REBUF: Jam Resistant BBC based Uncoordinated Frequency Division

One of the central tenants of information security is availability. One common form of attack against the availability of information in wireless networks is jamming. Currently, the most common techniques to provide jam-resistant communication, such as frequency-hopping spread spectrum (FHSS), are based on the use of a symmetric shared secret. However, there are theoretical approaches to jam resistance without a pre-shared secret. One theoretical approach using concurrent codes, called the BBC algorithm, was developed at the United States Air Force Academy. We developed and tested the effectiveness of REBUF, a Jam Resistant BBC based Uncoordinated Frequency Division Multiplexing (FDM) system. REBUF is the first known implementation of the BBC algorithm in a simultaneous frequency division multiplexing system. The contributions of this paper include: demonstrating the practical use of a BBC based FDM system, the ability of such a system to jam traditional orthogonal frequency division multiplexing (OFDM) systems, and the resilience of such a system to some common forms of jamming

Optimal Strategies in Jamming Resistant Uncoordinated Frequency Hopping Systems

Uncoordinated frequency hopping (UFH) has recently emerged as an effective mechanism to defend against jamming attacks. Existing research focuses on the optimal design of the hopping pattern, which implicitly assumes that the strategy of the attacker is fixed. In practice, the attacker might adjust its strategy to maximize its damage on the communication system. In this thesis, we study the design of optimal hopping pattern (the defense strategy) as long as the optimal jamming pattern (the attack strategy). In particular, we model the dynamic between the legitimate users and the attacker as a zero sum game, and study the property of this game. We show that when the legitimate users and the jammer can access only one channel at any time, the game has a unique Nash equilibrium. In the Nash equilibrium, the legitimate users and Eve will access or jam only a subset of channels that have good channel quality. Furthermore, the better the channel, the larger the probability that Eve will jam the channel and the smaller the probability the legitimate users will access this channel. We further extend the study to multiple access multiple jamming case and characterize the Nash equilibrium. We also give numerical results to illustrate the analytical results derived in this thesis

Spectrum Sensing and Security Challenges and Solutions: Contemporary Affirmation of the Recent Literature

Cognitive radio (CR) has been recently proposed as a promising technology to improve spectrum utilization by enabling secondary access to unused licensed bands. A prerequisite to this secondary access is having no interference to the primary system. This requirement makes spectrum sensing a key function in cognitive radio systems. Among common spectrum sensing techniques, energy detection is an engaging method due to its simplicity and efficiency. However, the major disadvantage of energy detection is the hidden node problem, in which the sensing node cannot distinguish between an idle and a deeply faded or shadowed band. Cooperative spectrum sensing (CSS) which uses a distributed detection model has been considered to overcome that problem. On other dimension of this cooperative spectrum sensing, this is vulnerable to sensing data falsification attacks due to the distributed nature of cooperative spectrum sensing. As the goal of a sensing data falsification attack is to cause an incorrect decision on the presence/absence of a PU signal, malicious or compromised SUs may intentionally distort the measured RSSs and share them with other SUs. Then, the effect of erroneous sensing results propagates to the entire CRN. This type of attacks can be easily launched since the openness of programmable software defined radio (SDR) devices makes it easy for (malicious or compromised) SUs to access low layer protocol stacks, such as PHY and MAC. However, detecting such attacks is challenging due to the lack of coordination between PUs and SUs, and unpredictability in wireless channel signal propagation, thus calling for efficient mechanisms to protect CRNs. Here in this paper we attempt to perform contemporary affirmation of the recent literature of benchmarking strategies that enable the trusted and secure cooperative spectrum sensing among Cognitive Radios

