Earticle

The purpose of this work is to present a route choice decision-making method under uncertainty conditions, and to explain the route choice behavior process from behavior point of view. A method of grey relational analysis is presented to handle route choice decision-making problem, using interval number vectors of route attributes and incomplete attribute weights vectors. The influence factors of route choice are analyzed firstly. Then giving the interval values of each route attributes, the grey relational coefficient of each route alternative from positive ideal solution (PIS) and negative ideal solution (NIS) are calculated, and an optimal model is provided in order to get the suitable attribute weights vector. Furthermore, all alternative routes are ranked according to the rate values. In the end, a numerical example is given with the real network of 3 lanes and 7 alleys in Fuzhou, and the results show that the proposed method is simple and effective.

Performance of AOMDV routing protocol is more reliable and efficient than other routing protocols in ad hoc network. It provides multipath routing which means it follows multipath to send data packets from source to destination. It do not follow multipath simultaneously but when failure occurs in one path, then it immediately discovers new path and then delivers packets up to destination. When any DDOS attack occurs in a network, then it degrades the performance of AOMDV routing protocol. Under this attack AOMDV consumes more power and bandwidth which further degrades the performance and makes it unreliable. In this paper a new protocol is proposed named EAOMDV i.e. Enhanced Ad hoc on demand distance vector routing protocol. It is enhance version of AOMDV which overcomes the problems that occurs during attack in AOMDV. In this, DDOS flooding attack is introduced. Then performance of existing and proposed protocol is measured under various parameters i.e. throughput, packet delivery ratio, end to end delay and normalized routing load.

This paper proposes a new fragile video watermarking algorithm based on motion vector, we first use the frame difference method to detect moving targets, determine the motion macro blocks. Then in the macroblock by the diamond search, the fragile watermark is embedded in the P-frame motion vectors for inter prediction, so that by changing the mapping of pixel precision motion vector is to be embedded watermark information embedding watermark information. Experimental results show little effect on the video quality of the algorithm.

In Augmented Reality (AR), users’ main concern includes privacy and safety of data. Since location based services(LBS) are one of the major applications of the AR, it is important to have a privacy-aware management of location information, providing location privacy for clients against vulnerabilities or abuse. Here we analyzed the merit and demerit of exiting location privacy protection method. Then a perceived K-value location privacy protection method was raised. Hereafter the protocol of this algorithm was described and simulated in detail. The results demonstrated this method can effectively realize the location privacy protection.

There were about nine hundred information protection human resources less in Korea in 2012 but this number is expected to increase to about two thousand, five hundred in 2015, widening the gap between demand and supply. This is more of a serious qualitative issue than a quantitative issue in terms of scarce resources. There is an average proficiency gap of 1.2 points out of seven points among the resources companies would like to hire. In particular, among the twenty-five knowledge and technology levels, fields such as cryptology only meet about 70% of the industry's expectation level. To solve the quantitative supply and demand gap of information protection human resources, it is necessary to attract more human resources into the information protection industry through various training paths. Also, in order to enhance the qualitative fidelity level of the information protection resources, there is a need to foster customized talents that meet company demands by adjusting training curriculum according to proficiency differences.

Location-based services (LBS) become more feature-rich and versatile due to the explosion of mobile devices and the advances of positioning technologies. However, revealing personal private locations to potentially un-trusted LBS server and others may raise serious privacy concerns if these locations are not protected adequately. In this paper, we present a survey of existing methods dealing with outdoor and indoor localization techniques and location privacy protection techniques. We propose a taxonomy that summarizes the state-of-the-art. This survey is intended to help researchers in quickly understanding existing works and challenges, and possible improvements to bring.

Along with the rapid growth of the number of intelligent mobile devices as well as applications in recent years owing to the development of network information and telecommunication technologies, personal privacy faces a unprecedented level of risk. This seems to be especially inevitable in digital forensic investigations. Consequently, there is an urgent need to balance the desire for privacy preservation and information extraction in the process of digital forensic investigations. This paper focuses on the issues related to personal privacy protection by proposing a digital forensics framework based on the theory of ontology. In addition to introducing the basic concepts that will lead to the establishment of the framework, we will also present some experiments that can be viewed as the demonstration of the effectiveness of the framework in privacy protection in the process of digital forensic investigations. The framework can thus serve as the foundation in the design of digital forensic tools and systems that can respect the privacy of individuals.

