Bao Huynh

Parallel Frequent Subgraph Mining on Multi-Core Processor Systems

ICIC Express Letters, 2016

MicroRNA‐16 regulates ENaC expression in alveolar epithelial cells

The FASEB Journal, 2012

Deletion of NALP3 protects against hyperoxia‐induced acute lung injury

The FASEB Journal, 2012

ABSTRACT

Deletion of NALP3 protects against hyperoxia‐induced acute lung injury

The FASEB Journal, 2012

ABSTRACT

Hyperoxia induced lung injury is associated with alterations in circadian clock genes in mice

The FASEB Journal, 2013

A tanulmányt a szerző saját indíttatásból írta, és ezzel a Magyar Nemzeti Bankban semmilyen dönté... more

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A Novel Approach for Mining Closed Clickstream Patterns

Cybernetics and Systems, 2021

Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining ... more Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining because they produce more compact representations. Additionally, the runtime and memory usage required for mining CSPs is much lower than the sequential pattern mining. This task has fascinated numerous researchers. In this study, we propose a novel approach for closed clickstream pattern mining using C-List (CCPC) data structure. Closed clickstream pattern mining is a more specific task of CSP mining that has been lacking in research investment; nevertheless, it has promising applications in various fields. CCPC consists of two key steps: It initially builds the SPPC-tree and the C-List for each frequent 1-pattern and then determines all frequently closed clickstream 1-patterns; next, it constructs the C-List for each frequent k-pattern and mines the remaining frequently closed k-patterns. The proposed method is optimized by modifying the SPPC-tree structure and a new property is added ...

A Novel Approach for Mining Closed Clickstream Patterns

Cybernetics and Systems, 2021

Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining ... more Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining because they produce more compact representations. Additionally, the runtime and memory usage required for mining CSPs is much lower than the sequential pattern mining. This task has fascinated numerous researchers. In this study, we propose a novel approach for closed clickstream pattern mining using C-List (CCPC) data structure. Closed clickstream pattern mining is a more specific task of CSP mining that has been lacking in research investment; nevertheless, it has promising applications in various fields. CCPC consists of two key steps: It initially builds the SPPC-tree and the C-List for each frequent 1-pattern and then determines all frequently closed clickstream 1-patterns; next, it constructs the C-List for each frequent k-pattern and mines the remaining frequently closed k-patterns. The proposed method is optimized by modifying the SPPC-tree structure and a new property is added ...

Efficient Method for Mining High-Utility Itemsets Using High-Average Utility Measure

Detecting Malicious Fast-Flux Domains Using Feature-based Classification Techniques

Journal of Internet Technology, 2020

In recent years, new generation botnets tend to use an evasion technique based on Domain Name Sys... more In recent years, new generation botnets tend to use an evasion technique based on Domain Name System (DNS) called Fast-Flux Service Network (FFSN) to hide the actual location of their malicious servers. Detection of FFSN continues to be a challenging issue because of the similar behavior between FFSN and other legitimate infrastructures, such as Content Delivery Networks (CDNs) and Round Robin Domain Name System (RRDNS). In this paper, we present a novel approach based on analyzing the passive DNS traffic traces to detect malicious FFSNs. By analyzing DNS traces, we extracted ten key features and employed on the popular machine learning algorithms to build classifiers aim to classify a domain as either malicious flux service or legitimate. The seven among the ten features are first introduced in this study. The effectiveness of selected features is illustrated by comparing the distribution of 95% confidence interval for the mean and standard errors between legit, malware and fast-fl...

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Detecting Malicious Fast-Flux Domains Using Feature-based Classification Techniques

Journal of Internet Technology, 2020

In recent years, new generation botnets tend to use an evasion technique based on Domain Name Sys... more In recent years, new generation botnets tend to use an evasion technique based on Domain Name System (DNS) called Fast-Flux Service Network (FFSN) to hide the actual location of their malicious servers. Detection of FFSN continues to be a challenging issue because of the similar behavior between FFSN and other legitimate infrastructures, such as Content Delivery Networks (CDNs) and Round Robin Domain Name System (RRDNS). In this paper, we present a novel approach based on analyzing the passive DNS traffic traces to detect malicious FFSNs. By analyzing DNS traces, we extracted ten key features and employed on the popular machine learning algorithms to build classifiers aim to classify a domain as either malicious flux service or legitimate. The seven among the ten features are first introduced in this study. The effectiveness of selected features is illustrated by comparing the distribution of 95% confidence interval for the mean and standard errors between legit, malware and fast-fl...

