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The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. Most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR's performance., Comment: Information Systems Frontiers, 2022

Users of social networks tend to post and share content with little restraint. Hence, rumors and fake news can quickly spread on a huge scale. This may pose a threat to the credibility of social media and can cause serious consequences in real life. Therefore, the task of rumor detection and verification has become extremely important. Assessing the veracity of a social media message (e.g., by fact checkers) involves analyzing the text of the message, its context and any multimedia attachment. This is a very time-consuming task that can be much helped by machine learning. In the literature, most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. In this paper, we second the hypothesis that exploiting all of the components of a social media post enhances the accuracy of veracity detection. To further the state of the art, we first propose using a set of advanced image features that are inspired from the field of image quality assessment, which effectively contributes to rumor detection. These metrics are good indicators for the detection of fake images, even for those generated by advanced techniques like generative adversarial networks (GANs). Then, we introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features (i.e., text, social context, and image features) by supervised machine learning. Such algorithms provide interpretability and explainability in the decisions taken, which we believe is particularly important in the context of rumor verification. Experimental results show that MONITOR can detect rumors with an accuracy of 96% and 89% on the MediaEval benchmark and the FakeNewsNet dataset, respectively. These results are significantly better than those of state-of-the-art machine learning baselines., 25th European Conference on Advances in Databases and Information Systems (ADBIS 2021), Aug 2021, Tartu, Estonia

Users of social networks tend to post and share content with little restraint. Hence, rumors and fake news can quickly spread on a huge scale. This may pose a threat to the credibility of social media and can cause serious consequences in real life. Therefore, the task of rumor detection and verification has become extremely important. Assessing the veracity of a social media message (e.g., by fact checkers) involves analyzing the text of the message, its context and any multimedia attachment. This is a very time-consuming task that can be much helped by machine learning. In the literature, most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. In this paper, we second the hypothesis that exploiting all of the components of a social media post enhances the accuracy of veracity detection. To further the state of the art, we first propose using a set of advanced image features that are inspired from the field of image quality assessment, which effectively contributes to rumor detection. These metrics are good indicators for the detection of fake images, even for those generated by advanced techniques like generative adversarial networks (GANs). Then, we introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features (i.e., text, social context, and image features) by supervised machine learning. Such algorithms provide interpretability and explainability in the decisions taken, which we believe is particularly important in the context of rumor verification. Experimental results show that MONITOR can detect rumors with an accuracy of 96% and 89% on the MediaEval benchmark and the FakeNewsNet dataset, respectively. These results are significantly better than those of state-of-the-art machine learning baselines.

Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications. However, data security is of premium importance to many users and often restrains their adoption of cloud technologies. Various approaches, i.e., data encryption, anonymization, replication and verification, help enforce different facets of data security. Secret sharing is a particularly interesting cryptographic technique. Its most advanced variants indeed simultaneously enforce data privacy, availability and integrity, while allowing computation on encrypted data. The aim of this paper is thus to wholly survey secret sharing schemes with respect to data security, data access and costs in the pay-as-you-go paradigm.

National audience; Les conséquences d'une intrusion dans un système d'information peuvent s'avéver problématiques pour l'existence d'une entreprise ou d'une organisation. Les impacts sont synonymes d'une perte financière, d'image de marque et de sérieux. La détection d'une intrusion n'est pas une finalité en soit, la ré-duction du delta détection-réaction est devenue prioritaire. Nous proposons une méthode prenant en compte les aspects techniques par l'utilisation d'une mé-thode hybride de Data mining mais aussi les aspects fonctionnels. L'addition de ces deux aspects permet d'obtenir une vision générale sur l'hygiène du système d'information mais aussi une orientation sur la surveillance et les corrections à apporter.

International audience; Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications. However, data security is of premium importance to many users and often restrains their adoption of cloud technologies. Various approaches, i.e., data encryption, anonymization, replication and verification, help enforce different facets of data security. Secret sharing is a particularly interesting cryptographic technique. Its most advanced variants indeed simultaneously enforce data privacy, availability and integrity, while allowing computation on encrypted data. The aim of this paper is thus to wholly survey secret sharing schemes with respect to data security, data access and costs in the pay-as-you-go paradigm.

