School Of Engineering And Information Technology

Large scale constrained problems 19 optimization 20 Decomposition approach 21 Variable interaction identification 22 2 3 a b s t r a c t 24 Cooperative Coevolutionary algorithms (CC) have been successful in solving large scale 25 optimization problems. The performance of CC can be improved by decreasing the number 26 of interdependent variables among decomposed subproblems. This is achieved by first 27 identifying dependent variables, and by then grouping them in common subproblems. This 28 approach has potential because so far no grouping technique has been mainly developed 29 for constrained problems. In this paper, a new variable interaction identification technique 30 to identify the dependent variables in large scale constrained problems is proposed. The 31 proposed technique is tested on both a new test suite of constrained problems with med-32 ium and high dimensions, which include overlapping subproblems and different levels of 33 complexity and nonseparability and also the established DED problem. The experimental 34 results have shown that the proposed technique contributes to the decomposition 35 approach over a range of high dimensions, in comparison with other state-of-the art group-36 ing techniques. It achieves better performance with higher feasibility ratios and less com-37 putational time. 38 Please cite this article in press as: E. Sayed Q1 et al., Decomposition-based evolutionary algorithm for large scale constrained problems, Inform. Sci. (2014), http://dx.performance for these complex problems at dimension 4000 and outperformed the aggregation approach [73]. A major 102 drawback of these techniques is that the subproblem size or the number of subproblems needs to be predefined. Differential 103 Grouping [63] is a simple decomposition technique that decomposes a large scale unconstrained problem into smaller sub-104

Large scale optimization problems are very challenging problems. Most of the recently developed optimization algorithms lose their efficiency when the dimensionality of the problems increases. Decomposing a large scale problem into smaller subproblems overcomes this drawback. However, if the large scale optimization problem contains dependent variables, they should be grouped into one subproblem to avoid a decrease in performance. In this paper, the Dependency Identification with Memetic Algorithm (DIMA) model is proposed for solving large scale optimization problems. The Dependency Identification (DI) technique identifies the best arrangement to group the dependent variables into smaller scale subproblems. These subproblems are then evolved using a Memetic Algorithm (MA) with a proposed self-directed Local Search (LS). As the subproblems of a nonseparable large scale problem may contain interdependent variables, the proposed model, DIMA, uses an Information Exchange Mechanism to maintain one value for all the instances of any independent variable in the different subproblems. A newly designed test suite of problems has been developed to evaluate the performance of DIMA. The first evaluation shows that the DI technique is competitive to other decomposition techniques in the literature in terms of consuming less computational resources and better performance. Another evaluation shows that DI makes the optimization of a decomposed large scale problem using DIMA as powerful as the optimization of a complete large scale problem using MA. This makes DIMA a promising optimization model for optimization problems which can be 10 times larger (or more) than the large scale optimization problems under consideration in this paper.

This paper proposes a chaotic fingerprint images encryption approach. An image of a fingerprint is encrypted via a twodimensional (2D) chaotic sequence obtained from multi-scroll chaotic attractors. Initial values of the chaotic attractors are served as the private key, which can be generated from the pixel distribution of the binary images of the captured fingerprints. Due to the dynamic uncertainties in the acquisition process of fingerprint images, the keys generated from the pixel value distribution are virtually random. With the elaborately designed 2D chaotic sequence, the encrypted fingerprint images have balanced 0-1 ratio and ideal nonlinearity. Only with the valid private key can the images of fingerprint be recovered. Simulation results and 2D-DFT validate this chaotic encryption approach.

Host-based anomaly intrusion detection system design is very challenging due to the notoriously high false alarm rate. This paper introduces a new host-based anomaly intrusion detection methodology using discontiguous system call patterns, in an attempt to increase detection rates whilst reducing false alarm rates. The key concept is to apply a semantic structure to kernel level system calls in order to reflect intrinsic activities hidden in high-level programming languages, which can help understand program anomaly behaviour. Excellent results were demonstrated using a variety of decision engines, evaluating the KDD98 and UNM data sets, and a new, modern data set. The ADFA Linux data set was created as part of this research using a modern operating system and contemporary hacking methods, and is now publicly available. Furthermore, the new semantic method possesses an inherent resilience to mimicry attacks, and demonstrated a high level of portability between different operating system versions.