The rapid increase in advanced persistent threats in the cyber space engenders full attention to the use of intrusion detection with emphasis on Artificial Intelligence-based intrusion detection systems as a mitigation mechanism. The sharp increase in attack surfaces can be partially attributed to the fact that Internet becomes the de facto means of converged communications and online transactions accommodating different types of services under the same scheme. Most current intrusion detection systems (IDS) deploy signature patterns of known attacks and anomaly detection approaches in detecting intrusions in an attempt to reduce the computational complexity introduced by large scale data sets. However, these approaches have been proved to be inadequate to detect novel attacks often resulting in a high false positive rate. This research will therefore seek to address the issue of detecting persistent network threats by combining the approaches of misuse and anomaly detection in one system. Our algorithm incorporates the concept of active response against all four broad attack types analyzed in the literature to realize another algorithm for intrusion detection and prevention as well as active response called HYBRITQ-4. The algorithm introduces a mechanism for classifying packets based on protocol information to enhance pattern searches and matching when detecting abnormal packets. Findings from our investigation suggest that the proposed algorithm can efficiently improve the detection rate, false positive rate and accuracy of detecting intrusions in patterns of known and novel attacks.

A new algorithm FTRA has been proposed, which infuses users’ trust network and rating data. The sparse problem of rating data will significantly reduce the accuracy of collaborative filtering recommendation. In addition to the users’ ratings data on the Internet, other data sources which can be used in the process of recommend, and one of the more common is trust network data which describes the mutual relationship between users. To solve this problem, this paper will the data of trust network as an important supplement on the rating data, and bases on graph theory concepts or methods, the similarity method in the paper, and the Katz method which is used to calculate the similarity of link, proposes the FTRA algorithm which organic infuses this two data, and then better to solve the sparse problem of the rating data faced by collaborative filtering. The experimental results on the Epinions dataset show that the FTRA algorithm is superior to or significantly better than the comparison algorithms, which include the algorithms that only based on the rating data or the trust relationship, and the other algorithms infusing the two data sources.

Hybrid intrusion detection systems that make use of data mining techniques, in order to improve effectiveness, have been actively pursued in the last decade. However, their complexity to build detection models has become very expensive when confronted with large-scale datasets, making them unviable for real-time retraining. In order to overcome the limitation of the conventional hybrid method, we propose a new lightweight hybrid intrusion detection method that consists of a combination of feature selection, clustering and classification. According to our hypothesis that there are different natures of attack events in each of network protocols, the proposed method examines each of network protocol data separately, but their processes are the same. First, the training dataset is divided into training subsets, depending on their type of network protocol. Next, each training subset is reduced dimensionally by eliminating the irrelevant and redundant features throughout the feature selection process; and then broken down into disjointed regions, depending on their similar feature values, by K -Means clustering. Lastly, the C4.5 decision tree is used to build multiple misuse detection models for suspicious regions, which deviate from the normal and anomaly regions. As a result, each detection model is built from high-quality data, which are less complex and consist of relevant data. For better understanding of the enhanced performance, the proposed method was evaluated through experiments using the NSL-KDD dataset. The experimental results indicate that the proposed method is better in terms of effectiveness (F-value: 0.9957, classification accuracy: 99.52%, false positive rate: 0.26%), and efficiency (the training and testing times of the proposed method are approximately 33% and 25%, respectively, of the time required for its comparison) than the conventional hybrid method using the same algorithm.

One of the greatest challenges in vehicular ad-hoc networks (VANET) is to establish stable routing. Due to dynamic network , keeping stable routing is a difficult problem. Therefore, trust-based stable routing (TBSR) protocol is proposed in this paper. The node is chosen to be next-hop node until it reaches to trust threshold. To evaluate the node trust, we introduce the node' role trust, interaction printer trust, and recommendation trust. Moreover, the Analytic Hierarchy algorithm is used to coalesce various recommendation opinions from neighbor nodes. The simulation results show that the proposed approach outperforms GPSR (greedy perimeter stateless routing) and OLSR (optimized link-state routing) in terms of throughput, average delay .