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Efficient Methods for Clickstream Pattern Mining on Incremental Databases

IEEE Access, 2021

Sequence pattern mining is a core task of data mining in various fields. Clickstream pattern mini... more Sequence pattern mining is a core task of data mining in various fields. Clickstream pattern mining, a variant of sequence pattern mining, is very useful in e-commerce, and is used to analyze, evaluate and predict online customer behaviors, attracting the interest of many researchers. Clickstream pattern mining has become important due to its wide range of applications. However, most previous approaches are not suitable to apply when clickstreams are inserted into a database, because this task is too time consuming. The challenge of this problem is how to find a solution to minimize the runtimes and reduce the number of times the original databases are scanned to reduce the computational cost in the mining process on incremental databases. In this paper, we propose two effective methods for mining clickstream patterns from incremental databases, named inCMUB and Eff-inCMUB, based on the pre-large concept. inCMUB inserts new clickstreams from an inserted database into the existing tree and mines all frequent clickstream patterns, while Eff-inCMUB is a new approach, and builds a new tree from the inserted database to find pre-large 1-patterns and then it updates the pre-large clickstream patterns mined from the original database to extract frequent clickstream patterns. The experiments show that our proposed methods outperform the SMUB algorithm in terms of runtimes, memory usage and scalability on real-word clickstream databases. INDEX TERMS Incremental database, clickstream pattern, sequence pattern mining, SPPC-tree.

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Efficient Methods for Clickstream Pattern Mining on Incremental Databases

IEEE Access, 2021

Sequence pattern mining is a core task of data mining in various fields. Clickstream pattern mini... more Sequence pattern mining is a core task of data mining in various fields. Clickstream pattern mining, a variant of sequence pattern mining, is very useful in e-commerce, and is used to analyze, evaluate and predict online customer behaviors, attracting the interest of many researchers. Clickstream pattern mining has become important due to its wide range of applications. However, most previous approaches are not suitable to apply when clickstreams are inserted into a database, because this task is too time consuming. The challenge of this problem is how to find a solution to minimize the runtimes and reduce the number of times the original databases are scanned to reduce the computational cost in the mining process on incremental databases. In this paper, we propose two effective methods for mining clickstream patterns from incremental databases, named inCMUB and Eff-inCMUB, based on the pre-large concept. inCMUB inserts new clickstreams from an inserted database into the existing tree and mines all frequent clickstream patterns, while Eff-inCMUB is a new approach, and builds a new tree from the inserted database to find pre-large 1-patterns and then it updates the pre-large clickstream patterns mined from the original database to extract frequent clickstream patterns. The experiments show that our proposed methods outperform the SMUB algorithm in terms of runtimes, memory usage and scalability on real-word clickstream databases. INDEX TERMS Incremental database, clickstream pattern, sequence pattern mining, SPPC-tree.

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An Efficient Method for Mining Clickstream Patterns

Recently, hybrid approaches, which combine an FP-tree-like data structure with an interaction-bas... more Recently, hybrid approaches, which combine an FP-tree-like data structure with an interaction-based approach, are efficient approaches for mining frequent itemsets. However, applying those approaches for sequential pattern mining arose some challenges. In this paper, we introduce a hybrid approach for a specific version of sequential pattern mining, clickstream pattern mining, with our proposed B-List structure and SMUB algorithm. The SMUB algorithm exploited the B-List structure that is generated from the SPPC tree and the B-List intersection are used to discover all sequential patterns in the given sequence database. Via our experiments on various databases, SMUB has been shown to be more efficient than the current state-of-the-art algorithm, CM-Spade, in terms of runtime, and scalability, especially on huge databases with very small thresholds.

