This paper makes an exhaustive survey of various applications of Quantum inspired computational intelligence (QCI) techniques proposed till date Definition, categorization and motivation for QCI techniques are stated clearly Major Drawbacks and challenges are discussed The significance of this work is that it presents an overview on applications of QCI in solving various problems in engineering, which will be very much useful for researchers on Quantum computing in exploring this upcoming and young discipline

Applications of quantum inspired computational intelligence: a survey

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed.

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A neural-visualization IDS for honeynet data.

The past decade has witnessed an increasing interest in the application of Computational Intelligence methods to problems derived from the field of cryptography and cryptanalysis. This phenomenon can be attributed both to the effectiveness of these methods to handle hard problems, and to the major importance of automated techniques in the design and cryptanalysis of cryptosystems. This chapter begins with a brief introduction to cryptography and Computational Intelligence methods. A short survey of the applications of Computational Intelligence to cryptographic problems follows, and our contribution in this field is presented. Specifically, some cryptographic problems are viewed as discrete optimization tasks and Evolutionary Computation methods are utilized to address them. Furthermore, the effectiveness of Artificial Neural Networks to approximate some cryptographic functions is studied. Finally, theoretical issues of Ridge Polynomial Networks and cryptography are presented. The experimental results reported suggest that problem formulation and representation are critical determinants of the performance of Computational Intelligence methods in cryptography. Moreover, since strong cryptosystems should not reveal any patterns of the encrypted messages or their inner structure, it appears that Computational Intelligence methods can constitute a first measure of the cryptosystems’ security.

Cryptography and Cryptanalysis Through Computational Intelligence

This paper presents a review of the literature on the use of artificial neutral networks in cryptography. Different neural network based approaches have been categorized based on their applications to different components of cryptosystems such as secret key protocols, visual cryptography, design of random generators, digital watermarking, and steganalysis.

http://ieeexplore.ieee.org/iel5/4681917/4698939/04699061.pdf

A review of applications of artificial neural networks in cryptosystems

Cascade Forward Back-propagation Neural Network Based Group Authentication Using (n, n) Secret Sharing Scheme

Visual cryptography finds many applications in the cryptographic field such as key management, message concealment, authorization, authentication, identification, and entertainment. The authors propose a novel approach for visual cryptography using neural networks (NNs). To perform encrypting, the input to the NN is a set of gray level images, and the output is a set of binary images (shares) that fulfils the desirable access scheme. This approach is considerably different from the traditional one, and can be applied to cope with very complex access schemes.

A neural network approach for visual cryptography

This paper proposes the general paradigm to build Q'tron neural networks (NNs) for visual cryptography. Given a visual encryption scheme, usually described using an access structure, it was formulated as a optimization problem of integer programming by which the a Q'tron NN with the so-called integer-programming-type energy function is, then, built to fulfill that scheme. Remarkably, this type of energy function has the so-called known-energy property, which allows us to inject bounded noises persistently into Q'trons in the NN to escape local minima. The so-built Q'tron NN, as a result, will settle down onto a solution state if and only if the instance of the given encryption scheme is realizable.

The General Neural-Network Paradigm for Visual Cryptography

In this paper, we propose a neural-network approach for visual authorization It is an application on visual cryptography. The scheme contains a key-share and a set of user-shares. The administrator owns the key-share, and each user owns a user-share issued by the administrator from the usershare set. The shares in the user share set are visually indistinguishable, i.e., they halve the same pictorial meaning. However, the stacking of the key-share with different user-shares will reveal significantly different images. Therefore, the administrator (in fact, only the administrator) can visually recognize the authority assigned to a particular user by viewing the information appearing in the superposed image of key-share and user-share.

A neural-network approach for visual cryptography and authorization

The semipublic encryption for visual cryptography using Q'tron neural networks

In this paper, we propose a neural-network approach for visual authorization, which is an application of visual cryptography (VC). The scheme contains a key-share and a set of user-shares. The administrator owns the key-share, and each user owns a user-share issued by the administrator from the user-share set. The shares in the user-share set are visually indistinguishable, i.e. they have the same pictorial meaning. However, the stacking of the key-share with different user-shares will reveal significantly different images. Therefore, the administrator (in fact, only the administrator) can visually recognize the authority assigned to a particular user by viewing the information appearing in the superposed image of key-share and user-share. This approach is completely different from traditional VC approaches. The salient features include: (i) the access schemes are described using a set of graytone images, and (ii) the codebooks to fulfil them are not required; and (iii) the size of share images is the same as the size of target image.

Suchen Chiang

Papers

A neural network approach for visual cryptography

The General Neural-Network Paradigm for Visual Cryptography

A neural-network approach for visual cryptography and authorization

The semipublic encryption for visual cryptography using Q'tron neural networks

A neural-network approach for visual cryptography and authorization