A Colonel Blotto Game for Anti-Jamming in the Internet of Things

Labib, Mina; Ha, Sean; Saad, Walid; Reed, Jeffrey H.

doi:10.1109/glocom.2015.7417437

Cited by 37 publications

(14 citation statements)

References 13 publications

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“…Thus, anti-jamming countermeasures for IoT applications have been widely investigated in literature [84]. Given the distributed nature of the network, game theory has been identified as a well-suited mathematical framework to provide effective anti-jamming mechanisms [85],where IoT devices act as the players of a game aiming at either avoiding or mitigating the disruptive attacks of the jammer. Other approaches rely on traditional jamming-proof mechanisms such as relaying [86,87], spread-spectrum [88]and frequency hopping technologies [89].…”

Section: B Iot Wireless Networkingmentioning

confidence: 99%

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

Restuccia

D’Oro

Melodia

2018

IEEE Internet Things J.

206

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The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. Indeed, nextgeneration IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning and software-defined networking will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of machine learning and software-defined networking to address existing and nextgeneration IoT security threats.

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Section: B Iot Wireless Networkingmentioning

confidence: 99%

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

Restuccia

D’Oro

Melodia

2018

IEEE Internet Things J.

206

View full text Add to dashboard Cite

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“…The anti-jamming communication game as developed in [28] optimizes the transmit power over multiple channels in cognitive radio networks based on the NE of the CBG. The CBG-based jamming game as formulated in [15] shows that neither the defender nor the attacker can dominate with limited computational resources. The CBG-based jamming game as formulated in [16] shows how the number of subcarriers impacts the anti-jamming performance of Internet of Things with continuous and asymmetric radio power resources.…”

Section: Related Workmentioning

confidence: 99%

“…The player who applies more resources on a battlefield in a Colonel Blotto game wins it, and the overall payoff of a player in the game is proportional to the number of the winning battlefields [13]. The Colonel Blotto game has been recently applied to design the spectrum allocation of network service providers [14], the jamming resistance methods for Internet of Things [15] and [16].…”

Section: Introductionmentioning

confidence: 99%

Defense Against Advanced Persistent Threats in Dynamic Cloud Storage: A Colonel Blotto Game Approach

Min

Xiao

Xie

et al. 2018

IEEE Internet Things J.

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Advanced Persistent Threat (APT) attackers apply multiple sophisticated methods to continuously and stealthily steal information from the targeted cloud storage systems and can even induce the storage system to apply a specific defense strategy and attack it accordingly. In this paper, the interactions between an APT attacker and a defender allocating their Central Processing Units (CPUs) over multiple storage devices in a cloud storage system are formulated as a Colonel Blotto game. The Nash equilibria (NEs) of the CPU allocation game are derived for both symmetric and asymmetric CPUs between the APT attacker and the defender to evaluate how the limited CPU resources, the date storage size and the number of storage devices impact the expected data protection level and the utility of the cloud storage system. A CPU allocation scheme based on "hotbooting" policy hill-climbing (PHC) that exploits the experiences in similar scenarios to initialize the quality values to accelerate the learning speed is proposed for the defender to achieve the optimal APT defense performance in the dynamic game without being aware of the APT attack model and the data storage model. A hotbooting deep Q-network (DQN)-based CPU allocation scheme further improves the APT detection performance for the case with a large number of CPUs and storage devices. Simulation results show that our proposed reinforcement learning based CPU allocation can improve both the data protection level and the utility of the cloud storage system compared with the Q-learning based CPU allocation against APTs.

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“…B Y injecting faked or replayed signals, a jammer aims to interrupt the ongoing communication of mobile devices such as smartphones, mobile sensing robots and mobile servers, and even launch denial of service (DoS) attacks in wireless networks [1]- [5]. With the pervasion of smart radio devices such as universal software radio peripherals (USRPs) [6], jammers can cooperatively and flexibly choose their jamming policies to block the mobile devices efficiently [7], [8]. Jammers can even induce the mobile device to enter a specific communication mode and then launch the jamming attacks accordingly.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional anti-jamming communication based on deep reinforcement learning

Han

Xiao

Poor

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

164

106

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By using smart radio devices, a jammer can dynamically change its jamming policy based on opposing security mechanisms; it can even induce the mobile device to enter a specific communication mode and then launch the jamming policy accordingly. On the other hand, mobile devices can exploit spread spectrum and user mobility to address both jamming and interference. In this paper, a two-dimensional anti-jamming mobile communication scheme is proposed in which a mobile device leaves a heavily jammed/interfered-with frequency or area. It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework. More specifically, a hotbooting deep Q-network based two-dimensional mobile communication scheme is proposed that exploits experiences in similar scenarios to reduce the exploration time at the beginning of the game, and applies deep convolutional neural network and macro-action techniques to accelerate the learning speed in dynamic situations. Several real-world scenarios are simulated to evaluate the proposed method. These simulation results show that our proposed scheme can improve both the signal-to-interference-plus-noise ratio of the signals and the utility of the mobile devices against cooperative jamming compared with benchmark schemes.

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A Colonel Blotto Game for Anti-Jamming in the Internet of Things

Cited by 37 publications

References 13 publications

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

Defense Against Advanced Persistent Threats in Dynamic Cloud Storage: A Colonel Blotto Game Approach

Two-dimensional anti-jamming communication based on deep reinforcement learning

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