Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning Large language models (LLMs) to unlock state-of-the-art performance. Fine-tuning approaches proposed in the literature for LLMs on program repair tasks generally overlook the need to reason about the logic behind code changes, beyond syntactic patterns in the data. High-performing fine-tuning experiments also usually come at very high computational costs. With MORepair , we propose a novel perspective on the learning focus of LLM fine-tuning for program repair: we not only adapt the LLM parameters to the syntactic nuances of the task of code transformation (objective ➊), but we also specifically fine-tune the LLM with respect to the logical reason behind the code change in the training data (objective ➋). Such a multi-objective fine-tuning will instruct LLMs to generate high-quality patches. We apply MORepair to fine-tune four open-source LLMs with different sizes and architectures. Experimental results on function-level and repository-level repair benchmarks show that the implemented fine-tuning effectively boosts LLM repair performance by 11.4% to 56.0%. We further show that our fine-tuning strategy yields superior performance compared to the state-of-the-art approaches, including standard fine-tuning, Fine-tune-CoT, and RepairLLaMA.

The inherent constraint of the "human-in-the-loop" sensing mechanism, imposes mobile crowdsensing with high dynamics and uncertainty, ultimately leading to the issue of incomplete data collection. Current data inference solutions in mobile crowdsensing can be broadly categorized as low-rank models and deep learning models. Low-rank models apply structural prior for data inference, but have limited model capacity, while deep learning models possess salient feature expressivity, but are prone to overfitting in sparse crowdsensing scenarios. In this paper, we try to absorb the strengths of both two paradigms, and propose a structural prior guided Transformer, StrucFormer, for crowd-sensing data inference. Specifically, we exploit structural prior of low-rankness to power canonical Transformer from the aspects of input embedding, attention forming and model regularization, which enables the model to precisely capture the spatiotemporal and multi-type data correlations for accurate inference with only sparse observations. Extensive empirical results demonstrate the superiority of StrucFormer in terms of accuracy and generality in heterogeneous urban sensing tasks. The code is available at: https://github.com/CUPK-K/StrucFormer.

As software complexity increases, integrating vulnerability detection becomes essential to ensure the security and integrity of modern systems. Traditional static and dynamic analysis methods face limitations in efficiency and accuracy, particularly for large-scale vulnerability detection, while existing deep learning methods struggle to fully capture structural information and dependencies in code, leading to incomplete identification of vulnerabilities. In this paper, we propose VulEPEDE, an innovative function-level vulnerability detection method. VulEPEDE leverages Program Dependency Graphs (PDG) to represent function code and constructs a Vulnerability Semantic Dependency Graph (VSDG) using slicing techniques, introducing function parameter nodes as slicing candidates to capture more comprehensive vulnerability trigger chains. It integrates two core modules: the Enhanced Positional Encoding (EPE) module and the Dependency Embedding (DE) module. The EPE module combines node attribute encoding with positional encoding using a Transformer and multi-head attention mechanism to capture complex features and contextual semantics of code, while the DE module learns dependency embeddings between code nodes through convolutional neural networks. We evaluate VulEPEDE on three widely used datasets, comparing its performance against state-of-the-art deep learning-based methods. Experimental results demonstrate that VulEPEDE outperforms the best baseline methods by 1.66%, 17.54%, and 28.96% in F1-score across the three datasets, with considerable computational efficiency.

