This paper presents a privacy-preserving event detection scheme based on measurements made by a network of sensors. A diameter-like decision statistic made up of the marginal types of the measurements observed by the sensors is employed. The proposed detection scheme can achieve the best type-I error exponent as the type-II error rate is required to be negligible. Detection performance with finite-length observations is also demonstrated through a simulation example of spectrum sensing. Privacy protection is achieved by obfuscating the sensors marginal types with random zero-modulo-sum numbers that are generated and distributed via the exchange of encrypted messages among the sensors. The privacy-preserving performance against “honest but curious” adversaries, including colluding sensors, the fusion center, and external eavesdroppers, is analyzed through a series of cryptographic games. It is shown that the probability that any probabilistic polynomial time adversary successfully estimates the sensors measured types cannot be much better than independent guessing, when there are at least two noncolluding sensors.

The security of Industrial Control Systems (ICS) is closely related to national security. With secret exploration and analysis of a target ICS, highly-skilled attackers can gain enough key knowledge about the system (e.g., the physical model of the system and the corresponding detection threshold), and then launch stealthy attacks by keeping the detection indicator under its threshold, thus bypasses existing intrusion detection mechanisms. However, we discover that all devices in industrial control systems consume energy at run time and the energy consumption varies according to different operation types and system states. Therefore, there exists relationships between control operation, system state and energy consumption of the device. Accordingly, we put forward a novel ICS intrusion detection approach based on multi-dimensional data fusion. This approach collects information about power consumption of physical devices, control operation and system state, and then identifies stealthy attacks by feeding the multi-dimensional information into a cascade detection algorithm. Experimental results verify that our approach has a better detection performance than other detection methods.

In this paper, the anti-jamming issue in unmanned aerial vehicle (UAV) networks is analyzed in a static game and a dynamic game. We investigate the effect of wireless channel fading characteristics from a UAV to a ground station and flying cost on the performance of a closed-form Nash equilibrium (NE) in the static game. Besides, in a Stackelberg dynamic game, wherein the system model is hard to determine, we propose a Q- learning based anti-jamming scheme and evaluate its performance via exhaustive simulations, which can achieve relatively higher average utility and Signal to Interference plus Noise Ratio (SINR) than a benchmark method.

The covert communication in the physical layer and WiFi network is an important tendency for the current research on covert channel. On the other hand, the MIMO beamforming technique used in the physical layer of WiFi networks provides great potential for developing covert transmission scheme. To fill this gap, this paper presents a novel covert channel based on the coordinated operations in the control channel and data channel of MIMO system, called CovertMIMO. Under this scheme, the covert transmitter can make some slight modification on the normal uplink process, such that the recovered physical layer signal at the receiver side deviates from the pre-agreed overt signal. Through the deviation, some covert information can be encoded and delivered. To implementing CovertMIMO, this paper considers two kinds of wardens that follow the minimum principle and distribution principle respectively. Against them, the parameter identification is transformed into solving an optimization problem or nonlinear equation set. The transmission capacity and undetectability of CovertMIMO are also analyzed in detail. At last, the effectiveness of CovertMIMO is validated through extensive experiments.

In this paper, we propose a new security design, called as Zero Reconciliation Secret Key Extraction, for backscatter wireless systems, in which a reader needs to establish secret keys for multiple tags. Our design is able to eliminate the reconciliation process in conventional physical layer based key establishment, therefore improving the efficiency while still maintaining security in such a process. The essence in our design is to use a channel state information (CSI) characteristic, named CSI Ratio, at the reader to differentiate multiple tags, then employ multiple-input multiple-output (MIMO) precoding for legitimate tags to effectively and securely establish secret keys, at the same time leveraging artificial jamming to forestall eavesdropping attacks in the network. We evaluate our design with real-world experimental data and show that the proposed approach can achieve relatively high secret key extraction rates and maintain low bit error rates.

CSI fingerprint localization is an advanced and promising technique for indoor localization, which identifies the user's location by mapping his measured CSI against the server's CSI fingerprint database. This approach is highlighted due to its high granularity for location distinction and strong robustness to noise disturbances, but it also causes potential privacy leakage for the three participants in localization process: the user, the server, and the AP. Currently, there has been little research done on this issue, and the existing work often ignores the privacy concern on the AP. To fill the gap, this paper develops a privacypreserving fuzzy localization scheme with CSI fingerprint. On one hand, it leverages the property of CSI training to guarantee the randomness and independence of the user's measurement in each time of localization, and uses homomorphic encryption to achieve the data transmission and measurement comparison in cipher. These operations enable our scheme to preserve the location privacy of the user and APs as well as the data privacy of the server. On the other hand, the adoption of CSI fingerprint and fuzzy logic enhances the localization accuracy greatly. Through simulation experiments performed on CRAWDAD database, the efficiency of our proposed scheme is validated.