A Mixed-Integer Programming Approach for Jammer Placement Problems for Flow-Jamming Attacks on Wireless Communication Networks

In this dissertation, we study an important problem of security in wireless networks. We study different attacks and defense strategies in general and more specifically jamming attacks. We begin the dissertation by providing a tutorial introducing the operations research community to the various types of attacks and defense strategies in wireless networks. In this tutorial, we give examples of mathematical programming models to model jamming attacks and defense against jamming attacks in wireless networks. Later we provide a comprehensive taxonomic classification of the various types of jamming attacks and defense against jamming attacks. The classification scheme will provide a one stop location for future researchers on various jamming attack and defense strategies studied in literature. This classification scheme also highlights the areas of research in jamming attack and defense against jamming attacks which have received less attention and could be a good area of focus for future research. In the next chapter, we provide a bi-level mathematical programming model to study jamming attack and defense strategy. We solve this using a game-theoretic approach and also study the impact of power level, location of jamming device, and the number of transmission channels available to transmit data on the attack and defense against jamming attacks. We show that by increasing the number of jamming devices the throughput of the network drops by at least 7%. Finally we study a special type of jamming attack, flow-jamming attack. We provide a mathematical programming model to solve the location of jamming devices to increase the impact of flow-jamming attacks on wireless networks. We provide a Benders decomposition algorithm along with some acceleration techniques to solve large problem instances in reasonable amount of time. We draw some insights about the impact of power, location and size of the network on the impact of flow-jamming attacks in wireless networks

Multi-channel Stochastic Resource Allocation and Dynamic Access Scheduling

Modern communication systems often have the ability to transmit signals on multiple communication mediums (e.g., RF, visible light) or interfaces (e.g., MAC layer protocols) at the same time. While each channel has different characteristics, a centralized controller with channel condition information will be able to schedule the resource allocated to each channel to achieve various optimization criteria. In this thesis, we focus on two usage scenarios: Indoor hybrid free space optical (FSO)-WiFi femtocells and multi-channel satellite communication (SATCOM). For the Indoor hybrid free space optical (FSO)-WiFi femtocells, a smart network controller is designed to determine which channel/interface to use for a specific user/time slot combination to maximize some pre-specified objectives such as load balance. In particular, this problem is modeled as a dynamic scheduling problem, which is a Markov decision process problem that is solved using a deep-Q reinforcement learning (RL) framework. For the SATCOM scenario, a smart network controller is proposed to transmit information securely on different channels to mitigate jamming and eavesdropping attacks. The proposed approaches combine elements from game theory and information theory to provide provably secure protocols from an information theoretic viewpoint

Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT.

PhD Theses.Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every eld of life and reshaped the technological world. Several tiny devices are seamlessly connected in a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT su ers from malicious attacks that pulverize communication and perturb network performance. Therefore, recently it is envisaged to introduce higher-level Arti cial Intelligence (AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate CR-IoT networks to establish secure transmission against vicious attacks autonomously. In this context, sub-band information from the Orthogonal Frequency Division Multiplexing (OFDM) modulated transmission in the spectrum has been extracted from the radio device receiver terminal, and a generalized state vector (GS) is formed containing low dimension in-phase and quadrature components. Accordingly, a probabilistic method based on learning a switching Dynamic Bayesian Network (DBN) from OFDM transmission with no abnormalities has been proposed to statistically model signal behaviors inside the CR-IoT spectrum. A Bayesian lter, Markov Jump Particle Filter (MJPF), is implemented to perform state estimation and capture malicious attacks. Subsequently, GS containing a higher number of subcarriers has been investigated. In this connection, Variational autoencoders (VAE) is used as a deep learning technique to extract features from high dimension radio signals into low dimension latent space z, and DBN is learned based on GS containing latent space data. Afterward, to perform state estimation and capture abnormalities in a spectrum, Adapted-Markov Jump Particle Filter (A-MJPF) is deployed. The proposed method can capture anomaly that appears due to either jammer attacks in transmission or cognitive devices in a network experiencing di erent transmission sources that have not been observed previously. The performance is assessed using the receiver

Secure protocols for wireless availability

Since wireless networks share a communication medium, multiple transmissions on the same channel cause interference to each other and degrade the channel quality, much as multiple people talking at the same time make for inefficient meetings. To avoid transmission collision, the network divides the medium into multiple orthogonal channels (by interleaving the channel access in frequency or time) and often uses medium access control (MAC) to coordinate channel use. Alternatively (e.g., when the wireless users use the same physical channel), the network users can emulate such orthogonal channel access in processing by spreading and coding the signal. Building on such orthogonal access technology, this dissertation studies protocols that support the coexistence of wireless users and ensure wireless availability. In contrast to other studies focusing on improving the overall e fficiency of the network, I aim to achieve reliability at all times. Thus, to study the worst-case misbehavior, I pose the problem within a security framework and introduce an adversary who compromised the network and has insider access. In this dissertation, I propose three schemes for wireless availability: SimpleMAC, Ignore-False-Reservation MAC (IFR-MAC), and Redundancy O ffset Narrow Spectrum (RONS). SimpleMAC and IFR-MAC build on MAC protocols that utilize explicit channel coordination in control communication. SimpleMAC counters MAC-aware adversary that uses the information being exchanged at the MAC layer to perform a more power e fficient jamming attack. IFR-MAC nulli ffies the proactive attack of denial-of-service injection of false reservation control messages. Both SimpleMAC and IFR-MAC quickly outperform the Nash equilibrium of disabling MAC and converge to the capacity-optimal performance in worst-case failures. When the MAC fails to coordinate channel use for orthogonal access or in a single-channel setting (both cases of which, the attacker knows the exact frequency and time location of the victim's channel access), RONS introduces a physical-layer, processing-based technique for interference mitigation. RONS is a narrow spectrum technology that bypasses the spreading cost and eff ectively counters the attacker's information-theoretically optimal strategy of correlated jamming