Multi-stage network attack (MSA) is a serious threat to data security. The high-dimensionality of the alert data along with the diverse features, leads to poor detection performance for MSA. Consequently, this paper proposes a multi-stage network attack detection algorithm based on Gaussian mixture hidden Markov model and transfer learning. Firstly, a sequence modeling framework of Gaussian mixture hidden Markov models is proposed. It uses a Gaussian mixture model to cluster high-dimensional alert data and a hidden Markov model to fully consider the temporal structure of MSA, the alert features of each stage, and transitions between stages. Secondly, optimized Baum-Welch and Viterbi algorithms are proposed, combined with the forward-backward algorithm to train the parameter of the Gaussian mixture hidden Markov model and detect the attack sequence of MSA. Finally, an improved transfer learning method is proposed, which addresses the sparsity of labeled data in MSA scenarios, a Kullback-Leibler (KL) divergence value is added as a penalty term to narrow the distribution differences between the source and target domains and solves the bias problem in the transfer learning process. The proposed algorithm is validated on the datasets DARPA 2000 and CSE-CIC-IDS2018, and the effectiveness and superiority is verified on multiple evaluation indicators.Note to Practitioners—Network attacks gradually show the large-scale, coordinated and multi-stage characteristics. Complex multi-step attacks with strong concealment and persistence have become the development trend of network attacks, which seriously threaten and infringe the secure storage and transmission of information. Most existing studies use hidden Markov model (HMM) to model multi-stage network attacks. HMM is usually more suitable for multi-step attacks occurring in a specific sequence within a continuous time interval. However, in actual multi-stage network attacks, attackers do not need to follow the exact sequence of multi-step attacks, and the intervals between successive stages of an attack can be hours, days, or even months. Attackers may also perform interleaved attacks to hide attacks. Therefore, this paper proposes a multi-stage network attack detection algorithm based on Gaussian hybrid hidden Markov and transfer learning. The optimized Gaussian hybrid hidden Markov model is used to model the alert data of multi-stage network attacks, and the improved transfer learning method is adopted to apply the knowledge learned from the source domain to the multi-stage network attack detection model of the target domain. The experimental results show that the proposed algorithm can effectively process the alert data of different attack stages under complex multi-stage network attacks, distinguish the real threat alert, false alert and irrelevant alert, and improve the performance of detecting multi-stage network attacks. The method presented in this paper can provide a valuable solution for complex multi-stage network attack detection such as advanced persistent threat (APT). Future work will further combine adversarial generation network methods to avoid the interference of adversarial attack samples, and explore more ways to improve the performance of multi-step attack detection.

The Hongtu-1 (HT-1) synthetic aperture radar (SAR) system is the first spaceborne single-pass multibaseline (MB) interferometric SAR (InSAR) system based on four HT-1 SAR satellites flying in a cartwheel formation. This setup includes three secondary satellites that function as receive-only units to create a compact multistatic SAR system. The primary objective of the HT-1 mission is to generate a consistent global digital elevation model (DEM) at 1:50000 scale. In addition, the HT-1 mission will feature novel SAR imaging technology demonstrations. On March 30, 2023, four HT-1 satellites were successfully launched and started to provide spaceborne radar data services to users. This article provides a detailed description of the HT-1 MB InSAR system, including the SAR performance, the cartwheel formation designed for multistatic SAR data collection with desired baselines, and the uninterrupted synchronization link. The interferometric performance is thoroughly analyzed. With the recorded data, imaging and interferometric processing procedures are introduced, and the capabilities of single-pass MB InSAR DEM generation are demonstrated. Compared with those of ICESAT, the height errors are less than 2 m in flat terrain and less than 5 m in mountainous terrain. Moreover, the resolution and swath of multisatellite mosaic imaging are 3 m and 80 km, respectively. The repeat-pass differential InSAR measurement for surface deformation monitoring is also included.