Advanced Encryption Standard (AES) is used in many security systems for its high security. However, the high computation complexity of the round encryption of AES makes AES hard to be performed on many embedded systems with constrained resource. This paper proposes a novel round encryption method based on a 512-Byte lookup table. The experiments results show that, the AES implementation utilizing the round encryption method proposed reaches an encryption performance comparable to that of the widely used implementation with 4 1-KB lookup tables, while consumes much less storage overhead.

Image forgery is a major issue in today's digital publishing and printing. Now a day’s system can be used for forensic purpose to validate the authenticity of an image. In this paper we present an approach for image forgery authentication. We observe that a non morphed and non forged image shows homogeneity in non spectral domain. This homogeneity is lost when any forgery or morphing is applied on the images. We therefore apply a set of transform over the images. We combine DCT statistics, LBP features with curvelet statistics and Gabor transform of the images to represent an image in the transformed domain. CASIA image dataset with seven thousand authentic and same numbers of tempered images is used to verify the technique. We divide the dataset into equal halves to perform training and testing. Transformed images are used to train Hidden Markov model as HMM can extract probabilistic state information from a large statistical model. A test images is tested in transformed domain by HMM with log likelihood estimator. In case HMM returns an indeterminist result or multiple subset of result, the transformed test image is tested with two class SVM classifier with RBF kernel. Results show that the accuracy of the system is over 89% for 500 test instances.

This paper analyzes the present situation of the current network security problems and points out the research and development of intrusion detection system has very important significance on the basis of comparative analysis of the traditional static security model and PPDR dynamic security model, and according to this model, using ant colony algorithm is a distributed computing network intrusion of metrics, the determination of index contrast and invasion route, increase the accuracy of testing operation and calculation results show that the effectiveness of the solution and the convergence speed. For distributed network intrusion is put forward a new kind of means.

Biometric system is using for personal recognition of people in many social and economical activities now a days. A good biometric trait should be measurable, distinctive and stable over time. Real-world deployment of biometric systems often has to contend with degraded signal quality and erratic behavior of the biometric data. For last few years biometric data quality measure become an important concern after poor pathological sample and other many causes. The user, sensor and environmental facts are causes to quality degradation of biometric system. This study approaches that have been used to extract additional information about the biometric data that can then be used to improve performance in degraded conditions and also discuss about the sensor and environmental facts .This study will also discuss how this problem can be overcome to maintain the quality in biometric system with a special emphasis on face, fingerprint, iris modalities with different organizational standards.

With the rapid development of computer network, the problem of reliability of computer network is becoming more and more attention of builders, users and the network designer. The reliability of computer network has become a key technical indicators to measure the comprehensive performance of computer network. Aiming at a single known computer network, applying to established the computer network link cost model and the computer network reliability model, and carries on the simulation using the genetic algorithm intelligent algorithm; comprehensive evaluation for a variety of known different computer network reliability index system, and we have given the evaluation results. The simulation results show that, the established computer network link cost model and the computer network reliability model in the thesis are suitable, model algorithm and programming language is effective, it has the practical application significance which the paper proposed the computer network reliability optimization design concrete plan.

In this paper, an image watermarking scheme is presented, in which Least Significant Bit and discrete cosine transform are used. The discrete cosine transform is performed on the original host image, and the secret watermark image is embedded into the coefficient of discrete cosine transform, which will replace the least significant bit. The embedded secret watermark bit will cause minimal distortion of the original host image, but we cannot find the difference of the original host image and the watermarked image. The experiment based on this algorithm demonstrates that the watermarking is robust to the common signal processing techniques, including noise attack, JPEG Compression attack and so on.