Synoptic analysis techniques for intrusion detection in wireless networks

Current system administrators are missing intrusion alerts hidden by large numbers of false positives. Rather than accumulation more data to identify true alerts, we propose an intrusion detection tool that e?ectively uses select data to provide a picture of ?network health?. Our hypothesis is that by utilizing the data available at both the node and cooperative network levels we can create a synoptic picture of the network providing indications of many intrusions or other network issues. Our major contribution is to provide a revolutionary way to analyze node and network data for patterns, dependence, and e?ects that indicate network issues. We collect node and network data, combine and manipulate it, and tease out information about the state of the network. We present a method based on utilizing the number of packets sent, number of packets received, node reliability, route reliability, and entropy to develop a synoptic picture of the network health in the presence of a sinkhole and a HELLO Flood attacker. This method conserves network throughput and node energy by requiring no additional control messages to be sent between the nodes unless an attacker is suspected. We intend to show that, although the concept of an intrusion detection system is not revolutionary, the method in which we analyze the data for clues about network intrusion and performance is highly innovative

Defeating Super-Reactive Jammers WithDeception Strategy: Modeling, SignalDetection, and Performance Analysis

This paper aims to develop a novel framework to defeat a super-reactive jammer, one of the mostdifficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budgetand is equipped with the self-interference suppression capability to simultaneously attack and listen tothe transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus,we introduce a smart deception mechanism to attract the jammer to continuously attack the channel andthen leverage jamming signals to transmit data based on the ambient backscatter communication whichis resilient to radio interference/jamming. To decode the backscattered signals, the maximum likelihood(ML) detector can be adopted. However, the method is notorious for its high computational complexityand require a specific mathematical model for the communication system. Hence, we propose a deeplearning-based detector that can dynamically adapt to any channel and noise distributions. With the LongShort-Term Memory network, our detector can learn the received signals’ dependencies to achieve theperformance close to that of the optimal ML detector. Through simulation and theoretical results, wedemonstrate that with proposed approaches, the more power the jammer uses to attack the channel, thebetter bit error rate performance we can achiev

Defeating Super-Reactive Jammers WithDeception Strategy: Modeling, SignalDetection, and Performance Analysis

This paper aims to develop a novel framework to defeat a super-reactive jammer, one of the mostdifficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budgetand is equipped with the self-interference suppression capability to simultaneously attack and listen tothe transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus,we introduce a smart deception mechanism to attract the jammer to continuously attack the channel andthen leverage jamming signals to transmit data based on the ambient backscatter communication whichis resilient to radio interference/jamming. To decode the backscattered signals, the maximum likelihood(ML) detector can be adopted. However, the method is notorious for its high computational complexityand require a specific mathematical model for the communication system. Hence, we propose a deeplearning-based detector that can dynamically adapt to any channel and noise distributions. With the LongShort-Term Memory network, our detector can learn the received signals’ dependencies to achieve theperformance close to that of the optimal ML detector. Through simulation and theoretical results, wedemonstrate that with proposed approaches, the more power the jammer uses to attack the channel, thebetter bit error rate performance we can achiev

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

Adaptive Interference Removal for Un-coordinated Radar/Communication Co-existence

Most existing approaches to co-existing communication/radar systems assume that the radar and communication systems are coordinated, i.e., they share information, such as relative position, transmitted waveforms and channel state. In this paper, we consider an un-coordinated scenario where a communication receiver is to operate in the presence of a number of radars, of which only a sub-set may be active, which poses the problem of estimating the active waveforms and the relevant parameters thereof, so as to cancel them prior to demodulation. Two algorithms are proposed for such a joint waveform estimation/data demodulation problem, both exploiting sparsity of a proper representation of the interference and of the vector containing the errors of the data block, so as to implement an iterative joint interference removal/data demodulation process. The former algorithm is based on classical on-grid compressed sensing (CS), while the latter forces an atomic norm (AN) constraint: in both cases the radar parameters and the communication demodulation errors can be estimated by solving a convex problem. We also propose a way to improve the efficiency of the AN-based algorithm. The performance of these algorithms are demonstrated through extensive simulations, taking into account a variety of conditions concerning both the interferers and the respective channel states