Mobile crowdsensing is a promising scheme for performing large-scale urban monitoring, but it always faces the issue of unstable spatiotemporal coverage, which results in the incompletion of data collection. The common solutions for tackling this issue, are to use the existing subset of measurements for inferring the remaining unsensed data by leveraging the latent data correlations. However, existing data inference techniques, both for matrix/tensor factorization based methods and deep learning based methods, cannot well capture the high-order and dynamic data correlations simultaneously under the mobile crowdsensing scheme. In this paper, we propose a novel 3Dimensional (3D) channel attention aided neural tensor completion method, called "3DA-NTC", for more accurate crowdsensing data inference, through leveraging both the multi-dimensional data structure mining ability of tensor factorization as well as the high-order, dynamic correlation learning ability of deep neural network. Specifically, to capture the spatiotemporal and multitype data correlations, we first use a 3D tensor to model the 3Order interaction among crowdsensing data. Then, we combine the traditional inner product based tensor factorization with outer product computing to enhance the modeling of nonlinear data correlations and form an interaction tensor, based on which, we apply a 3D channel attention aided convolutional neural network to further extract the features of high-order and dynamic data interactions for missing value inference. Extensive experiments on two real-world urban sensing datasets, including U-Air and SensorScope, are conducted to evaluate the performance of 3DANTC, and the results demonstrate the superiority of our method compared with the state-of-the-art (SOTA) baselines in missing data recovery.

Vulnerabilities in software and distributed systems are increasing, and system security becomes a challenge for developers. Giving a quick vulnerability classification for newly discovered vulnerabilities is helpful for developers to quickly analyze the vulnerabilities and complete the vulnerability fix. Therefore, Res-CNN-BiTLSTM is proposed for vulnerability classification in this paper. The model consists of Text Convolutional Neural Network (TextCNN), Bidirectional improved gating mechanism (BiTLSTM) and residual blocks. Firstly, TextCNN is employed to capture local features from vulnerability description information, according to different convolutional kernel sizes. Secondly, an improved gating mechanism (TLSTM) is constructed to enhance the expressive power of Long Short-Term Memory (LSTM). BiTLSTM is employed to extract long-text features of vulnerability descriptions through both forward and backward directions. Thirdly, the residual block is used for enhancing and fusing the local features with the long text features to retain important text features. Finally, a fully connected layer is employed to classify vulnerabilities. The results show that Res-CNN-BiTLSTM has the highest macro precision (MacroP), macro recall (MacroR), and macro F1 (MacroF1) on National Vulnerability Database (NVD) dataset. It has a high prediction precision on CWE-352 and CWE-416. The performance of Res-CNN-BiTLSTM is better than the other comparative models. It is effective for vulnerability automatic classification. Meanwhile, the effectiveness of each part of the model is proved by ablation experiments.

In the field of Internet of Things (IoT), the intrusion detection data is scarce because of the network security and privacy. This article proposes a domain adaptive IoT intrusion detection algorithm based on GWR-GCN feature extraction and conditional domain adversary, which aims to improve intrusion detection in the IoT domain by learning from other intrusion detection domains with rich data. First, a GWR-GCN-based domain-invariant feature extraction method is proposed, where the growing when required network (GWR) calculates the correlation between the original data, and the related data is connected into a graph by the Hebb learning principle. The graph convolutional neural network (GCN) is used to mine the feature information of the graph-structured data and extract the optimal domain-invariant features. Second, a Copula-based data distribution alignment method is proposed to decompose the overall feature distribution difference between the source and target domains into the marginal distribution difference of a single feature and the joint distribution difference between features. Meanwhile, the correlation between features on the data distribution is considered to further reduce the data distribution difference and improve the cross-domain ability. Finally, a conditional domain adversarial intrusion detection model is proposed to improve the detection performance by adding the class information as a condition in the discriminator, considering the correlation between features and classes, and reducing the effect of domain shift on distributional alignment. In order to verify the proposed algorithm, experiments are conducted on the traditional network and the IoT domain data sets, and the superiority is verified on multiple evaluation indicators.