In the recent years, Wireless Sensor Networks (WSN) are widely used to reap the benefits of sensing capacity of mobile nodes and its wireless mode of communication. Sensor network architectures can be used for various applications that include intrusion detection, military patrols, border monitoring, etc. The security of nodes in a sensor network is a challenging issue as they are easy targets for attacks. The node replication attack is one among those attacks. The adversary can capture security credentials from a node by compromising the node. The adversary can replicate the node using the same ID. To rectify this problem in a sensor network, various replica node detection techniques have been proposed. These techniques do not work well when multiple replicas are introduced in the network against genuine nodes. Subsequently, the detection capability of the network is degraded. To overcome these problems, this paper proposes Enhanced Efficient Distributed Detection (EEDD) algorithm. It combines the best features of EDD and danger theory of artificial immune system. This hybrid approach EEDD detects the clones. This method prevents multiple replicas in the WSN. The advantages of the proposed method include (i) increased detection rate, (ii) decreased overheads, (iii) high Packet Delivery Ratio (PDR) and (iv) low energy consumption. The proposed method is tested in a simulated environment.

In this paper, a novel blind color image watermarking technique using U matrix through QSVD transformation is proposed to protect the intellectual property rights of color images. The proposed method tries to insert the watermark through moderating the coefficient of the quaternion elements in U matrix. .In this method, the color image is considered as an array of pure quaternion numbers. Then the array of pure quaternion is divided into non-overlapping blocks and we perform QSVD to the block to get the U matrix. The watermarking is inserted into the coefficient of the quaternion elements in the first column of the U matrix. Besides, in the procedure of watermark insertion and extraction, ensuring higher fidelity and robustness and resilience to several possible image attacks have been considered. The experimental results showed that the proposed method performance created watermarked images with better PSNRs and more robustness versus several attacks such as Salt &Pepper noise, Brightness adjustment, Sharpen and Blurring and Clipping.

With the development of IT and internet services, the cloud computing system has attracted much interest in processing the big data efficiently. Because services using big data on the cloud computing environment consider a lot of users, an efficient user access control scheme is required. However, the existing schemes have a critical problem that the cost of the key management for the user access control is high. To solve the problem, we propose a user access control scheme for reducing authentication keys in cloud systems. The proposed scheme can reduce the number of keys based on a resource set tree by applying the minimum spanning tree. Finally, we show from the performance analysis that the proposed scheme outperforms the existing scheme in terms of key generation cost.

In this paper, we focus on the problem of signature and role extraction from large-scale mail archives. Due to the huge scale and great diversity of large-scale mail archives, the extraction methods should not only be able to extract signatures and roles accurately without any training data, but also be general enough to work well with large-scale mail archives with different characteristics. To address this problem, we first propose an unsupervised language model based method to identify sig-natures from large numbers of emails, and then present an unsupervised two-stage method to effectively extract roles from the identified signatures. Experimental results on two real-world datasets show that our methods are general and effective for both the signature and role extrac-tion from large-scale mail archives.

Multibiometrics is the usage of more than one physiological or behavioral characteristic to identify an individual. Multibiometrics is advantageous over unibiometrics as it is resilience to spoofing and has low False Acceptance Rate (FAR). However Multibiometrics requires storage of multiple biometric templates for each user, which results in increased risk to user privacy and system security. This paper will discuss the concept off biometrics and biometric system, multimodal biometric fusion techniques, crypto-biometrics and an algorithm for session key generation for secure communication of data.

In recent years, the problem of network security has been more and more people's attention, as one of the most important technology of network security, intrusion detection technology has gone through nearly thirty years of development, but it still exists some deficiency factors. Aiming at the defects of the traditional BP neural network intrusion detection model in the detection rate and the convergence speed, the improved PSO-BP neural network is applied to intrusion detection system model in this paper. Experimental and simulation, verifying the improved effect of system in the false negative rate, false positives rate and convergence speed of. Detailed analysis of the standard BP neural network algorithm and improved way of common, including gradient descent algorithm and additional momentum algorithm. Local search capability of BP neural network and the global search ability of particle swarm optimization , we have a detailed description of the PSO algorithm is applied to the case of BP neural network and discusses the improved PSO-BP neural network algorithm flow.

Feature selection and classifier design is the key to network intrusion detection. In order to improve network intrusion detection rate for feature selection problem, this paper proposed a network intrusion detection method (ACO-FS -SVM) combining ant colony algorithm to select the features with a feature weighting SVM. First, the use of support vector machine classification accuracy and feature subset dimension construct a comprehensive fitness weighting index. Then use the ant colony algorithm for global optimization and multiple search capabilities to achieve optimal solutions feature subset search feature. And then selected the key feature of network data and calculated information gain access to various features weights and heavy weights to build support vector machine classifier based on the characteristics of network attacks right. At last, refine the final design of the local search methods to make the feature selection results without redundant features while improve the convergence resistance, and verify the data set by KDD1999 effectiveness of the algorithm. The results show that ACO-FS-SVM can effectively reduce the dimension of features, and have improved network intrusion detection accuracy and detection speed.

