Jiankun Hu

A Fingerprint Orientation Model Based on 2D Fourier Expansion (FOMFE) and Its Application to Singular-Point Detection and Fingerprint Indexing

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

A simple and efficient hidden Markov model scheme for host-based anomaly intrusion detection

Extensive research activities have been observed on network-based intrusion detection systems (IDSs). However, there are always some attacks that penetrate traffic-profiling-based network IDSs. These attacks often cause very serious damages such as modifying host critical files. A host-based anomaly IDS is an effective complement to the network IDS in addressing this issue. This article proposes a simple data preprocessing approach to speed up a hidden Markov model (HMM) training for system-call-based anomaly intrusion detection. Experiments based on a public database demonstrate that this data preprocessing approach can reduce training time by up to 50 percent with unnoticeable intrusion detection performance degradation, compared to a conventional batch HMM training scheme. More than 58 percent data reduction has been observed compared to our prior incremental HMM training scheme. Although this maximum gain incurs more degradation of false alarm rate performance, the resulting performance is still reasonable.

A survey of network anomaly detection techniques

Information and Communication Technology (ICT) has a great impact on social wellbeing, economic growth and national security in todays world. Generally, ICT includes computers, mobile communication devices and networks. ICT is also embraced by a group of people with malicious intent, also known as network intruders, cyber criminals, etc. Confronting these detrimental cyber activities is one of the international priorities and important research area. Anomaly detection is an important data analysis task which is useful for identifying the network intrusions. This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering. The paper also discusses research challenges with the datasets used for network intrusion detection. HighlightsMaps different types of anomalies with network attacks.Provides an up-to-date taxonomy of network anomaly detection.Evaluates effectiveness of different categories of techniques.Explores recent research related to publicly available network intrusion evaluation datasets.

Mechanical strain induces osteogenic differentiation: Cbfa1 and Ets-1 expression in stretched rat mesenchymal stem cells

Distraction osteogenesis is an active process of bone regeneration under controlled mechanical stimulation. Osteogenic differentiation of mesenchymal stem cells (MSCs) is essential for bone formation during this process. Cbfa1 and Ets-1 (core binding factor alpha 1 and v-ets erythroblastosis virus E26 oncogene homolog 1) are transcription factors that play important roles in the differentiation of MSCs to osteoblasts. In order to mimic a single activation of a clinical distraction device, a short period of cyclic mechanical strain (40 min and 2,000 microstrains) was applied to rat MSCs. Cellular proliferation and alkaline phosphatase (ALP) activity were examined. The mRNA expression of Cbfa1 and Ets-1, as well as ALP, a specific osteoblast marker, was detected using real-time quantitative reverse transcription polymerase chain reaction. The results showed that mechanical strain can promote MSC proliferation, increase ALP activity and up-regulate the expression of Cbfa1 and Ets-1. A significant increase in Ets-1 expression was detected immediately after mechanical stimulation, but Cbfa1 expression was elevated later. The temporal expression pattern of ALP coincided perfectly with that of Cbfa1. Mechanical strain may act as a stimulator to induce differentiation of mesenchymal stem cells into osteoblasts, which is vital for bone formation in distraction osteogenesis.

Pair-polar coordinate-based cancelable fingerprint templates

Fingerprint-based authentication has been widely implemented, however, security and privacy of fingerprint templates still remain an issue. Some schemes have been proposed to protect fingerprint templates, such as the design of cancelable fingerprint templates. Yet, most of the existing schemes rely on accurate fingerprint image registration, which is very hard to achieve, especially considering the need to avoid storing any information related to the raw fingerprint features. In this paper, a pair-polar coordinate-based template design method is developed which does not need registration. The proposed scheme explores the relative relationship of minutiae in a rotation- and shift-free pair-polar framework. A many-to-one mapping is applied to ensure the non-invertible recovery of raw templates. A random translation parameter is introduced to further distort the minutia distribution. Under various scenarios, the proposed scheme is evaluated using the public databases, FVC2002DB1, FVC2002DB2 and FVC2002DB3. The experiment results show that the new method satisfies the template protection requirements and the performance degradation caused by the transformation is very low.

A hybrid public key infrastructure solution (HPKI) for HIPAA privacy/security regulations

The Health Insurance Portability and Accountability 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.

A program-based anomaly intrusion detection scheme using multiple detection engines and fuzzy inference

In this paper, a hybrid anomaly intrusion detection scheme using program system calls is proposed. In this scheme, a hidden Markov model (HMM) detection engine and a normal database detection engine have been combined to utilise their respective advantages. A fuzzy-based inference mechanism is used to infer a soft boundary between anomalous and normal behaviour, which is otherwise very difficult to determine when they overlap or are very close. To address the challenging issue of high cost in HMM training, an incremental HMM training with optimal initialization of HMM parameters is suggested. Experimental results show that the proposed fuzzy-based detection scheme can reduce false positive alarms by 48%, compared to the single normal database detection scheme. Our HMM incremental training with the optimal initialization produced a significant improvement in terms of training time and storage as well. The HMM training time was reduced by four times and the memory requirement was also reduced significantly.

Global Ridge Orientation Modeling for Partial Fingerprint Identification

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. First, 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.

A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns

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.

Alignment-free cancelable fingerprint template design: A densely infinite-to-one mapping (DITOM) approach

Registration-based cancelable template schemes rely on accurate fingerprint image alignment, which is very difficult to achieve. In this paper, by exploiting pair-minutiae vectors, we develop a lightweight, alignment-free scheme for generating cancelable fingerprint templates. The proposed mathematical model is based on a densely infinite-to-one mapping (DITOM) aiming to achieve the non-invertible property. The transformation designed describes the intersection of a collection of hyperplanes and effectively realizes infinite-to-one mapping. The proposed scheme has the properties of non-invertibility, revocability and multiple template independence. Evaluation of the proposed scheme over FVC2002 DB1, DB2 and DB3 shows that the new method exhibits satisfactory performance compared to existing methods.