Due to well-hidden and stage-triggered properties of second-order SQL injections in web applications, current approaches are ineffective in addressing them and still report high false negatives and false positives. To reduce false results, we propose a Proxy-based static analysis and dynamic execution mechanism towards detecting, locating and preventing second-order SQL injections (SQLPsdem). The static analysis first locates SQL statements in web applications and identifies all data sources and injection points (e.g., Post, Sessions, Database, File names) that injection attacks can exploit. After that, we reconstruct the SQL statements and use attack engines to jointly generate attacks to cover all the state-of-the-art attack patterns so as to exploit these applications. We then use proxy-based dynamic execution to capture the data transmitted between web applications and their databases. The data are the reconstructed SQL statements with variable values from the attack payloads. If a web application is vulnerable, the data will contain malicious attacks on the database. We match the data with rules formulated by attack patterns to detect first and second-order SQL injection vulnerabilities in web applications, particularly the second-order ones. We use a representative and complete coverage of attack patterns and precise matching rules to reduce false results. By escaping and truncating malicious payloads in the data transmitted from the web application to the database, we can eliminate the possible negative impact of the data on the database. In the evaluation, by generating 52,771 SQL injection attacks using four attack generators, SQLPsdem successfully detects 26 second-order (including 13 newly discovered ones) and 375 first-order SQL injection vulnerabilities in 12 open-source web applications. SQLPsdem can also 100% eliminate the malicious impact of the data with negligible overhead.

Feature engineering is a classical problem in recommender systems, and feature interactions is one of the most important parts of feature engineering. Factorization based models are widely used for explicit feature interactions. However, most current works utilize separate features to model cross features. Such a pattern limits the significance of cross features, since realistic recommendation scenarios are rich in associations between features. In this paper, we classify the basic feature interactions into sum-interaction and product-interaction, and improve the current general strategy of explicit feature interactions. Based on these theoretical studies, we propose a novel explicit feature interactions model Attentional Aggregative Interaction Network (AAIN), which models higher-order features using a cyclic explicit module. Specifically, we introduce attention mechanism for the reorganization of separate features, followed by product-interaction and higher-order features’ compression and output. The model is efficient since: 1) AAIN automatically learns high-order feature interactions and filters them with different weights. 2) AAIN optimizes the interaction between features into the interaction between feature groups, which allows for other relevant information to be considered when performing interactions. Furthermore, we integrate AAIN model with the classical deep neural network (DNN) model into a new model Deep Attentional Aggregative Interaction Network (DAAIN). Experiments on real-world datasets show that our models achieve state-of-the-art results.

Access control vulnerabilities that lead to elevated privileges are among the most dangerous vulnerabilities in Web applications. Most of the existing detection methods use dynamic or static analysis techniques alone, which suffer from high manual involvement, low automation, high leakage rate, low page coverage, and other deficiencies. To this end, this paper proposes a novel access control vulnerability detection method (DetAC) based on a sitemap model with global information representation. This method first constructs a static site-wide sitemap model based on the page link addresses in the Web application source code through static analysis techniques. After that, the application is logged in and executed dynamically with different role users. During this process, execution traces and request parameters are collected and converted into annotations to fill the corresponding edges of the static site-wide sitemap model. Then, the sitemap model with global information representation is obtained. This model can represent both the global control flow and data flow of the application. Then DetAC analyzes the role-based and user-based access control policies of the Web application based on the node reachability and annotated data features of the model. And according to the information such as role, user, and access resources, it generates attack vectors to achieve different roles and the same role of different users to access each other’s resources. Finally, access control vulnerabilities are detected based on the equivalence of the results obtained using attack vector access and normal access to the Web application server. DetAC was validated on five real open-source Web applications, and the results showed that DetAC successfully detected up to 12 access control vulnerabilities, which are more than those of the traditional seven tools. The dynamic analysis page coverage rate was significantly improved during the detection process, reaching an average of 91.37%.

Aiming at the limitations of existing algorithms of network intrusion detection in dealing with complex data of imbalance and high dimensionality, this paper proposes an intrusion detection algorithm based on convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM). First, the data-type conversion, oversampling technology and image data conversion are included in the data preprocessing to make the data balanced and adapt to the input format. Then, by the convolutional layer, pooling layer and fully connected layer of the CNN model, the main features are abstracted from the converted image data. Finally, data of the main features is used for training and testing the LightGBM model, so as to get the final classification results. This paper uses KDDCUP99 dataset to carry out multi-classification experiments. By comparing the experiments before and after balancing the dataset, and comparing with similar algorithms, it verifies the superiority of the proposed algorithm in the classification performance of intrusion detection, especially for the minority attack classes.