In the big data era, the cloud computing services have been adopted to face the emergence of data that needs to be stored and processed properly. However, these services need to provide safety mechanisms to insure its secure adoption. Thus, several solutions have been proposed including the use of secure architectures by customers. In that context, an architecture based on multi-agent systems has been proposed which aims to secure both storage and exploration of data hosted in the Cloud. In this paper, we present a brief synthesis of data security methods. We then focus on the multi-agent system architecture. Finally, we propose our solution considering the design and implementation in Java of an alteration agent which will ensure the secure storage of data stored in the Cloud. We finally present the test results of this agent on real datasets.

International audience; A data warehouse stores secret data about the privacy of individuals and important business activities. This makes access to this source a risk of disclosure of sensitive data. Hence the importance of implementing security measures which guarantee the data confidentiality by establishing an access control policy. In this direction, several propositions were made, but none are considered as a standard for access management to data warehouses. In this article, we will present our approach that allows first to exploit the permissions defined in the data sources in order to help the administrator to define access permissions to the data warehouse, and then our system will automatically generate the sensitivity level of each data warehouse element according to the permissions granted to an object in the data warehouse.

The expansion and democratization of the digital world coupled with the effect of the Internet globalization, has allowed individuals, countries, states and companies to interconnect and interact at incidence levels never previously imagined. Cybercrime, in turn, is unfortunately one the negative aspects of this rapid global interconnection expansion. We often find malicious individuals and/or groups aiming to undermine the integrity of Information Systems for either financial gain or to serve a cause. Our study investigates and proposes a hybrid data mining methodology in order to detect abnormal behavior that could potentially threaten the security of an Information System, in a simple way that is understandable to all involved parties, whether they are security experts or standard users.

International audience; Respect for privacy and data confidentiality in a company are two fundamentals that must be protected. However, a Data Warehouse can be used as a very powerful mechanism for discovering crucial information, hence the importance of implementing security measures which guarantee the data confidentiality by establishing an access control policy. In this direction, several propositions were made, however, none is considered as a standard of access management to data warehouses. In this article, we will present our approach that allows first to exploit the permissions defined in the data sources in order to help the administrator to define access permissions to the data warehouse, and then our system will automatically generate the sensitivity level of each data warehouse element according to the permissions granted to an object in the data warehouse.

International audience; Les entrepôts de données contiennent des données sensibles qui doivent être protégées contre les accès non autorisés, aussi bien directs que par inférence. Les accès directs sont contrôlables par des autorisations gérées par le serveur OLAP. Cependant, ce dernier n'offre pas de mécanismes pour protéger l'entrepôt contre deux types d'inférences : les inférences précises permettant la déduction de valeurs exactes des mesures, et les inférences partielles permettant d'avoir une idée grossière sur les valeurs des mesures. Dans cet article, nous proposons une approche pour la sécurisation des entrepôts de données qui, d'une part, interdit les inférences partielles dans le cas des requêtes utilisant la fonction d'agrégation Sum et, d'autre part, empêche les inférences précises dans le cas des requêtes utilisant les fonctions d'agrégation Min ou Max. Pour ce faire, nous exploitons les méthodes statistiques contre les inférences partielles, et les réseaux Bayésiens contre les inférences précises.

Republished from the International Journal of Data Warehousing and Mining, Vol. 11, No. 2, April-June 2015, 21-42; International audience; Cloud computing can help reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud rais-es legitimate security concerns. In this paper, we propose a new multi-secret sharing approach for deploying a data warehouse in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. We first validate the relevance of our ap-proach theoretically, and then experimentally with both a simple random dataset and the Star Schema Benchmark. We also demonstrate its superiority to related, existing methods.

Cloud business intelligence is an increasingly popular choice to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. In this pa-per, we propose a novel approach for securing cloud data warehouses by flexible verifiable secret sharing, fVSS. Secret sharing encrypts and distributes data over several cloud ser-vice providers, thus enforcing data privacy and availability. fVSS addresses four shortcomings in existing secret sharing-based approaches. First, it allows refreshing the data ware-house when some service providers fail. Second, it allows on-line analysis processing. Third, it enforces data integrity with the help of both inner and outer signatures. Fourth, it helps users control the cost of cloud warehousing by balanc-ing the load among service providers with respect to their pricing policies. To illustrate fVSS' efficiency, we thoroughly compare it with existing secret sharing-based approaches with respect to security features, querying power and data storage and computing costs.

