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1. Sensitivity of Dynamic Network Slicing to Deep Reinforcement Learning Based Jamming Attacks

   Wang, Feng; Gursoy, M. Cenk; Velipasalar, Senem (September 2023, 2023 IEEE 34th Annual Inter- national Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC))

   In this paper, we consider multi-agent deep reinforcement learning (deep RL) based network slicing agents in a dynamic environment with multiple base stations and multiple users. We develop a deep RL based jammer with limited prior information and limited power budget. The goal of the jammer is to minimize the transmission rates achieved with network slicing and thus degrade the network slicing agents' performance. We design a jammer with both listening and jamming phases and address jamming location optimization as well as jamming channel optimization via deep RL. We evaluate the jammer at the optimized location, generating interference attacks in the optimized set of channels by switching between the jamming phase and listening phase. We show that the proposed jammer can significantly reduce the victims' performance without direct feedback or prior knowledge on the network slicing policies. 

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2. Fortifying 5G Networks: Defending Against Jamming Attacks with Multipath Communications

   Mohammadi, Hossein; Zhang, Minglong; Jha, Abhisek; Marojevic, Vuk; Chou, Rémi; Kim, Taejoon (October 2024, IEEE)

   The advent of 5G technology introduces significant advancements in speed, latency, and device connectivity, but also poses complex security challenges. Among typical denial-of-service (DoS) attacks, jamming attack can severely degrade network performance by interfering critical communication channels. To address this issue, we propose a novel security solution utilizing multipath communication, which distributes message segments across multiple paths to ensure message recovery even when some paths are compromised. This strategy enhances network resilience and aligns with zero-trust architecture principles. Moreover, the proposed scheme has been implemented in our testbed to validate the concept and assess the network performance under jamming attacks. Our findings demonstrate that this method eliminates the negative impacts caused by DoS attacks, maintaining the integrity and availability of critical network services. The results highlight the robustness of multipath communication in securing 5G networks against sophisticated attacks, thereby safeguarding essential communications in dynamic and potentially hostile environments. 

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3. A Cooperative Jamming Game in Wireless Networks under Uncertainty

   https://doi.org/10.1007/978-3-030-63086-7_14  

   Xu, Z.; Baykal-Gürsoy, M. (December 2020, Security and Privacy in Communication Networks. SecureComm 2020. Lecture Notes)

   null (Ed.)

   Considered is a multi-channel wireless network for secret communication that uses the signal-to-interference-plus-noise ratio (SINR) as the performance measure. An eavesdropper can intercept encoded messages through a degraded channel of each legitimate transmitter-receiver communication pair. A friendly interferer, on the other hand, may send cooperative jamming signals to enhance the secrecy performance of the whole network. Besides, the state information of the eavesdropping channel may not be known completely. The transmitters and the friendly interferer have to cooperatively decide on the optimal jamming power allocation strategy that balances the secrecy performance with the cost of employing intentional interference, while the eavesdropper tries to maximize her eavesdropping capacity. To solve this problem, we propose and analyze a non-zero-sum game between the network defender and the eavesdropper who can only attack a limited number of channels. We show that the Nash equilibrium strategies for the players are of threshold type. We present an algorithm to find the equilibrium strategy pair. Numerical examples demonstrate the equilibrium and contrast it to baseline strategies. 

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4. Manipulating Drone Position Control

   https://doi.org/10.1109/CNS.2019.8802817  

   Chen, Wenxin; Dong, Yingfei; Duan, Zhenhai (June 2019, 2019 IEEE Conference on Communications and Network Security (CNS))

   Although consumer drones have been used in many attacks, besides specific methods such as jamming, very little research has been conducted on systematical methods to counter these drones. In this paper, we develop generic methods to compromise drone position control algorithms in order to make malicious drones deviate from their targets. Taking advantage of existing methods to remotely manipulate drone sensors through cyber or physical attacks (e.g., [1], [2]), we exploited the weaknesses of position estimation and autopilot controller algorithms on consumer drones in the proposed attacks. For compromising drone position control, we first designed two state estimation attacks: a maximum False Data Injection (FDI) attack and a generic FDI attack that compromised the Kalman-Filter-based position estimation (arguably the most popular method). Furthermore, based on the above attacks, we proposed two attacks on autopilot-based navigation, to compromise the actual position of a malicious drone. To the best of our knowledge, this is the first piece of work in this area. Our analysis and simulation results show that the proposed attacks can significantly affect the position estimation and the actual positions of drones. We also proposed potential countermeasures to address these attacks. 

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5. Hardware Security in Sensor and its Networks

   https://doi.org/10.3389/fsens.2022.850056  

   Monjur, Mohammad Mezanur; Heacock, Joseph; Calzadillas, Joshua; Mahmud, MD Shaad; Roth, John; Mankodiya, Kunal; Sazonov, Edward; Yu, Qiaoyan (May 2022, Frontiers in Sensors)

   Sensor networks and IoT systems have been widely deployed in monitoring and controlling system. With its increasing utilization, the functionality and performance of sensor networks and their applications are not the only design aims; security issues in sensor networks attract more and more attentions. Security threats in sensor and its networks could be originated from various sectors: users in cyber space, security-weak protocols, obsolete network infrastructure, low-end physical devices, and global supply chain. In this work, we take one of the emerging applications, advanced manufacturing, as an example to analyze the security challenges in the sensor network. Presentable attacks—hardware Trojan attack, man-in-the-middle attack, jamming attack and replay attack—are examined in the context of sensing nodes deployed in a long-range wide-area network (LoRaWAN) for advanced manufacturing. Moreover, we analyze the challenges of detecting those attacks. 

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