Software vulnerabilities are one of the roots of computer security problems. The traditional static analysis and dynamic analysis methods based on software source code mainly have some deficiencies, such as high false positive rate, high false negative rate and insufficient semantic information captured. Nevertheless, the application of machine learning, Natural Language Processing and other technologies in software vulnerability prediction can effectively mitigate such issues. This paper proposed a vulnerability prediction method based on multiple-level N-gram feature extraction and heterogeneous ensemble learning. First, by code intermediate representation and constructing a multiple-level N-gram feature generation model, two kinds of N-gram semantic features with different window size and different granularity at word and char level were extracted to retain the semantic and structural information of code. Second, TF–IDF was used to construct the vector space model as the input of prediction model. As a single classifier was prone to overfitting and poor generalization, this paper conducted benchmark testing on five classical machine learning algorithms (NB, SVM, DT, LR, RF), and then combined four (SVM, DT, LR, RF) among them, which had better performance as the base classifiers to form the stacking heterogeneous ensemble method to build the vulnerability prediction model. Finally, the proposed method was verified on buffer overflow vulnerability and resource management vulnerability datasets, with a lowest false positive rate and false negative rate which can reach 1.58% and 4.06%, respectively.

Because of the sequence and number of calls of functions, software network cannot reflect the real execution of software. Thus, to detect crucial functions (DCF) based on software network is controversial. To address this issue, from the viewpoint of software dynamic execution, a novel approach to DCF is proposed in this paper. It firstly models, the dynamic execution process as an execution sequence by taking functions as nodes and tracing the stack changes occurring. Second, an algorithm for deleting repetitive patterns is designed to simplify execution sequence and construct software sequence pattern sets. Third, the crucial function detection algorithm is presented to identify the distribution law of the numbers of patterns at different levels and rank those functions so as to generate a decision-function-ranking-list (DFRL) by occurrence times. Finally, top-k discriminative functions in DFRL are chosen as crucial functions, and similarity the index of decision function sets is set up. Comparing with the results from Degree Centrality Ranking and Betweenness Centrality Ranking approaches, our approach can increase the node coverage to 80%, which is proven to be an effective and accurate one by combining advantages of the two classic algorithms in the experiments of different test cases on four open source software. The monitoring and protection on crucial functions can help increase the efficiency of software testing, strength software reliability and reduce software costs.

In a software network system, it is of great significance to identify key functions for software fault detection and maintenance. In order to better understand the characteristics and internal structure of software, a key Node Discovery algorithm based on Evidence Theory called NDET is proposed in this paper. First, the software complex network model is constructed according to the execution process of the software. Based on the Dempster-Shafer evidence theory (D-S evidence theory), the discernment frame is formed, the maximum and minimum values of the network degree and strength are determined. Second, the Basic Probability Assignment (BPA) of each node degree is calculated by considering the node degree distribution ratio value. Third, based on Dempster’s rule of combination, the evidential centrality of the node itself and the fluctuation value of the node influenced by neighbor nodes are considered for the key measurement. Finally, by using the Susceptible–Infected–Recovered (SIR) model to simulate the spreading process on real software networks, the performance of NDET is evaluated. Experiment results verify the validity and accuracy of NDET for identifying key function nodes in software.