National audience; La démocratisation d’Internet, couplée à l’effet de la mondialisation, a pour résultat d’interconnecter les personnes, les états et les entreprises. Le côté déplaisant de cette interconnexion mondiale des systèmes d’information réside dans un phénomène appelé "Cybercriminalité". Ainsi, des personnes, des groupes mal intentionnés ont pour objectif de nuire à l’intégrité des systèmes d’Information dans un but financier ou pour servir une "cause". Cependant, des moyens de protection existent depuis plusieurs années, mais ces derniers ne permettent pas une détection en temps réel et sont réservés aux seuls initiés. En conséquence, nous proposons une méthode d’analyse des flux en temps réel permettant de détecter les comportements anormaux et dangereux menaçant la sécurité des Systèmes d’Information et d’appréhender les risques d’une façon compréhensible par tous les acteurs.

Dans cet article, nous portons notre regard sur l'aide à la décision du point de vue des systèmes décisionnels au sens des entrepôts de données et de l'analyse en ligne. Après avoir défini les concepts qui sous-tendent ces systèmes, nous nous proposons d'aborder les problématiques de recherche qui leur sont liées selon quatre points de vue : les données, les environnements de stockage, les utilisateurs et la sécurité. Nous abordons finalement les problèmes qui restent ouverts dans le domaine des entrepôts de données.

This paper relies on a UML profile with a graphical concrete syntax for the design of secure data warehouses. The UML extensions define security concepts to adopt the RBAC and MAC standards, to define conflicts of interests, and to model multidimensional schemas. In addition, this profile has formal semantics defined in Prolog that provides for the verification of both the design well-formedness and the coherence of security policies of data warehouse designs.

This study introduces a new method based on Greedy-Boost, a multiple classifier system, for better and faster intrusion detection. Detection of the anomalies in the data-processing networks is regarded as a problem of data classification allowing to use data mining and machine learning techniques to perform intrusion detection. With such automatic processing procedures, human expertise only focuses on a small set of potential anomalies which may result in important time savings and efficiency. In order to be scalable and efficient, these kinds of approaches must respect important requirements. The first is to obtain a high level of precision, that is to be able to detect a maximum of anomalies with a minimum of false alarms. The second is to detect potential anomalies as fast as possible. We propose Greedy-Boost, a new approach of boosting which is based on an adaptive combination of multiple classifiers to perform the precision of the detection. This approach uses an aspect of smooth that ensures stability of the classifier system and offers speed of detection. The experimental results, conducted on the KDD99 dataset, prove that our proposed approach outperforms several state-of-the-art methods, particularly in detecting rare attack types.

The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them, data mining has brought on remarkable contributions to the intrusion detection problem. However, the generalization ability of data mining-based methods remains limited, and hence detecting sophisticated attacks remains a tough task. In this thread, we present a novel method based on both clustering and classification for developing an efficient intrusion detection system (IDS). The key idea is to take useful information exploited from fuzzy clustering into account for the process of building an IDS. To this aim, we first present cornerstones to construct additional cluster features for a training set. Then, we come up with an algorithm to generate an IDS based on such cluster features and the original input features. Finally, we experimentally prove that our method outperforms several well-known methods., Comment: 15th East-European Conference on Advances and Databases and Information Systems (ADBIS 11), Vienna : Austria (2011)