The popularity of location-based services leads to serious concerns on user privacy. It is very easy for a person to know his/her location with the help of devices having GPS facility. When user’s location is provided to Location Based Services (LBS), it is possible for user to know all location dependent information like location of friends or Nearest Restaurant, weather or traffic conditions. The massive use of mobile devices paves the way for the creation of wireless networks that can be used to exchange information. When the exchange of information is done amongst entrusted parties, the privacy of the user could be in harmful. Existing protocol doesn’t work on many different mobile devices and another issue is that, Location Server (LS) should provide misleading data to user. This gives rise to new challenges and reconsideration of the existing privacy metrics.

A novel technique for intrusion detection based on real-valued dual negative selection scheme is proposed in this paper. In traditional real-valued negative selection algorithms, whether the candidate detectors can detect self-set or not totally relies on the affinity extent and the constant-sized mechanism is unfavorable to eliminating the black holes with irregular sizes. The proposed technique introduces the mechanism of variable-sized dual negative selection, in which each mutual detector has to pass three tests. Firstly, the new mutual detector should not be detected by the current existing ones. In other words, the existence of the new detector is necessary. Secondly, those detectors which can detect self-set will be eliminated. Thirdly, the detectors distribution has to be optimized aiming at enhancing the detecting efficiency. Experimental results demonstrate that the proposed technique has much less black holes, fewer detectors and higher detecting rates.

Now days, Data security is very challenging issue that touches many areas including computers and communication. Recently, we came across many attacks on cyber security that have played with the confidentiality of the users. These attacks just broke all the security algorithms and affected the confidentiality, authentication, integrity, availability and identification of user data. Cryptography is one such way to make sure that confidentiality, authentication, integrity, availability and identification of user data can be maintained as well as security and privacy of data can be provided to the user. Encryption is the process of converting normal data or plaintext to something incomprehensible or cipher-text by applying mathematical transformations or formulae. These mathematical transformations or formulae used for encryption processes are called algorithms. We have analysed ten data encryption algorithms DES, Triple DES, RSA, AES, ECC, BLOWFISH, TWOFISH, THREEFISH, RC5 and IDEA etc. Among them DES, Triple DES, AES, RC5, BLOWFISH, TWOFISH, THREEFISH and IDEA are symmetric key cryptographic algorithms. RSA and ECC are asymmetric key cryptographic algorithms. In this paper, we have analysed various encryption algorithms on the basis of different parameters and compared them to choose the best data encryption algorithm so that we can use it in our future work.

Traditional network intrusion detection algorithms are time consuming due to the existence of redundant attributes. In order to improve the efficiency of network intrusion detection, in this paper, we propose a wavelet transform based support vector machine ensemble algorithm. Firstly, we use wavelet transform to remove the redundant attributes from the original dataset. Then we train a support vector machine ensemble on the simplified dataset. As the wavelet transform in this algorithm can effectively remove the redundant attributes, the proposed algorithm is with high efficiency. Simulation experiments on KDD CUP 99 data set show that the proposed algorithm has good intrusion detection performance.

Recently, one of the growing hot topics in the field of wireless network is to enable of Delay/Disruption Tolerant Networking (DTN). It aims to solve the long delay paths and unpredictable link disruptions that occur in the challenged networks. In order to maintain a safe and secure DTN model, a secure way of key distribution would be required to provide robustness from all the attacks it suffers. This paper proposes a clear and illustrative security architecture for DTN with secure key management framework to distribute the cryptographic keys to the constituted nodes in a secure way. The performance of how the cryptographic keys work and perform during a node is departed away from the network are discussed. The distributed keys use the public key cryptography to mitigate during the network attacks. Along with the key management protocol, some degree of security features can be achieved with the bundle of security protocol to rescue the network model from any attacks.