Intrusion detection system (IDS) can effectively identify anomaly behaviors in the network; however, it still has low detection rate and high false alarm rate especially for anomalies with fewer records. In this paper, we propose an effective IDS by using hybrid data optimization which consists of two parts: data sampling and feature selection, called DO_IDS. In data sampling, the Isolation Forest (iForest) is used to eliminate outliers, genetic algorithm (GA) to optimize the sampling ratio, and the Random Forest (RF) classifier as the evaluation criteria to obtain the optimal training dataset. In feature selection, GA and RF are used again to obtain the optimal feature subset. Finally, an intrusion detection system based on RF is built using the optimal training dataset obtained by data sampling and the features selected by feature selection. The experiment will be carried out on the UNSW-NB15 dataset. Compared with other algorithms, the model has obvious advantages in detecting rare anomaly behaviors.

Buffer overflow vulnerability is the most common and serious type of vulnerability in software today, as network security issues have become increasingly critical. To alleviate the security threat, many vulnerability mining methods based on static and dynamic analysis have been developed. However, the current analysis methods have problems regarding high computational time, low test efficiency, low accuracy, and low versatility. This paper proposed a software buffer overflow vulnerability prediction method by using software metrics and a decision tree algorithm. First, the software metrics were extracted from the software source code, and data from the dynamic data stream at the functional level was extracted by a data mining method. Second, a model based on a decision tree algorithm was constructed to measure multiple types of buffer overflow vulnerabilities at the functional level. Finally, the experimental results showed that our method ran in less time than SVM, Bayes, adaboost, and random forest algorithms and achieved 82.53% and 87.51% accuracy in two different data sets. The method presented in this paper achieved the effect of accurately predicting software buffer overflow vulnerabilities in C/C++ and Java programs.

Interactive software can run not only independently but also often collaboratively to perform tasks thus forming a larger group of software networks. Hence the analysis of interactions is essential as a way to measure the stability of the entire software group network, i.e. the interactive patterns and frequency. However, current studies rarely investigate the performance of software as groups but as individuals thus omitting their interactions. Especially, the performance of some traditional measurement algorithms which execute in nondistributed runtime environments is poor. In this paper, we proposed a new software group stability model concentrating on software network level behaviors as a group. An algorithm is proposed to extract key nodes and critical interactive items based on frequent interaction pattern, then the stability of software group can be assessed based on the loss of connectivity caused by removing key nodes and key edges from the network, using the algorithm SG-StaMea. Furthermore, our algorithms can quantify the stability. To validate the efficacy of our model, the Spark and Hadoop platforms have been selected as targets systems. Both experiments and experimental data showed that our algorithms have significantly improved the accuracy of software stability measurement compared to classical algorithm such as Apriori of frequent pattern.

The scale and complexity of software systems are constantly increasing, imposing new challenges for software fault location and daily maintenance. In this paper, the Security Feature measurement algorithm of Frequent dynamic execution Paths in Software, SFFPS, is proposed to provide a basis for improving the security and reliability of software. First, the dynamic execution of a complex software system is mapped onto a complex network model and sequence model. This, combined with the invocation and dependency relationships between function nodes, fault cumulative effect, and spread effect, can be analyzed. The function node security features of the software complex network are defined and measured according to the degree distribution and global step attenuation factor. Finally, frequent software execution paths are mined and weighted, and security metrics of the frequent paths are obtained and sorted. The experimental results show that SFFPS has good time performance and scalability, and the security features of the important paths in the software can be effectively measured. This study provides a guide for the research of defect propagation, software reliability, and software integration testing.

Intrusion detection refers to monitoring network data information, quickly detecting intrusion behavior, can avoid the harm caused by intrusion to a certain extent. Traditional intrusion detection methods are mainly focused on rule files and data mining. They have the disadvantage of not being able to detect new types of attacks and have the slow detection speed. To address these issues, an intrusion detection method based on improved PCA combined with Gaussian Naive Bayes was proposed. By weighting the first few feature vectors of the traditional PCA, data pollution can be reduced. The number of final weighted principal components is 2 through sequential selection. The dimensionality reduction of the data is achieved through improved PCA. Finally, the intrusion behaviors were detected by using the Gaussian Naive Bayes classifier. The indexes of detection accuracy, detection time, precision rate, and recall rate were applied to evaluate the results. The experimental results show that, comparing with the traditional Bayes method, the method proposed in this article can reduce the detection time by 60%, shorten it to 0.5s, and increase the detection rate to 91.06%. The mean value of detection accuracy is about 86% by cross-validation.

