for mobile communication systems

Rao¹,

Holtmanns²,

Aura³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…There has been some recent research on DoS attacks against the LTE air interface. For example, the authors of [19] use simulations to determine the number of attackers needed to degrade service of legitimate VoIP users. Although peripherally similar to our work in scope, this research does not examine the impact of a botnet due to various QoS requirements nor does it provide results from a real-world environment.…”

Section: Botnet Threats In Cellular Networkmentioning

confidence: 99%

An Analysis of DoS Attack Strategies Against the LTE RAN

Jermyn

Salles-Loustau

Zonouz

2014

JCSM

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Long Term Evolution (LTE) is the latest 3GPP mobile network standard, offering an all-IP network with higher efficiency and up to ten times the data rates of its predecessors. Due to an increase in cyber crime and the proliferation of mobile computing, attacks stemming from mobile devices are becoming more frequent and complex. Mobile malware can create smart-phone botnets in which a large number of mobile devices conspire to perform malicious activities on the cellular network. It has been shown that such botnets can cause a denial of service (DoS) by exhausting user traffic capacity over the air interface. Through simulation and with studies in a real-world deployment, this paper examines the impact of a botnet of devices seeking to attack the LTE network using different types of strategies. We quantify the adverse effects on legitimate users as the size of the botnet scales up in both sparsely and denselypopulated cells for varying traffic Quality of Service (QoS) requirements. Our results show that a single attacker can drastically reduce the QoS of legitimate devices in the same cell. Furthermore, we prove that the impact of the attack can be optimized by tuning the attack strategy, leveraging the LTE uplink MAC scheduler.

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“…Such unauthorized usage can be as simple as utilizing mobile phone networks without paying the service provider or as serious as using an SDR to distribute malware to legitimate users without their knowledge. The consequences of malware spread in this manner can be damaging to the point of causing complete network outages [20]. Violators can even cause network disruptions by using SDRs to simply send out a jamming signal.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Sensor Network Coverage with Location Uncertainty

Sodergren

Hair

Phillips

et al. 2017

2017 IEEE Visualization in Data Science (VDS)

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Figure 1: The visual interface in exploring sensor network coverage with location uncertainty: (a) control panel, (b) viewing panel, (c) interaction panel and (d) color panel. In the interaction panel, the coverage of a randomly generated sensor network is visualized by the union of disk-like sensor regions overlaid with a Rips complex representation. ABSTRACTWe present an interactive visualization system for exploring the coverage in sensor networks with uncertain sensor locations. We consider a simple case of uncertainty where the location of each sensor is confined to a discrete number of points sampled uniformly at random from a region with a fixed radius. Employing techniques from topological data analysis, we model and visualize network coverage by quantifying the uncertainty defined on its simplicial complex representations. We demonstrate the capabilities and effectiveness of our tool via the exploration of randomly distributed sensor networks. *

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Botnets in 4G cellular networks: Platforms to launch DDoS attacks against the air interface

Cited by 15 publications

References 10 publications

Threat modeling framework for mobile communication systems

Threat modeling framework for mobile communication systems

An Analysis of DoS Attack Strategies Against the LTE RAN

Visualizing Sensor Network Coverage with Location Uncertainty

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