Coding for Cryptographic Security Enhancement using Stopping Sets

In this paper we discuss the ability of channel codes to enhance cryptographic secrecy. Toward that end, we present the secrecy metric of degrees of freedom in an attacker's knowledge of the cryptogram, which is similar to equivocation. Using this notion of secrecy, we show how a specific practical channel coding system can be used to hide information about the ciphertext, thus increasing the difficulty of cryptographic attacks. The system setup is the wiretap channel model where transmitted data traverse through independent packet erasure channels with public feedback for authenticated ARQ (Automatic Repeat reQuest). The code design relies on puncturing nonsystematic low-density parity-check codes with the intent of inflicting an eavesdropper with stopping sets in the decoder. Furthermore, the design amplifies errors when stopping sets occur such that a receiver must guess all the channel-erased bits correctly to avoid an expected error rate of one half in the ciphertext. We extend previous results on the coding scheme by giving design criteria that reduces the effectiveness of a maximum-likelihood attack to that of a message-passing attack. We further extend security analysis to models with multiple receivers and collaborative attackers. Cryptographic security is enhanced in all these cases by exploiting properties of the physical-layer. The enhancement is accurately presented as a function of the degrees of freedom in the eavesdropper's knowledge of the ciphertext, and is even shown to be present when eavesdroppers have better channel quality than legitimate receivers.Comment: 13 pages, 8 figure

Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions

The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE Transactions on Information Forensics and Securit

Increasing Physical Layer Security through Scrambled Codes and ARQ

We develop the proposal of non-systematic channel codes on the AWGN wire-tap channel. Such coding technique, based on scrambling, achieves high transmission security with a small degradation of the eavesdropper's channel with respect to the legitimate receiver's channel. In this paper, we show that, by implementing scrambling and descrambling on blocks of concatenated frames, rather than on single frames, the channel degradation needed is further reduced. The usage of concatenated scrambling allows to achieve security also when both receivers experience the same channel quality. However, in this case, the introduction of an ARQ protocol with authentication is needed.Comment: 5 pages, 4 figures; Proc. IEEE ICC 2011, Kyoto, Japan, 5-9 June 201

Rumba : a Python framework for automating large-scale recursive internet experiments on GENI and FIRE+

It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inferenc