Frequent pattern mining can well extract insight from transaction patterns, and it is a desired capability for fully understanding the customer's purchase behavior. However, most of the algorithms are focus on the transverse relationship and the longitudinal analysis is missed. To address this defect, FP-ICA, a Frequent Pattern mining algorithm for Item-oriented and Customer-oriented Analysis is proposed. A pattern with its items occur in the same transaction is item-oriented, and a pattern with its items occur cross several transactions of a customer is customer-oriented. FP-ICA transforms the transactions to a bitmap which contains a header for recording customer information, and the frequent patterns are obtained by logic And-operation. Different mining rules are used for item-oriented and customer-oriented discovery. Experiments are conducted to demonstrate the fast speed achievement and good scalability of FP-ICA.

Frequent intra-sequence pattern mining and inter-sequence pattern mining are both important ways of association rule mining for different applications. However, most algorithms focus on just one of them, as attempting both is usually inefficient. To address this deficiency, FIIP-BM, a Frequent Intra-sequence and Inter-sequence Pattern mining algorithm using Bitmap with a maxSpan is proposed. FIIP-BM transforms each transaction to a bit vector, adjusts the maximal span according to user's demand and obtains the frequent sequences by logic And-operation. For candidate 2-pattern generation, the subscripts of the joining items should be checked first; the bit vector of the joining item will be left-shifted before calculation if the subscript is not 0. Left alignment rule is used for different bit vector length problems. FIIP-BM can mine both intra-sequence and inter-sequence patterns. Experiments are conducted to demonstrate the computational speed and memory efficiency of the FIIP-BM algorithm.

The stability analysis and control design problem of Takagi-Sugeno (T-S) fuzzy time-delay systems under mismached premise membership functions are investigated in this paper. A membership function dependent stability criterion is derived first based on Lyapunov stability analysis method. As the information of the membership functions is included in the derived criterion, it is less conservtive than those of independent ones. Then, a fuzzy controller is derived to stabilize the corresponding closed-loop system. All the conditions in this paper are described in linear matrix inequalities (LMIs) frame that can be handled numerically. Finally, some numerical examples are presented to indicate the effectiveness of our approach.

Although frequent traversal sequence (FTS) mining has been extensively studied over the last decade in Web usage mining, it is challenging to extend the mining technique to dynamic Web click streams. The main challenge is that existing false-positive methods control memory consumption and output accuracy by a relaxation ratio r (r = e/s, e is the error parameter, and s is the specified minimum support). However, the higher the value of r, the more saving is the memory consumption and the better recall but degrades the output precision, while on the contrary, decreasing r gives a more precise output but needs higher storage space. In this paper, the upper and lower bounds are established to constrain r, a weighted harmonic average (WHA) of the two bounds is designed to adjust r, and a novel algorithm FTS-Stream is proposed to find the FTS over a time-sensitive sliding window. Thus, the precision and recall can be maintained with the WHA (r). Our analysis and experiments show that FTS-Stream has high accuracy and requires less memory in dynamic Web clickstreams

An important application of sequential mining technique is frequent traversal sequence (FTS) mining. However, the Web data grows quickly, some data may be outdated, and previous FTS may be changed when the database is updated. We have to re-mine FTS from the updated database, but re-finding FTS consume too much execution time. In this paper, a novel structure, IE-LATTICE (improved extended lattice) is designed to store the previous FTS. An efficient algorithm based on bidirectional constraint, IMFTS (incremental mining frequent traversal sequence) is proposed, which utilizes the previous mining results and constraint strategy to discover the new FTS just from the added and deleted part of the database. Experimental results show that IMFTS algorithm efficiently reduces the execution time for mining FTS