Biometric authentication systems can address the problems of genuine user verification and usability suffered by the conventional cryptosystems based on the password and token. However, as biometrics is not replaceable, its privacy has become a concern. Also, if the same biometrics is used in multiple applications, it will be vulnerable to potential cross-matching attacks. In this paper, a projection line method is proposed for cancelable fingerprint template design. The main advantage of this method is its simplicity. Because there exist many possibilities projecting minutia into the projection line controlled by the whole range of possible projection line properties, the proposed approach offers non-invertible transformation and also possess good revocability property.

The wide-spread use of mobile devices has made it possible to develop mobile voting system as a complement to the existing electronic voting system. However, due to limited onboard resource, it is challenging to achieve both efficiency and security strength for mobile voting system. Conventional solution is to either use symmetric encryption algorithms or hybrid symmetric and asymmetric algorithms at the expense of weaker security strength. In our proposed mobile voting scheme, the users' votes are secured by using the elliptic curve cryptography (ECC) algorithm. ECC is chosen as it has smaller key size than other public key cryptographies; and its homomorphic encryption property which is able to keep users' anonymity. We evaluate the method of ECDH with AES and ECC in comparison with our proposed method. The experiment demonstrates that our proposed ECC-based scheme can outperform the traditional hybrid symmetric and asymmetric cryptographic scheme in the context of mobile evoting environment.

In response to the call for papers 127 papers were submitted to the conference. These papers were evaluated on the basis of their significance, novelty, and technical quality. Each paper was reviewed by at least three members of the Program Committee. The Program Committee meeting was held electronically, holding intensive discussion over a period of two weeks. Of the papers submitted, 28 full papers and 28 short papers were selected for presentation at the conference. Besides the technical program, composed of the papers collected in these proceedings, the conference includes two keynotes.

In this paper, we propose a method to directly estimate a camera's motion parameters by using normal flow vectors. In contrast to traditional methods, which tackle the problem by calculating optical flows or establishing motion correspondences, our proposed approach does not require conventional assumptions about the captured scene, such as consistent smoothness or distinct feature availability. In the proposed algorithm, the normal flows are classified into different groups, and each group will provide a possible solution regarding the camera's motion parameters. Then, the strategy of hypothesis and confirmation is adopted to eliminate the incorrect solutions. Finally, the optimal solution is obtained via the clustering algorithm. We have tested the proposed method on both synthetic image data and real image sequences. The experimental results illustrate the feasibility and reliability of the algorithm.

Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challenge today. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, we investigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topological features. Specifically, we propose an analytical approach for reconstructing the global topology representation from a partial fingerprint. Firstly, we present an inverse orientation model for describing the reconstruction problem. Then, we provide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in the existing segments while exploring missing structures in the unknown parts. We have further developed algorithms for estimating the missing orientation structures based on some a priori knowledge of ridge topology features. Our statistical experiments show that our proposed model-based approach can effectively reduce the number of candidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance for partial fingerprint identification. Index Terms-Partial fingerprint identification, global ridge orientation estimation, model-based approach. ! 1 INTRODUCTION D Espite tremendous progress made in automatic fingerprint identification systems, matching incomplete or partial fingerprints such as latent prints remains a critical challenge today. Existing partial fingerprint algorithms concentrate on improving the accuracy of one-to-one matching based on local ridge details, i.e. Level 2 and Level 3 features. Jea and Govindaraju [1] addresses the problem by using localized secondary features derived from the relative information of minutiae points. Fang et al. [2] exploits representative points along ridge lines in addition to minutiae points and reports improved matching performance. Chen and Jain [3] investigates the extended Level 3 features of dots and incipients to provide more information for partial fingerprint matching. However, the performance of their method depends on image quality as well as the number of high level features detectable in the partial fingerprint segments. These ad hoc algorithms are designed on the basis of more delicate one-to-one comparisons. When used in one-to-many applications, they generally assume sequential matching or that the candidate list for such matching has already been established. However, sequential matching is not efficient for large-scale identification which can involve thousands or millions of records in the target database and retrieving a short and reliable list of candidates for matching is difficult in practice. As suggested by the NIST report Concept of