In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and dynamic large intrusion detection dataset, the detection accuracy of naïve Bayesian classifier does not scale up as well as decision tree. It has been successfully tested in other problem domains that naïve Bayesian tree improves the classification rates in large dataset. In naïve Bayesian tree nodes contain and split as regular decision-trees, but the leaves contain naïve Bayesian classifiers. The experimental results on KDD99 benchmark network intrusion detection dataset demonstrate that this new approach scales up the detection rates for different attack types and reduces false positives in network intrusion detection., {"references":["James P. Anderson, \"Computer security threat monitoring and\nsurveillance,\" Technical Report 98-17, James P. Anderson Co., Fort\nWashington, Pennsylvania, USA, April 1980.","Dorothy E. Denning, \"An intrusion detection model,\" IEEE Transaction\non Software Engineering, SE-13(2), 1987, pp. 222-232.","D. Y. Yeung, and Y. X. Ding, \"Host-based intrusion detection using\ndynamic and static behavioral models,\" Pattern Recognition, 36, 2003,\npp. 229-243.","Lazarevic, A., Ertoz, L., Kumar, V., Ozgur,. A., Srivastava, and J., \"A\ncomparative study of anomaly detection schemes in network intrusion\ndetection,\" In Proc. of the SIAM Conference on Data Mining, 2003.","Barbara, Daniel, Couto, Julia, Jajodia, Sushil, Popyack, Leonard, Wu,\nand Ningning, \"ADAM: Detecting intrusion by data mining,\" IEEE\nWorkshop on Information Assurance and Security, West Point, New\nYork, June 5-6, 2001.","Lee W., Stolfo S., and Mok K., \"Adaptive Intrusion Detection: A data\nmining approach,\" Artificial Intelligence Review, 14(6), December\n2000, pp. 533-567.","N.B. Amor, S. Benferhat, and Z. Elouedi, \"Naïve Bayes vs. decision\ntrees in intrusion detection systems,\" In Proc. of 2004 ACM Symposium\non Applied Computing, 2004, pp. 420-424.","Mukkamala S., Janoski G., and Sung A.H., \"Intrusion detection using\nneural networks and support vector machines,\" In Proc. of the IEEE\nInternational Joint Conference on Neural Networks, 2002, pp.1702-\n1707.","J. Luo, and S.M. Bridges, \"Mining fuzzy association rules and fuzzy\nfrequency episodes for intrusion detection,\" International Journal of\nIntelligent Systems, John Wiley & Sons, vol. 15, no. 8, 2000, pp. 687-\n703.\n[10] YU Yan, and Huang Hao, \"An ensemble approach to intrusion detection\nbased on improved multi-objective genetic algorithm,\" Journal of\nSoftware, vol. 18, no. 6, June 2007, pp. 1369-1378.\n[11] Shon T., Seo J., and Moon J., \"SVM approach with a genetic algorithm\nfor network intrusion detection,\" In Proc. of 20th International\nSymposium on Computer and Information Sciences (ISCIS 2005),\nBerlin: Springer-Verlag, 2005, pp. 224-233.\n[12] Dorothy E. Denning, and P.G. Neumann \"Requirement and model for\nIDES- A real-time intrusion detection system,\" Computer Science\nLaboratory, SRI International, Menlo Park, CA 94025-3493, Technical\nReport # 83F83-01-00, 1985.\n[13] D. Anderson, T. Frivold, A. Tamaru, and A. Valdes, \"Next generation\nintrusion detection expert system (NIDES),\" Software Users Manual,\nBeta-Update Release, Computer Science Laboratory, SRI International,\nMenlo Park, CA, USA, Technical Report SRI-CSL-95-0, May 1994.\n[14] D. Anderson, T.F. Lunt, H. Javitz, A. Tamaru, and A. Valdes, \"Detecting\nunusual program behavior using the statistical component of the next\ngeneration intrusion detection expert system (NIDES),\" Computer\nScience Laboratory, SRI International, Menlo Park, CA, USA, Technical\nReport SRI-CSL-95-06, May 1995.\n[15] S.E. Smaha, and Haystack, \"An intrusion detection system,\" in Proc. of\nthe IEEE Fourth Aerospace Computer Security Applications\nConference, Orlando, FL, 1988, pp. 37-44.\n[16] N. Ye, S.M. Emran, Q. Chen, and S. Vilbert, \"Multivariate statistical\nanalysis of audit trails for host-based intrusion detection,\" IEEE\nTransactions on Computers 51, 2002, pp. 810-820.\n[17] Martin Roesch, \"SNORT: The open source network intrusion system,\"\nOfficial web page of Snort at http://www.snort.org/\n[18] L. C. Wuu, C. H. Hung, and S. F. Chen, \"Building intrusion pattern\nminer for sonrt network intrusion detection system,\" Journal of Systems\nand Software, vol. 80, Issue 10, 2007, pp. 1699-1715.\n[19] W. Lee, R.A. Nimbalkar, K.K. Yee, S.B. Patil, P.H. Desai, T.T. Tran,\nand S.J. Stolfo, \"A data mining and CIDF based approach for detecting\nnovel and distributed intrusions,\" In Proc. of the 3rd International\nWorkshop on Recent Advances in Intrusion Detection (RAID 2000),\nToulouse , France, 2000, pp. 49-65.\n[20] W. Lee, and S.J. Stolfo, \"Data mining approach for intrusions\ndetection,\" In Proc. of the 7th USENIX Security Symposium\n(SECURITY-98), Berkeley, CA, USA, 1998, pp. 79-94.\n[21] C. Kruegel, D. Mutz, W. Robertson, and F. Valeur, \"Bayesian event\nclassification for intrusion detection,\" In Proc. of the 19th Annual\nComputer Security Applications Conference, Las Veges, NV, 2003.\n[22] N. Ye, M. Xu, and S.M. Emran, \"Probabilistic networks with undirected\nlinks for anomaly detection,\" In Proc. of the IEEE Systems, Man, and\nCybernetics Information Assurance and Security Workshop, West Point,\nNY, 2000.\n[23] A. Valdes, and K. Skinner, \"Adaptive model-based monitoring for cyber\nattack detection,\" In Recent Advances in Intrusion Detection Toulouse,\nFrance, 2000, pp. 80-92.\n[24] A.K. Ghosh, and A. Schwartzbart, \"A study in using neural networks for\nanomaly and misuse detection,\" In Proc. of the Eighth USENIX Security\nSymposium, Washington, DC, 1999, pp. 141-151.\n[25] M. Ramadas, and S.O.B. Tjaden, \"Detecting anomalous network traffic\nwith self-organizing maps,\" In Proc. of the 6th International Symposium\non Recent Advances in Intrusion Detection, Pittsburgh, PA, USA, 2003,\npp. 36-54.\n[26] R. Kohavi, \"Scaling up the accuracy of naïve Bayes classifiers: A\nDecision Tree Hybrid,\" In Proc. of the 2nd International Conference on\nKnowledge Discovery and Data Mining, Menlo Park, CA:AAAI\nPress/MIT Press, 1996, pp. 147-149.\n[27] The KDD Archive. KDD99 cup dataset, 1999.\nhttp://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html"]}

