Rong-Jian Chen

A novel intrusion detection system based on hierarchical clustering and support vector machines

This study proposed an SVM-based intrusion detection system, which combines a hierarchical clustering algorithm, a simple feature selection procedure, and the SVM technique. The hierarchical clustering algorithm provided the SVM with fewer, abstracted, and higher-qualified training instances that are derived from the KDD Cup 1999 training set. It was able to greatly shorten the training time, but also improve the performance of resultant SVM. The simple feature selection procedure was applied to eliminate unimportant features from the training set so the obtained SVM model could classify the network traffic data more accurately. The famous KDD Cup 1999 dataset was used to evaluate the proposed system. Compared with other intrusion detection systems that are based on the same dataset, this system showed better performance in the detection of DoS and Probe attacks, and the beset performance in overall accuracy.

Performance evaluation of score level fusion in multimodal biometric systems

In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper the performance of sum rule-based score level fusion and support vector machines (SVM)-based score level fusion are examined. Three biometric characteristics are considered in this study: fingerprint, face, and finger vein. We also proposed a new robust normalization scheme (Reduction of High-scores Effect normalization) which is derived from min-max normalization scheme. Experiments on four different multimodal databases suggest that integrating the proposed scheme in sum rule-based fusion and SVM-based fusion leads to consistently high accuracy. The performance of simple sum rule-based fusion preceded by our normalization scheme is comparable to another approach, likelihood ratio-based fusion [8] (Nandakumar et al., 2008), which is based on the estimation of matching scores densities. Comparison between experimental results on sum rule-based fusion and SVM-based fusion reveals that the latter could attain better performance than the former, provided that the kernel and its parameters have been carefully selected.

An improved SVD-based watermarking technique for copyright protection

The drawbacks of SVD-based image watermarking are false positive, robust and transparency. The former can be overcome by embedding the principal components of the watermark into the host image, the latter is dependent on how much the quantity (i.e., scaling factor) of the principal components is embedded. For the existing methods, the scaling factor is a fixed value; actually, it is image-dependent. Different watermarks need the different scaling factors, although they are embedded in the same host image. In this paper, two methods are proposed to improve the reliability and robustness. To improve the reliability, for the first method, the principal components of the watermark are embedded into the host image in discrete cosine transform (DCT); and for the second method, those are embedded into the host image in discrete wavelets transform (DWT). To improve the robustness, the particle swarm optimization (PSO) is used for finding the suitable scaling factors. The experimental results demonstrate that the performance of the proposed methods outperforms than those of the existing methods.

A Novel Anti-Collision Algorithm in RFID Systems for Identifying Passive Tags

Radio frequency identification has been developed and used in many applications in the real world. Due to the shared wireless channel between tags and the reader during communication, the tag collision arbitration is a significant issue for reducing the communication overhead. This paper presents a novel anti-collision algorithm named New Enhanced Anti-Collision Algorithm (NEAA) using counters and stack to reduce the probability of collision efficiently and to make it possible to identify multiple passive tags in a timeslot. The upper bound of total timeslots for identifying N passive tags is first derived in this paper; suppose the length of a tag ID is n, the upper bound of total timeslots for identifying N (N= 2n) passive tags is derived to be 2n-1 - n + 4, when n > 2. This bound is quite tight. Compared to the existing methods proposed by other researchers, the performance evaluation shows that the proposed scheme in this paper consumes fewer timeslots and has better performance for identifying tags.

An efficient phishing webpage detector

Phishing attack is growing significantly each year and is considered as one of the most dangerous threats in the Internet which may cause people to lose confidence in e-commerce. In this paper, we present a heuristic method to determine whether a webpage is a legitimate or a phishing page. This scheme could detect new phishing pages which black list based anti-phishing tools could not. We first convert a web page into 12 features which are well selected based on the existing normal and fishing pages. A training set of web pages including normal and fishing pages are then input for a support vector machine to do training. A testing set is finally fed into the trained model to do the testing. Compared to the existing methods, the experimental results show that the proposed phishing detector can achieve the high accuracy rate with relatively low false positive and low false negative rates.

Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques

In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) method for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series and particle swarm optimization. The MTPSO model can be dealt with two main factors easily and accurately, which are the lengths of intervals and the content of forecast rules. The experimental results of the temperature prediction and the TAIFEX forecasting show that the proposed model is better than any existing models and it can get better quality solutions based on the high-order fuzzy time series, respectively.