An Efficient Method for Mining Clickstream Patterns

Recently, hybrid approaches, which combine an FP-tree-like data structure with an interaction-bas... more Recently, hybrid approaches, which combine an FP-tree-like data structure with an interaction-based approach, are efficient approaches for mining frequent itemsets. However, applying those approaches for sequential pattern mining arose some challenges. In this paper, we introduce a hybrid approach for a specific version of sequential pattern mining, clickstream pattern mining, with our proposed B-List structure and SMUB algorithm. The SMUB algorithm exploited the B-List structure that is generated from the SPPC tree and the B-List intersection are used to discover all sequential patterns in the given sequence database. Via our experiments on various databases, SMUB has been shown to be more efficient than the current state-of-the-art algorithm, CM-Spade, in terms of runtime, and scalability, especially on huge databases with very small thresholds.

RENT: A Python Package for Repeated Elastic Net Feature Selection

Journal of Open Source Software, 2021

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RENT: A Python Package for Repeated Elastic Net Feature Selection

Journal of Open Source Software, 2021

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Constraint-Based Method for Mining Colossal Patterns in High Dimensional Databases

Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017, 2017

Constraint-based methods for mining patterns have been developed in recent years. They are based ... more Constraint-based methods for mining patterns have been developed in recent years. They are based on top-down manner to prune candidate patterns. However, for colossal pattern mining, bottom-up manners are efficient methods, so the previous approaches for pruning candidate patterns based on top-down manner cannot apply to colossal pattern mining with constraint when using bottom-up manner. In this paper, we state the problem of mining colossal pattern with pattern constraints. Next, we develop a theorem for efficient pruning candidate patterns with bottom-up manner. Finally, we propose an efficient algorithm for mining colossal patterns with pattern constraints based on this theorem.

Constraint-Based Method for Mining Colossal Patterns in High Dimensional Databases

Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017, 2017

Constraint-based methods for mining patterns have been developed in recent years. They are based ... more Constraint-based methods for mining patterns have been developed in recent years. They are based on top-down manner to prune candidate patterns. However, for colossal pattern mining, bottom-up manners are efficient methods, so the previous approaches for pruning candidate patterns based on top-down manner cannot apply to colossal pattern mining with constraint when using bottom-up manner. In this paper, we state the problem of mining colossal pattern with pattern constraints. Next, we develop a theorem for efficient pruning candidate patterns with bottom-up manner. Finally, we propose an efficient algorithm for mining colossal patterns with pattern constraints based on this theorem.

A New Strong Adversary Model for RFID Authentication Protocols

IEEE Access, 2020

Radio Frequency Identification (RFID) systems represent a key technology for ubiquitous computing... more Radio Frequency Identification (RFID) systems represent a key technology for ubiquitous computing and for the deployment of the Internet of Things (IoT). In RFID technology, authentication protocols are often necessary in order to confirm the identity of the parties involved (i.e. RFID readers, RFID tags and/or database servers). In this article, we analyze the security of a mutual authentication protocol proposed by Wang and Ma. Our security analysis clearly shows major security pitfalls in this protocol. Firstly, we show two approaches that an adversary may use to mislead an honest reader into thinking that it is communicating with a legitimate database. Secondly, we show how an adversary that has compromised some tags can impersonate an RFID reader to a legitimate database. Furthermore, we present a new adversary model, which pays heed on cases missed by previous proposals. In contrast to previous models where the communication between an RFID reader and a back-end server is through a secure channel, our model facilitates the security analysis of more general schemes where this communication channel (RFID reader-to-server) is insecure. This model determines whether the compromise of RFID tags has any impact on the security of the readerto-server communication or vice versa. In a secure protocol, the possible compromise of RFID tags should not affect the RFID reader-server communication. In this paper, we show that compromising of RFID tags in Wang and Ma protocol has a direct impact on the reader-server security. Finally, we propose a new authentication protocol that offers an adequate security level and is resistant against the mentioned security risks. The security proofs of the proposed protocol are supported with Gong-Needham-Yahalom (GNY) logic and Scyther tool, which are formal methods to evaluate the security of a cryptographic protocol.

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