Attacking Massive MIMO Cognitive Radio Networks by Optimized Jamming

Massive multiple-input multiple-output (MaMIMO) and cognitive radio networks (CRNs) are two promising technologies for improving spectral efficiency of next-generation wireless communication networks. In this paper, we investigate the problem of physical layer security in the networks that jointly use both technologies, named MaMIMO-CRN. Specifically, to investigate the vulnerability of this network, we design an optimized attacking scenario to MaMIMO-CRNs by a jammer. For having the most adversary effect on the uplink transmission of the legitimate MaMIMO-CRN, we propose an efficient method for power allocation of the jammer. The legitimate network consists of a training and a data transmission phase, and both of these phases are attacked by the jammer using an optimized power split between them. The resulting power allocation problem is non-convex. We thus propose three different efficient methods for solving this problem, and we show that under some assumptions, a closed-form solution can also be obtained. Our results show the vulnerability of the MaMIMO-CRN to an optimized jammer. It is also shown that increasing the number of antennas at the legitimate network does not improve the security of the network

Secure Simultaneous Information and Power Transfer for Downlink Multi-user Massive MIMO

In this paper, downlink secure transmission in simultaneous information and power transfer (SWIPT) system enabled with massive multiple-input multiple-output (MIMO) is studied. A base station (BS) with a large number of antennas transmits energy and information signals to its intended users, but these signals are also received by an active eavesdropper. The users and eavesdropper employ a power splitting technique to simultaneously decode information and harvest energy. Massive MIMO helps the BS to focus energy to the users and prevent information leakage to the eavesdropper. The harvested energy by each user is employed for decoding information and transmitting uplink pilot signals for channel estimation. It is assumed that the active eavesdropper also harvests energy in the downlink and then contributes during the uplink training phase. Achievable secrecy rate is considered as the performance criterion and a closed-form lower bound for it is derived. To provide secure transmission, the achievable secrecy rate is then maximized through an optimization problem with constraints on the minimum harvested energy by the user and the maximum harvested energy by the eavesdropper. Numerical results show the effectiveness of using massive MIMO in providing physical layer security in SWIPT systems and also show that our closed-form expressions for the secrecy rate are accurate

Jamming Detection in Low-BER Mobile Indoor Scenarios via Deep Learning

The current state of the art on jamming detection relies on link-layer metrics. A few examples are the bit-error-rate (BER), the packet delivery ratio, the throughput, and the increase in the signal-to-noise ratio (SNR). As a result, these techniques can only detect jamming \emph{ex-post}, i.e., once the attack has already taken down the communication link. These solutions are unfit for mobile devices, e.g., drones, which might lose the connection to the remote controller, being unable to predict the attack. Our solution is rooted in the idea that a drone unknowingly flying toward a jammed area is experiencing an increasing effect of the jamming, e.g., in terms of BER and SNR. Therefore, drones might use the above-mentioned phenomenon to detect jamming before the decrease of the BER and the increase of the SNR completely disrupt the communication link. Such an approach would allow drones and their pilots to make informed decisions and maintain complete control of navigation, enhancing security and safety. This paper proposes Bloodhound+, a solution for jamming detection on mobile devices in low-BER regimes. Our approach analyzes raw physical-layer information (I-Q samples) acquired from the wireless channel. We assemble this information into grayscale images and use sparse autoencoders to detect image anomalies caused by jamming attacks. To test our solution against a wide set of configurations, we acquired a large dataset of indoor measurements using multiple hardware, jamming strategies, and communication parameters. Our results indicate that Bloodhound+ can detect indoor jamming up to 20 meters from the jamming source at the minimum available relative jamming power, with a minimum accuracy of 99.7\%. Our solution is also robust to various sampling rates adopted by the jammer and to the type of signal used for jamming.Comment: 16 pages, 16 figures, 3 tables; Submitted and under revie