Novel SCAN-CA-based image security system using SCAN and 2-D von Neumann cellular automata

This paper presents a novel SCAN-CA-based image security system which belongs to synchronous stream cipher. Its encryption method is based on permutation of the image pixels and replacement of the pixel values. Permutation is done by scan patterns generated by the SCAN approach. The pixel values are replaced using the recursive cellular automata (CA) substitution. The proposed image encryption method satisfies the properties of confusion and diffusion as the characteristics of SCAN and CA substitution are flexible. The salient features of the proposed image encryption method are lossless, symmetric private key encryption, very large number of secret keys, key-dependent permutation, and key-dependent pixel value replacement. Simulation results obtained using some color and gray-level images clearly demonstrate the strong performance of the proposed SCAN-CA-based image security system.

A novel visual secret sharing scheme for multiple secrets without pixel expansion

The main concept of the original visual secret sharing (VSS) scheme is to encrypt a secret image into n meaningless share images. It cannot leak any information of the shared secret by any combination of the n share images except for all of images. The shared secret image can be revealed by printing the share images on transparencies and stacking the transparencies directly, so that the human visual system can recognize the shared secret image without using any devices. The visual secrets sharing scheme for multiple secrets (called VSSM scheme) is intended to encrypt more than one secret image into the same quantity of share images to increase the encryption capacity compared with the original VSS scheme. However, all presented VSSM schemes utilize a pre-defined pattern book with pixel expansion to encrypt secret images into share images. In general, it leads to at least 2x times pixel expansion on the share images by any one of the VSSM schemes. Thus, the pixel expansion problem becomes more serious for sharing multiple secrets. This is neither a practical nor the best solution for increasing the number of secret sharing images. In this paper, we propose a novel VSSM scheme that can share two binary secret images on two rectangular share images with no pixel expansion. The experimental results show that the proposed approach not only has no pixel expansion, but also has an excellent recovery quality for the secret images. As our best knowledge, this is the first approach that can share multiple visual secret images without pixel expansion.

Image copyright protection with forward error correction

A copyright protection method for digital image with 1/T rate forward error correction (FEC) is proposed in this paper. In this method, the original image is lossless and the watermark is robust to malicious attacks including geometric attacks such as scaling, rotation, cropping, print-photocopy-scan, and scaling-cropping attacks and nongeometric attacks such as low-pass filtering, sharpening, JPEG compression attacks. The watermark logo is fused with noise bits to improve the security, and later XORed with the feature value of the image by 1/T rate FEC. During extraction, the watermark bits are determined by majority voting, and the extraction procedure needs neither the original image nor the watermark logo. Experimental results show that not only the image is lossless but also the proposed method can effectively resist the common malicious attacks. Since the proposed method is based on spatial domain and there is no need to do frequency transform, the embedding and extraction performances are quite improved.

A blind reversible method for watermarking relational databases based on a time-stamping protocol

Highlights? An authentication protocol is designed for a reversible watermarking using time-stamp protocol. ? The prediction-error expansion on integers technique is used to achieve reversibility. ? The watermark is detected successfully even most of watermarked relation tuples are deleted. Digital watermarking technology has been adopted lately as an effective solution to protecting the copyright of digital assets from illicit copying. Reversible watermark, which is also called invertible watermark, or erasable watermark, helps to recover back the original data after the content has been authenticated. Such reversibility is highly desired in some sensitive database applications, e.g. in military and medical data. Permanent distortion is one of the main drawbacks of the entire irreversible relational database watermarking schemes. In this paper, we design an authentication protocol based on an efficient time-stamp protocol, and we propose a blind reversible watermarking method that ensures ownership protection in the field of relational database watermarking. Whereas previous techniques have been mainly concerned with introducing permanent errors into the original data, our approach ensures one hundred percent recovery of the original database relation after the owner-specific watermark has been detected and authenticated. In the proposed watermarking method, we utilize a reversible data-embedding technique called prediction-error expansion on integers to achieve reversibility. The detection of the watermark can be completed successfully even when 95% of a watermarked relation tuples are deleted. Our extensive analysis shows that the proposed scheme is robust against various forms of database attacks, including adding, deleting, shuffling or modifying tuples or attributes.