I-En Liao

A password authentication scheme over insecure networks

Authentication ensures that systems resources are not obtained fraudulently by illegal users. Password authentication is one of the simplest and the most convenient authentication mechanisms over insecure networks. The problem of password authentication in an insecure networks is present in many application areas. Since computing resources have grown tremendously, password authentication is more frequently required in areas such as computer networks, wireless networks, remote login, operation systems, and database management systems. Many schemes based on cryptography have been proposed to solve the problem. However, previous schemes are vulnerable to various attacks and are neither efficient, nor user friendly. Users cannot choose and change their passwords at will. In this paper, we propose a new password authentication scheme to achieve the all proposed requirements. Furthermore, our scheme can support the Diffie-Hellman key agreement protocol over insecure networks. Users and the system can use the agreed session key to encrypt/decrypt their communicated messages using the symmetric cryptosystem.

Security Enhancement on a New Authentication Scheme With Anonymity for Wireless Environments

In a paper recently published in the IEEE Transactions on Consumer Electronics, Zhu and Ma proposed a new authentication scheme with anonymity for wireless environments. However, this paper shows that Zhu and Mas scheme has some security weaknesses. Therefore, in this paper, a slight modification to their scheme is proposed to improve their shortcomings. As a result, the scheme proposed in this paper can enhance the security of Zhu and Mas scheme. Finally, the performance of this scheme is analyzed. Compared with the Zhu-Ma scheme, this scheme is also simple and efficient

Security enhancement for a dynamic ID-based remote user authentication scheme

In a paper recently published in the IEEE transaction on consumer electronics, Das, Saxena, and Gulati proposed a dynamic ID-based remote user authentication scheme using smart cards that allows the users to choose and change their passwords freely, and does not maintain any verifier table. It can protect against ID-theft, replaying, forgery, guessing, insider, and stolen verifier attacks. However, this paper shows that Das, Saxena, and Gulatis scheme has some attacks. Therefore, we propose a slight modification to their scheme to improve their weaknesses. As a result, the improved scheme can enhance the security of Das, Saxena, and Gulatis scheme. In addition, the proposed scheme does not add many computational costs additionally. Compare with their scheme, our scheme is also efficient.

An improved frequent pattern growth method for mining association rules

Many algorithms have been proposed to efficiently mine association rules. One of the most important approaches is FP-growth. Without candidate generation, FP-growth proposes an algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively constructs FP-trees to find all frequent itemsets. Performance results have demonstrated that the FP-growth method performs extremely well. In this paper, we propose the IFP-growth (improved FP-growth) algorithm to improve the performance of FP-growth. There are three major features of IFP-growth. First, it employs an address-table structure to lower the complexity of forming the entire FP-tree. Second, it uses a new structure called FP-tree+ to reduce the need for building conditional FP-trees recursively. Third, by using address-table and FP-tree+ the proposed algorithm has less memory requirement and better performance in comparison with FP-tree based algorithms. The experimental results show that the IFP-growth requires relatively little memory space during the mining process. Even when the minimum support is low, the space needed by IFP-growth is about one half of that of FP-growth and about one fourth of that of nonordfp algorithm. As to the execution time, our method outperforms FP-growth by one to 300 times under different minimum supports. The proposed algorithm also outperforms nonordfp algorithm in most cases. As a result, IFP-growth is very suitable for high performance applications.

A new approach for data clustering and visualization using self-organizing maps

A self-organizing map (SOM) is a nonlinear, unsupervised neural network model that could be used for applications of data clustering and visualization. One of the major shortcomings of the SOM algorithm is the difficulty for non-expert users to interpret the information involved in a trained SOM. In this paper, this problem is tackled by introducing an enhanced version of the proposed visualization method which consists of three major steps: (1) calculating single-linkage inter-neuron distance, (2) calculating the number of data points in each neuron, and (3) finding cluster boundary. The experimental results show that the proposed approach has the strong ability to demonstrate the data distribution, inter-neuron distances, and cluster boundary, effectively. The experimental results indicate that the effects of visualization of the proposed algorithm are better than that of other visualization methods. Furthermore, our proposed visualization scheme is not only intuitively easy understanding of the clustering results, but also having good visualization effects on unlabeled data sets.

Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information

Indoor location determination has emerged as a significant research topic due to the wide-spread deployment of wireless local area networks (WLANs) and the demand for context-aware services inside buildings. However, prediction accuracy remains a primary issue surrounding the practicality of WLAN-based location determination systems. This study proposes a novel scheme that utilizes mobile user orientation information to improve prediction accuracy. Theoretically, if the precise orientation of a user can be identified, then the location determination system can predict that users location with a high degree of accuracy by using the training data of this specific-orientation. In reality, a mobile users orientation can be estimated only by comparing variations in received signal strength; and nevertheless the predicted orientation may be incorrect. Incorrect orientation information causes the accuracy of the entire system to decrease. Therefore, this study presents an accumulated orientation strength algorithm which can utilize uncertain estimated orientation information to improve prediction accuracy. Implementation of this system is based on the Bayesian model, and the experimental results indeed show the effectiveness of our proposed approach.

Shielding wireless sensor network using Markovian intrusion detection system with attack pattern mining

Wireless sensor nodes are congenitally limited by insufficient hardware resources, such as memory size and battery life. These factors influence the lifespan of wireless sensor networks and pose numerous challenges regarding the addition of security mechanisms to protect sensor nodes. As the number of applications using wireless sensor networks increases, protecting sensor nodes from malicious attacks becomes ever more important. In this paper, we propose a new intrusion detection system called the Markovian IDS, to protect sensor nodes from malicious attacks. The Markovian IDS incorporates game theory with anomaly and misuse detection to determine the best defense strategies. It also employs Markov decision processes with an attack-pattern-mining algorithm to predict future attack patterns and implement appropriate measures. Experimental results show that the proposed Markovian IDS has a higher defense success rate than game theory or Markov decision processes alone.

A forward authentication key management scheme for heterogeneous sensor networks

Key encryption technology is a basic technique for protecting the secrecy of transmitted data among sensor nodes in wireless sensor networks. However, sensor nodes are inherently limited by insufficient hardware resources such as memory capacity and battery lifetime. As a result, few current key management schemes are appropriate for wireless sensor networks. This paper proposes a new key management method that uses dynamic key management schemes for heterogeneous sensor networks. The proposed scheme loads a hash function into the base station, cluster heads, and sensor nodes. The cluster heads and sensor nodes then generate their own keychains to provide forward authentication in case of key changes, security breaches, key changes due to security breaches. The cluster heads and sensor nodes establish pairwise keys to ensure transmission secrecy. The proposed scheme decreases the number of keys required for sensor nodes and cluster heads and is robust to the following attacks: guessing attacks, replay attacks, man-in-the-middle attacks, node capture attacks, and denial-of-service attacks.

A library recommender system based on a personal ontology model and collaborative filtering technique for English collections

Purpose – The purpose of this paper is not only to design a more effective recommendation system for libraries, but also to eliminate many of the weaknesses found in the existing library recommender systems.Design/methodology/approach – A novel library recommender system was designed for English collections by integrating personal ontology model and collaborative filtering model with domain specification.Findings – The trend of the traditional library is evolving toward that of digital library. The personal ontology recommender (PORE) system offers a friendly user interface and provides several personalized services.Research limitations/implications – This system is only implemented and tested in the Library of National Chung Hsing University in Taiwan.Originality/value – The paper demonstrates a good methodology to offer an active, effective, and personalized recommendation system for library patrons.

Ontology-based library recommender system using MapReduce

Recommender systems have been proven useful in numerous contemporary applications and helping users effectively identify items of interest within massive and potentially overwhelming collections. Among the recommender system techniques, the collaborative filtering mechanism is the most successful; it leverages the similar tastes of similar users, which can serve as references for recommendation. However, a major weakness for the collaborative filtering mechanism is its performance in computing the pairwise similarity of users. Thus, the MapReduce framework was examined as a potential means to address this performance problem. This paper details the development and employment of the MapReduce framework, examining whether it improves the performance of a personal ontology based recommender system in a digital library. The results of this extensive performance study show that the proposed algorithm can scale recommender systems for all-pairs similarity searching.