In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources., Comment: 14 Pages, IJNSA

In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources., {"references":["Richard Heady, George Luger, Arthur Maccabe, and Mark Servilla,\n\"The Architecture of a Network Level Intrusion Detection System,\"\nTechnical report, University of New Mexico, 1990.","James P. Anderson, \"Computer Security Threat Monitoring and\nSurveillance,\" Technical report, James P. Anderson Co., Fort\nWashington, Pennsylvania. April 1980.","Dorothy E. Denning, \"An Intrusion Detection Model,\" IEEE Transaction\non Software Engineering, SE-13(2), 1987, pp. 222-232.","Mukkamala S., Sung A. H. and Abraham A., \"Intrusion Detection using\nEnsemble of Soft Computing Paradigms,\" In Proceedings of the 3rd\nInternational Conference on Intelligent Systems Design and\nApplications, Springer Verlag Germany, 2003, pp. 209-217.","W.K. Lee, and S.J.Stolfo, \"A Data Mining Framework for Building\nIntrusion Detection Models,\" In Proceedings of the IEEE Symposium on\nSecurity and Privacy, Oakland, CA: IEEE computer Society Press, 1999,\npp. 120-132.","Commission of the European Communities, \"Information Technology\nSecurity Evaluation Criteria,\" Version 2.1.1991.","MIT Lincoln Laboratory, http://www.ll.mit.edu/IST/idaval/","Marcus A. Maloof, and Ryszard S. Michalski, \"Incremental learning\nwith partial instance memory,\" In Proceedings of Foundations of\nIntelligent Systems: 13th International Symposium, ISMIS 2002, volume\n2366 of Lecture Notes in Artificial Intelligence, Springer-Verlag, 2002,\npp. 16-27.","Wenke Lee, \"A Data Mining Framework for Constructing Features and\nModels for Intrusion Detection Systems,\" PhD thesis, Columbia\nUniversity, 1999.\n[10] Wei Fan, \"Cost-Sensitive, Scalable and Adaptive Learning using\nEnsemble-based Methods,\" PhD thesis, Columbia University, 2001.\n[11] M.A. Maloof and R.S. Michalski, \"A partial memory incremental\nlearning methodology and its applications to computer intrusion\ndetection,\" Reports of the Machine Learning and Inference Laboratory\nMLI 95-2, Machine Learning and Inference Laboratory, George Mason\nUniversity, 1995.\n[12] Kenneth A. Kaufman, Guido Cervone, and Ryszard S. Michalski, \"An\napplication of Symbolic Learning to Intrusion Detection: Preliminary\nResult from the LUS Methodology,\" Reports of the Machine Learning\nand Inference Laboratory MLI 03-2, Machine Learning and Inference\nLaboratory, George Mason University, 2003.\n[13] C. Elkan. (2007, Jan, 27). Results of the KDD-99 Knowledge Discovery\nContest [Online]. Available:\nhttp://www-cse.ucsd.edu/users/elkan/clresults.html\n[14] Tadeusz Pietraszek, and Chris Vanden Berghe, \"Defending Against\nInjection Attacks through Context-sensitive String Evaluation,\" In\nRecent Advances in Intrusion Detection (RAID2005), volume 3858 of\nLecture Notes in Computer Science, Seattle, WA, 2005, Springer-\nVerlag, pp. 124-145.\n[15] The PHP Group, PHP hypertext preprocessor, Web page at\nhttp://www.php.net. 2001-2004\n[16] The phpBB group, phpBB,com, Web page at http://www.phpbb,com.\n2001-1004\n[17] Martin Roesch, \"SNORT: The Open Source Network Intrusion System,\"\nOfficial web page of Snort at http://www.snort.org, 1998-2005.\n[18] X. Xu, X.N. Wang, \"Adaptive network intrusion detection method based\non PCA and support vector machines,\" Lecture Notes in Artificial\nIntelligence, ADMA 2005, LNAI 3584, 2005, pp. 696-703.\n[19] D.Y. Yeung, and Y.X. Ding, \"Host-based intrusion detection using\ndynamic and static behavioral model,\" Pattern Recognition, 36, 2003,\npp. 229-243."]}

