Jesus David Terrazas Gonzalez

A Modular Dynamical Cryptosystem Based on Continuous-Interval Cellular Automata

This paper presents a new cryptosystem based on chaotic continuous-interval cellular automata CCA to increase data protection as demonstrated by their flexibility to encrypt and decrypt information from distinct sources. Enhancements to cryptosystems are also presented including i a model based on a new chaotic CCA attractor, ii the dynamical integration of modules containing dynamical systems to generate complex sequences, and iii an enhancement for symmetric cryptosystems by allowing them to generate an unlimited number of keys. This paper also presents a process of mixing chaotic sequences obtained from cellular automata, instead of using differential equations, as a basis to achieve higher security and higher speed for the encryption and decryption processes, as compared to other recent approaches. The complexity of the mixed sequences is measured using the variance fractal dimension trajectory to compare them to the unmixed chaotic sequences to verify that the former are more complex. This type of polyscale measure and evaluation has never been done in the past outside this research group.

Comparison of cryptosystems using a single-scale statistical measure

A useful measure is presented for comparison of distinct cryptosystems, including (i) the RSA algorithm, (ii) ElGamal algorithm, (iii) a cryptosystem based on radio background noise (RBN), and (iv) a new cryptosystem based on chaos phenomena. The four cryptosystems are implemented to have the same computational power, and are compared through the marginal probability mass functions (mpmf). This paper shows experimentally that the chaos-based modular dynamical cryptosystem is (i) strong to statistical cryptanalysis by leaving no patterns or hooks in the ciphertexts and (ii) faster than the selected algorithms from public-key cryptography (RSA) and elliptic curve cryptography (ElGamal).

Comparison of selected cryptosystems using single-scale and poly-scale measures

This paper presents useful measures for comparison of distinct cryptosystems, including (i) the public-key cryptography RSA algorithm, (ii) the elliptic-curve cryptography ElGamal algorithm, (iii) a cryptosystem based on radio background noise (RBN), and (iv) a new cryptosystem based on chaos phenomena in cellular automata. The comparison is based on (i) a single-scale measure (i.e., the marginal probability mass functions (mpmf), and (ii) a poly-scale measure (i.e., the finite-sense stationarity, FSS10). Both comparison approaches use the same plaintext and computational power when testing the four cryptosystems. This paper shows experimentally that the chaos based modular dynamical cryptosystem is (i) strong to single-scale statistical cryptanalysis by leaving no patterns in the ciphertexts, (ii) strong to poly-scale cryptanalysis by having a smaller stationarity window than the alternative cryptosystems, and (iii) faster than the selected algorithms from RSA, ElGamal, and natural sources of randomness (RBN).

Security testing of a modular cryptosystem based on continuous cellular automata

This paper presents specially designed tests performed in a cryptosystem based on chaotic continuous cellular automata (CCA). The degree of the cryptosystem security is evaluated by taking two approaches: (i) stationarity and (ii) spectral fractal dimension. These tools are verified with known signals before applying them to test the cryptosystem. This paper provides groundbreaking research about a robust method to determine the minimum stationary window in a given time series. This new ideas is relevant because the stationarity of a signal can be rapidly determined. This idea has never been done in the past, at least in the reported literature, outside this research group.

ON internet based supermedia enhanced telepresence via cellular data network

This paper presents a proof of concept experiment designed to quantitatively and qualitatively test the supermedia streaming capabilities for telepresence via cellular data networks using a remote controlled (RC) helicopter. In the experiment, the RC helicopter flies with two smartphones attached. The first smartphone streams Skype video, 6-axis telemetry data (accelerometer), and round trip time (RTT) ping to a ground station via cellular data network. The second smartphone on the helicopter receives commands send from a remote location to take still pictures of the target. The system presented is capable of downloading still photos from the flying helicopter to the ground station. The experiment also characterizes packet loss, the positional error in the global positioning system (GPS) and radio reception strength (RSSI in dBm) of the smartphones onboard the RC helicopter. The mean delay exhibited in our experiment shows that supermedia telepresence is possible by cellular data networks.

Evaluating the Security Level of a Cryptosystem based on Chaos

This paper presents tests specially-designed for a cryptosystem based on chaotic continuous cellular automata CCA. The degree of the cryptosystem security is assessed by evaluating its i stationarity, ii spectral fractal dimension, and iii surrogate data. These tools are verified with known signals before applying them to test the cryptosystem. This paper introduces i a robust method to determine the minimum stationary window in a given time series, and ii a technique to conceal a chaotic attractor based on surrogate data. These new ideas are relevant because the stationarity of a signal can be determined rapidly, and the chaotic attractor concealment enhances the cryptosystem to increase its security degree.

Zero-crossing analysis of Lévy walks for real-time feature extraction

This paper presents a method, based on the Smirnov transform, for generating synthetic data with the statistical properties of Lévy-walks. This method for synthetic data generation can be utilized for generating arbitrary prescribed probability density functions (pdf). The Smirnov transform is used to solve a cybersecurity engineering problem associated with Internet traffic. The synthetic Lévy-walk process is intertwined with sections of other distinct characteristics (uniform noise, Gaussian noise, and an ordinary sinusoid) to create a composite signal, which is then analyzed with zero-crossing rate (ZCR) within a varying-size window. This paper shows that it is possible to identify the distinct sections present in the composite signal through ZCR. The differentiation of these sections shows an increasing ZCR value as the section under analysis exhibits a higher activity or complexity (from the sinusoid, to a synthetic Lévy-walk process, and uniform and Gaussian noise, respectively). The advantages of the ZCR computation directly in the time-domain are appealing for real-time implementations. The varying window in the ZCR produces more defined values as the window size increases.

Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction: Composite and Applied Signal Analysis for Strengthening the Internet-of-Things Against DDoS Attacks

A comparison between the probability similarities of a Distributed Denial-of-Service DDoS dataset and Levy walks is presented. This effort validates Levy walks as a model resembling DDoS probability features. In addition, a method, based on the Smirnov transform, for generating synthetic data with the statistical properties of Levy-walks is demonstrated. The Smirnov transform is used to address a cybersecurity problem associated with the Internet-of-things IoT. The synthetic Levy-walk is merged with sections of distinct signals uniform noise, Gaussian noise, and an ordinary sinusoid. Zero-crossing rate ZCR within a varying-size window is utilized to analyze both the composite signal and the DDoS dataset. ZCR identifies all the distinct sections in the composite signal and successfully detects the occurrence of the cyberattack. The ZCR value increases as the signal under analysis becomes more complex and produces steadier values as the varying window size increases. The ZCR computation directly in the time-domain is its most notorious advantage for real-time implementations.

Zero-Crossing Analysis and Information Divergence of Lévy Walks for Real-Time Feature Extraction

A method, based on the Smirnov transform, for generating synthetic data with the statistical properties of Levy-walks is presented. This method can be utilized for generating arbitrary prescribed probability density functions pdf. A cybersecurity engineering problem associated with Internet traffic is addressed. The synthetic Levy-walks process is intertwined with sections of distinct characteristics creating a composite signal that is analyzed through zero-crossing rate ZCR within a varying-size window to identify sections. The advantages of the ZCR computation directly in the time-domain are appealing for real-time implementations. Moreover, the characterization of the degree of closeness, via the Kullback-Leibler divergence KLD, among the pdfs of arbitrary processes focusing on Levy walks and model pdfs is presented. The results obtained from the KLD experiments provide a categorical determination of the closeness degree. These results, a remarkable achievement in this research, are also promising to be used as features for classification of complex signals in real-time.