ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

The explosive growth in volume, velocity, and diversity of data produced by mobile devices and cloud applications has contributed to the abundance of data or `big data.' Available solutions for efficient data storage and management cannot fulfill the needs of such heterogeneous data where the amount of data is continuously increasing. For efficient retrieval and management, existing indexing solutions become inefficient with the rapidly growing index size and seek time and an optimized index scheme is required for big data. Regarding real-world applications, the indexing issue with big data in cloud computing is widespread in healthcare, enterprises, scientific experiments, and social networks. To date, diverse soft computing, machine learning, and other techniques in terms of artificial intelligence have been utilized to satisfy the indexing requirements, yet in the literature, there is no reported state-of-the-art survey investigating the performance and consequences of techniques for solving indexing in big data issues as they enter cloud computing. The objective of this paper is to investigate and examine the existing indexing techniques for big data. Taxonomy of indexing techniques is developed to provide insight to enable researchers understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for BD-MCC. In this study, 48 indexing techniques have been studied and compared based on 60 articles related to the topic. The indexing techniques' performance is analyzed based on their characteristics and big data indexing requirements. The main contribution of this study is taxonomy of categorized indexing techniques based on their method. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. In addition, the significance of different procedures and performance is analyzed, besides limitations of each technique. In conclusion, several key future research topics with potential to accelerate the progress and deployment of artificial intelligence-based cooperative indexing in BD-MCC are elaborated on.

A survey on indexing techniques for big data: taxonomy and performance evaluation

Because of the increase flow of network traffic and its significance to the provision of ubiquitous services, cyberattacks attempt to compromise the security principles of confidentiality, integrity and availability. A Network Intrusion Detection System (NIDS) monitors and detects cyber-attack patterns over networking environments. Network packets consist of a wide variety of features which negatively affects detection of anomalies. These features include some irrelevant or redundant features which reduce the efficiency of detecting attacks, and increase False Alarm Rate (FAR). In this paper, the feature characteristics of the UNSW-NB15 and KDD99 datasets are examined, and the features of the UNSW-NB15 are replicated to the KDD99 data set to measure their effeciency. We apply An Association Rule Mining algorithm as feature selection to generate the strongest features from the two data sets. Some existing classifiers are utilised to evaluate the complexity in terms of accuracy and FAR. The experimental results show that, the original KDD99 attributes are less efficient than the replicated UNSW-NB15 attributes of the KDD99 data set. However, comparing the two data sets, the accuracy of the KDD99 dataset is better than the UNSW-NB 15 dataset, and the FAR of the KDD99 dataset is lower the UNSWNB 15 dataset.

The Significant Features of the UNSW-NB15 and the KDD99 Data Sets for Network Intrusion Detection Systems

A Review on Remote Data Auditing in Single Cloud Server: Taxonomy and Open Issues

EFM: Enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism

An efficient and secure approach for a cloud collaborative editing

A good feature selection policy which can choose significant and as less as possible features plays a key role for any successful NIDS. The paper presents a genetic algorithm combined with kNN (k-Nearest Neighbor) for feature weighting. We weight all initial 35 features in the training phase and then select tops of them to implement a NIDS for testing. Many DoS/DDoS attacks are applied to evaluate the system. For known attacks we can get the best 97.42% overall accuracy rate while only the top 19 features are considered; as for unknown attacks, we can get the best 78% overall accuracy rate by top 28 features.

Feature Weighting and Selection for a Real-Time Network Intrusion Detection System Based on GA with KNN

The fuzzy association rule has been proven to be effective to present userspsila network behavior offline from a huge amount of collected packets. However, not only effectiveness, efficiency is important as well for Network Intrusion Detection Systems (NIDSs). None of those proposed NIDSs subject to fuzzy association rule can meet the real-time requirement because they all applied static mining approach. In the paper, we propose a real-time NIDS by incremental mining for fuzzy association rules. By consistently comparing the two rule sets, one mined from online packets and the other mined from training attack free packets, our system can make a decision per time unit, 2 seconds in the paper. Experiments have been done to demonstrate its excellent effectiveness and efficiency of the system.

A real-time network intrusion detection system based on incremental mining approach

This paper deals with the problem of collaboratively secure document editing in cloud environment. Using cloud document editing service, several users can create and edit a document collaboratively. However, as the cloud service provider is not trusted to guarantee the confidentiality of these documents, existing methods encrypt the documents before uploading them to the cloud. While these approaches can be inefficient for collaborative editing, we propose to organize the content of a document using a Red-Black tree and encrypt each block of data separately, so as to improve the performance of collaborative editing systems. Although creating and maintaining the Red-Black tree introduces extra cost, compared to the whole document encryption strategy the experimental results show that for text editing operations, such as insertion and removal, the use of Red-Black tree algorithm improves efficiency by 31.04% if 3DES encryption is applied and by 23.94% if applying AES encryption.

A study on the data privacy and operation performance for cloud collaborative editing systems

In the paper, we propose a fast algorithm to generate fuzzy association rules by incremental mining approach, for which the transactions or data records are online instantly collected from live packets. That is, as one data record is collected online, the latest fuzzy rules can be obtained immediately. According to our simulation, in case of the number of features do not excess twenty, mining process can be completed from several milliseconds to seconds depending on the number of features.

Using Incremental Mining to Generate Fuzzy Rules for Real-Time Network Intrusion Detection Systems

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