An orientation model provides an analytical means for describing fingerprint ridge orientations. It can help in data storage and recovery as well as other possible communication applications related to biometric security. Since fingerprint ridge patterns often possess both smooth features and high curvature patterns, it is not easy to describe the overall topology with a single analytical model. A combination approach of different models is a way to address the problem. In this paper, we explore this topic by investigating local orientation models for estimating high curvature patterns in the singular regions. Our experimental results show that the resulting combination approach can improve the overall topology estimation and thus the end performance of a fingerprint authentication system.

Act (HIPAA) has set privacy and security regulations for the US healthcare industry. HIPAA has also established principles for security standards that global e-health industry tends to follow. In this paper, a hybrid public key infrastructure solution (HPKI) is proposed to comply with the HIPAA regulations. The main contribution is the new e-health security architecture that is contract oriented instead of session oriented which exists in most literatures. The proposed HPKI has delegated the trust and security management to the medical service provider during the contract period, which is more realistic. It is much an analogy to existing paper based health care systems in terms of functional structure. The cryptographically strong PKI scheme is deployed for the mutual authentication and the distribution of sensitive yet computational non-intensive data while efficient symmetric cryptographic technology is used for the storage and transmission of high volume of medical data such as medical images. One advantage is that the proposed HPKI can be constructed from existing cryptographic technologies where various relevant security standards, tools and products are available. Discussion has been provided to illustrate how proposed schemes can address the HIPAA privacy and security regulations.

Current computer networks and systems are facing serious threats from cyber terrorism and crime, which cause huge financial loss and potential life hazard. Although these attack systems have become more easy-to-use, sophisticated, and powerful, interest has greatly increased in the field of building more effective, intelligent, adaptive, active, and high performance networks and systems. This special issue focuses on issues related to Network and System Security, such as authentication, access control, availability, integrity, privacy, confidentiality, dependability and sustainability of computer networks and systems. We are pleased to present to you eight technical papers dealing with cutting-edge research and technology related to this topic. These papers were selected out of the significantly extended versions of the 131 submissions from 44 countries in the 4th International Conference on Network and System Security (NSS 2010) and a large number of open submissions. The selection has been very rigorous and only the best papers were selected. In the first paper, 'DHTrust: A robust and distributed reputation system for trusted peer-to-peer Networks' [1], Xue et al. propose a robust and distributed reputation system, DHTrust, which takes full advantage of the distributed hash table to distribute local reputation to trade off the damage of fake reputation information by genuine reputation information. This system can effectively distinguish and evaluate the fundamental behaviors of peers in peer-to-peer networks and thus to provide service and issue reputation scores. In the second paper, 'Breaking Tor anonymity with game theory and data mining' [2], Wagner et al. propose a mechanism to break Tor anonymity with one rogue exit node. The attack is composed of two elements: an active tag injection scheme and an inference attack that leverages a semi-supervised learning algorithm to reconstruct browsing sessions. A prototype is implemented and its performance is evaluated on the Tor network. The article also describes several countermeasures and interesting advanced attacks. In the third paper, 'Mobile device access control: an improved correlation based face authentication scheme and its Java ME application' [3], Xi et al. investigate face authentication based access control solutions for camera-equipped mobile devices. They propose a new hierarchical correlation based face authentication scheme by analyzing the relationship between each cross-correlation output peak generated from selected subregions of a face, in conjunction with conventional direct cross-correlation methods. The proposed algorithm is implementable on mobile devices with shortened processing time and reduced resource demand. In the fourth paper, 'Energy efficient authentication strategies for network coding' [4], Apavatjrut et al. investigate different strategies based on message authentication codes algorithms to deal with the attacks against network coding schemes. In wireless sensor networks, the node resources are limited in energy and in computational power. The authors develop new strategies to defend against pollution attacks on wireless sensor networks. In the fifth paper, 'On the identity anonymization of high dimensional rating data' [5], Sun et al. study the challenges of protecting privacy of individuals in the large public survey rating data, which is prone to attacks that can potentially identify user information. The authors propose a greedy approach to anonymize survey rating data that scales almost linearly with the input size. They apply the method to two real-life data sets to demonstrate their efficiency and practical utility. Intrusion detection systems (IDS) have been widely deployed in practice for detecting malicious behaviors. In the sixth paper, 'An alert correlation platform for memory-supported techniques' [6], Roschke et al. present a new way to enhance the detection process of intrusion detection systems by correlation and clustering of alerts. The proposed platform can be distributed over multiple processing units to share memory and processing power. The efficiency and the effectiveness of the platform

