Performance and Security Enhancements in Practical Millimeter-Wave Communication Systems

Millimeter-wave (mm-wave) communication systems achieve extremely high data rates and provide interference-free transmissions. to overcome high attenuations, they employ directional antennas that focus their energy in the intended direction. Transmissions can be steered such that signals only propagate within a specific area-of-interest. Although these advantages are well-known, they are not yet available in practical networks. IEEE 802.11ad, the recent standard for communications in the unlicensed 60 GHz band, exploits a subset of the directional propagation effects only. Despite the large available spectrum, it does not outperform other developments in the prevalent sub-6 GHz bands. This underutilization of directional communications causes unnecessary performance limitations and leaves a false sense of security. For example, standard compliant beam training is very time consuming. It uses suboptimal beam patterns, and is unprotected against malicious behaviors. Furthermore, no suitable research platform exists to validate protocols in realistic environments. To address these challenges, we develop a holistic evaluation framework and enhance the performance and security in practical mm-wave communication systems. Besides signal propagation analyses and environment simulations, our framework enables practical testbed experiments with off-the-shelf devices. We provide full access to a tri-band router’s operating system, modify the beam training operation in the Wi-Fi firmware, and create arbitrary beam patterns with the integrated antenna array. This novel approach allows us to implement custom algorithms such as a compressive sector selection that reduces the beam training overhead by a factor of 2.3. By aligning the receive beam, our adaptive beam switching algorithm mitigates interference from lateral directions and achieves throughput gains of up to 60%. With adaptive beam optimization, we estimate the current channel conditions and generate directional beams that implicitly exploit potential reflections in the environment. These beams increase the received signal strength by about 4.4 dB. While intercepting a directional link is assumed to be challenging, our experimental studies show that reflections on small-scale objects are sufficient to enable eavesdropping from afar. Additionally, we practically demonstrate that injecting forged feedback in the beam training enables Man-in-the Middle attacks. With only 7.3% overhead, our authentication scheme protects against this beam stealing and enforces responses to be only accepted from legitimate devices. By making beam training more efficient, effective, and reliable, our contributions finally enable practical applications of highly directional transmissions

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

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks