1,496 research outputs found
Theory of Interacting Neural Networks
In this contribution we give an overview over recent work on the theory of
interacting neural networks. The model is defined in Section 2. The typical
teacher/student scenario is considered in Section 3. A static teacher network
is presenting training examples for an adaptive student network. In the case of
multilayer networks, the student shows a transition from a symmetric state to
specialisation. Neural networks can also generate a time series. Training on
time series and predicting it are studied in Section 4. When a network is
trained on its own output, it is interacting with itself. Such a scenario has
implications on the theory of prediction algorithms, as discussed in Section 5.
When a system of networks is trained on its minority decisions, it may be
considered as a model for competition in closed markets, see Section 6. In
Section 7 we consider two mutually interacting networks. A novel phenomenon is
observed: synchronisation by mutual learning. In Section 8 it is shown, how
this phenomenon can be applied to cryptography: Generation of a secret key over
a public channel.Comment: Contribution to Networks, ed. by H.G. Schuster and S. Bornholdt, to
be published by Wiley VC
Variations on chaos in physics: from unpredictability to universal laws
The tremendous popular success of Chaos Theory shares some common points with
the not less fortunate Relativity: they both rely on a misunderstanding.
Indeed, ironically , the scientific meaning of these terms for mathematicians
and physicists is quite opposite to the one most people have in mind and are
attracted by. One may suspect that part of the psychological roots of this
seductive appeal relies in the fact that with these ambiguous names, together
with some superficial clich{\'e}s or slogans immediately related to them ("the
butterfly effect" or "everything is relative"), some have the more or less
secret hope to find matter that would undermine two pillars of science, namely
its ability to predict and to bring out a universal objectivity. Here I propose
to focus on Chaos Theory and illustrate on several examples how, very much like
Relativity, it strengthens the position it seems to contend with at first
sight: the failure of predictability can be overcome and leads to precise,
stable and even more universal predictions.Comment: Convegno "Matematica e Cultura 2015", Mar 2015, Venezia, Ital
Deterministic Chaos in Digital Cryptography
This thesis studies the application of deterministic chaos to digital
cryptography. Cryptographic systems such as pseudo-random generators
(PRNG), block ciphers and hash functions are regarded as a dynamic
system (X, j), where X is a state space (Le. message space)
and f : X -+ X is an iterated function. In both chaos theory and
cryptography, the object of study is a dynamic system that performs
an iterative nonlinear transformation of information in an apparently
unpredictable but deterministic manner. In terms of chaos theory, the
sensitivity to the initial conditions together with the mixing property
ensures cryptographic confusion (statistical independence) and diffusion
(uniform propagation of plaintext and key randomness into cihertext).
This synergetic relationship between the properties of chaotic and
cryptographic systems is considered at both the theoretical and practical
levels: The theoretical background upon which this relationship is
based, includes discussions on chaos, ergodicity, complexity, randomness,
unpredictability and entropy.
Two approaches to the finite-state implementation of chaotic systems
(Le. pseudo-chaos) are considered: (i) floating-point approximation of
continuous-state chaos; (ii) binary pseudo-chaos. An overview is given
of chaotic systems underpinning cryptographic algorithms along with
their strengths and weaknesses. Though all conventional cryposystems
are considered binary pseudo-chaos, neither chaos, nor pseudo-chaos are
sufficient to guarantee cryptographic strength and security.
A dynamic system is said to have an analytical solution Xn = (xo)
if any trajectory point Xn can be computed directly from the initial
conditions Xo, without performing n iterations. A chaotic system with an
analytical solution may have a unpredictable multi-valued map Xn+l =
f(xn). Their floating-point approximation is studied in the context of
pseudo-random generators.
A cryptographic software system E-Larm ™ implementing a multistream
pseudo-chaotic generator is described. Several pseudo-chaotic
systems including the logistic map, sine map, tangent- and logarithm feedback
maps, sawteeth and tent maps are evaluated by means of floating point
computations. Two types of partitioning are used to extract
pseudo-random from the floating-point state variable: (i) combining the
last significant bits of the floating-point number (for nonlinear maps);
and (ii) threshold partitioning (for piecewise linear maps). Multi-round
iterations are produced to decrease the bit dependence and increase non-linearity.
Relationships between pseudo-chaotic systems are introduced
to avoid short cycles (each system influences periodically the states of
other systems used in the encryption session).
An evaluation of cryptographic properties of E-Larm is given using
graphical plots such as state distributions, phase-space portraits, spectral
density Fourier transform, approximated entropy (APEN), cycle length
histogram, as well as a variety of statistical tests from the National Institute
of Standards and Technology (NIST) suite. Though E-Larm passes
all tests recommended by NIST, an approach based on the floating-point
approximation of chaos is inefficient in terms of the quality/performance
ratio (compared with existing PRNG algorithms). Also no solution is
known to control short cycles.
In conclusion, the role of chaos theory in cryptography is identified;
disadvantages of floating-point pseudo-chaos are emphasized although
binary pseudo-chaos is considered useful for cryptographic applications.Durand Technology Limite
Secrecy and Randomness: Encoding Cloud data Locally using a One-Time Pad
There is no secrecy without randomness, and we address poor cloud security using an analogue chaotic onetime pad encryption system to achieve perfect secrecy. Local encoding returns control to the client and makes stored cloud data unreadable to an adversary. Most cloud service providers encode client data using public encryption algorithms, but ultimately businesses and organisations are responsible for encoding data locally before uploading to the Cloud. As recommended by the Cloud Security Alliance, companies employing authentication and local encryption will reduce or eliminate, EU fines for late data breach discoveries when the EU implements the new general data protection regulations in 2018. Companies failing to detect data breaches within a 72-hour limit will be fined up to four percent of their global annual turnover and estimates of several hundred billion euros could be levied in fines based on the present 146 days average EU breach discovery. The proposed localised encryption system is additional to public encryption, and obeying the rules of one-time pad encryption will mean intercepted encrypted data will be meaningless to an adversary. Furthermore, the encoder has no key distribution problem because applications for it are of “one-to-cloud” type
Crowdfunding Non-fungible Tokens on the Blockchain
Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artist’s work in the first place. Additionally, while blockchains like Ethereum are ideal for smaller works of art, additional support is needed when the artwork is larger than is feasible to store on the blockchain. In this paper, we propose a fundraising mechanism that will help artists to gain financial support for their initiatives, and where the backers can receive a share of the profits in exchange for their support. We discuss our prototype implementation using the SpartanGold framework. We then discuss how this system could be expanded to support large NFTs with the 0Chain blockchain, and describe how we could provide support for ongoing storage of these NFTs
Fake Malware Generation Using HMM and GAN
In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the goal to build fake malware data that can be used to effectively train the machine learning models. We use and compare different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate such fake samples obtaining promising results
IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing
The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks
Symmetry in Chaotic Systems and Circuits
Symmetry can play an important role in the field of nonlinear systems and especially in the design of nonlinear circuits that produce chaos. Therefore, this Special Issue, titled “Symmetry in Chaotic Systems and Circuits”, presents the latest scientific advances in nonlinear chaotic systems and circuits that introduce various kinds of symmetries. Applications of chaotic systems and circuits with symmetries, or with a deliberate lack of symmetry, are also presented in this Special Issue. The volume contains 14 published papers from authors around the world. This reflects the high impact of this Special Issue
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