h i g h l i g h t s • We have proposed a KP-ABE scheme with ''attribute privacy'' features. • We have introduced a general transformation from the KP-ABE scheme to the ABEKS scheme. • We have designed a secure ABEKS scheme by the transformation. • We have provided the proof of security and consistency of the ABEKS scheme.

Being the most important critical infrastructure in Cyber-Physical Systems (CPSs), a smart grid exhibits the complicated nature of large scale, distributed and dynamic environment. Taxonomy of attacks is an effective tool in systematically classifying attacks and it has been placed as a top research topic in CPS by a National Science Foundation (NSG) Workshop. Most existing taxonomy of attacks in CPS are inadequate in addressing the tight coupling of cyber-physical process or/and lack systematical construction. This paper attempts to introduce taxonomy of attacks of agent-based smart grids as an effective tool to provide a structured framework. The proposed idea of introducing the structure of space-time and information flow direction, security feature, and cyber-physical causality is innovative and it can establish a taxonomy design mechanism that can systematically construct the taxonomy of cyber attacks which could have potential impact on the normal operation of the agent based smart grids. Based on the cyber-physical relationship revealed in the taxonomy, a concrete physical process based cyber attack detection scheme has been proposed. A numerical illustrative example has been provided to validate the proposed physical process based cyber detection scheme.

In this paper, we have proposed a fingerprint orientation model based on 2D Fourier expansions (FOMFE) in the phase plane. The FOMFE does not require prior knowledge of singular points (SPs). It is able to describe the overall ridge topology seamlessly, including the SP regions, even for noisy fingerprints. Our statistical experiments on a public database show that the proposed FOMFE can significantly improve the accuracy of fingerprint feature extraction and thus that of fingerprint matching. Moreover, the FOMFE has a low-computational cost and can work very efficiently on large fingerprint databases. The FOMFE provides a comprehensive description for orientation features, which has enabled its beneficial use in feature-related applications such as fingerprint indexing. Unlike most indexing schemes using raw orientation data, we exploit FOMFE model coefficients to generate the feature vector. Our indexing experiments show remarkable results using different fingerprint databases.

Key management has become a challenging issue in the design and deployment of secure wireless sensor networks. A common assumption in most existing distributed key management schemes is that all sensor nodes have the same capability. However, recent research works have suggested that connectivity and lifetime of a sensor network can be substantially improved if some nodes are given greater power and transmission capability. Therefore, how to exploit those heterogeneity features in design of a good distributed key management scheme has become an important issue. This paper proposes a unified framework for distributed key management schemes in heterogeneous wireless sensor networks. Analytical models are developed to evaluate its performance in terms of connectivity, reliability, and resilience. Extensive simulation results show that, even with a small number of heterogeneous nodes, the performance of a wireless sensor network can be improved substantially. It is also shown that our analytical models can be used to accurately predict the performance of wireless sensor networks under varying conditions.