This paper focuses on the challenges of post-ERP phases and especially on the actions and mechanisms to implement to stabilize these situations. The main objective is to show that these situations ...

The dramatic proliferation of sophisticated cyber attacks, in conjunction with the ever growing use of Internet-based services and applications, is nowadays becoming a great concern in any organization. Among many efficient security solutions proposed in the literature to deal with this evolving threat, ensemble approaches, a particular family of data mining, have proven very successful in designing high performance intrusion detection systems (IDSs) resting on the mutual combination of multiple classifiers. However, the strength of ensemble systems depends heavily on the methods to generate and combine individual classifiers. In this thread, we propose a novel design method to generate a robust ensemble-based IDS. In our approach, individual classifiers are built using both the input feature space and additional features exploited from k-means clustering. In addition, the ensemble combination is calculated based on the classification ability of classifiers on different local data regions defined in form of k-means clustering. Experimental results prove that our solution is superior to several well-known methods.

International audience; The extensive use of social media in the diffusion of information has also laid a fertile ground for the spread of rumors, which could significantly affect the credibility of social media. An ever-increasing number of users post news including, in addition to text, multimedia data such as images and videos. Yet, such multimedia content is easily editable due to the broad availability of simple and effective image and video processing tools. The problem of assessing the veracity of social network posts has attracted a lot of attention from researchers in recent years. However, almost all previous works have focused on analyzing textual contents to determine veracity, while visual contents, and more particularly images, remains ignored or little exploited in the literature. In this position paper, we propose a framework that explores two novel ways to assess the veracity of messages published on social networks by analyzing the credibility of both their textual and visual contents.

A new supervised learning algorithm using naïve Bayesian classifier is presented in this paper, which calculates the prior and conditional probabilities from a given training data and classifies the training examples using these probabilities. If any training example is misclassified then the algorithm calculates the information gain of attributes of the training data and chooses one attribute from training data with maximum information gain value. After the algorithm splits the training data into sub-datasets depending on the attribute values of the selected attribute, and again calculates the prior and conditional probabilities for each sub-dataset and classifies the examples of the each sub-dataset using their respective probabilities. The process will continue until all the training examples are correctly classified. Finally, the algorithm preserves the probabilities of each dataset for the future classification of unknown examples, whose attributes value are known but class value is unknown. The proposed algorithm addresses the problem of classifying the large dataset and it has been successfully tested on a number of benchmark problems, which achieved high classification rates using limited computational resources.

Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling and user driven-OLAP analysis., Comment: Business Intelligence Applications and the Web: Models, Systems and Technologies, Business Science Reference, 2011