Salah Bouktif

Security Concerns in Cloud Computing

The rate of threats against IT systems is directly proportional to the rate of growing technology. The emergence of new technology requires researchers and practitioners attention to discover new threats in order to make it reliable. Cloud computing is an emerging technology paradigm that migrates current technological and computing concepts into utility-like solutions similar to electricity and water systems. Security issues in cloud computing is shown to be the biggest obstacle that could subvert the wide benefits of cloud computing. The new concepts that the cloud introduces, such as multi-tenancy, creates new challenges to the security community. Addressing these challenges requires, in addition to the ability to cultivate and tune the security measures developed for other systems, proposing new security policies, models, and protocols to address the unique cloud security challenges. In this work, we provide comprehensive study of cloud computing security that includes classification of known security threats and the state-of-the-art practices in the endeavor to calibrate these threats. This paper also provides the dependency level within classification and provides a solution in form of preventive actions rather than proactive actions.

A Combination of Compositional Index and Genetic Algorithm for Predicting Transmembrane Helical Segments

Transmembrane helix (TMH) topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method. Availability: The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.

Integrating Function Point Project Information for Improving the Accuracy of Effort Estimation

Software organizations are putting efforts to improve the accuracy of the project cost estimation. This in turn helps them to allocate resources. Software cost estimation has been an area of key interest in software engineering community. Many estimation models divided among various categories have been proposed over a period of time. Function Point (FP) is one of the useful software cost estimation methodology that was first proposed twenty-five years ago using the project repository that contained information about various aspects of software project. In the last twenty five years software development productivity has grown rapidly but the complexity weight metrics values assigned to count standard FP still remain same. This fact raises critical questions about the validity of the complexity weight values and accuracy of the estimation process. The objective of this work is to present a genetic algorithm based approach to calibrate the complexity weight metrics of FP using the project repository of International Software Benchmarking Standards Group (ISBSG) dataset. The contribution of this work shows that information reuse and integration of past projectpsilas function-point structural elements improves the accuracy of software estimation process.

Ant colony based approach to predict stock market movement from mood collected on Twitter

The Profile of Mood States (POMS) and its variations have been used in many real world contexts to assess individuals behavior and measure mood. Social Networks such as Twitter and Facebook are considered precious research sources of collecting user mood measurements. In particular, we are inspired in this paper, by recent work on the prediction of the stock market movement from attributes representing the public mood collected from Twitter. In this paper, we build a new prediction model for the same stock market problem based on single models combination. Our proposed approach to build such model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. We implement our approach using Ant Colony Optimization algorithm and we use customized Bayesian Classifiers as single models. We compare our approach against the best Bayesian single model, model learned from all the available data, bagging and boosting algorithms. Test results indicate that the proposed model for stock market prediction performs better than those derived by alternatives approaches.

A New Approach for Quality Enforcement in Communities of Web Services

Nowadays, Web Services are considered as de facto and attracting distributed approach of application/services integration over the Internet. Web Services can also operate within communities to improve their visibility and market share. In a community, Web Services usually offer competing and/or complementing services. In this paper, we augment the community approach by defining a specific-purpose community to monitor Web Services operating in any Web Services community. This monitoring community consists of a set of Web Services capable of observing other Web Services. Clients, providers, as well as managers of communities can make use of the monitoring community to check if a Web Service is operating as expected. This paper defines the overall architecture of the monitoring community, the business model behind, different rules and terms to be respected by its members, services it offers to its various classes of customers. The paper also presents promising experimental results using the monitoring community.

Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models’ interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions. Availability The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.

A genetic algorithm to enhance transmembrane helices prediction

A transmembrane helix (TMH) topology prediction is becoming a central problem in bioinformatics because the structure of TM proteins is difficult to determine by experimental means. Therefore, methods which could predict the TMHs topologies computationally are highly desired. In this paper we introduce TMHindex, a method for detecting TMH segments solely by the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index deduced from a combination of the difference in amino acid appearances in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, genetic algorithm was employed to find the optimal threshold value to separate TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in 70 testing protein sequences. The level of accuracy achieved using TMHindex in comparison to recent methods for predicting the topology of TM proteins is a strong argument in favor of our method.

Predicting Stability of Open-Source Software Systems Using Combination of Bayesian Classifiers

The use of free and Open-Source Software (OSS) systems is gaining momentum. Organizations are also now adopting OSS, despite some reservations, particularly about the quality issues. Stability of software is one of the main features in software quality management that needs to be understood and accurately predicted. It deals with the impact resulting from software changes and argues that stable components lead to a cost-effective software evolution. Changes are most common phenomena present in OSS in comparison to proprietary software. This makes OSS system evolution a rich context to study and predict stability. Our objective in this work is to build stability prediction models that are not only accurate but also interpretable, that is, able to explain the link between the architectural aspects of a software component and its stability behavior in the context of OSS. Therefore, we propose a new approach based on classifiers combination capable of preserving prediction interpretability. Our approach is classifier-structure dependent. Therefore, we propose a particular solution for combining Bayesian classifiers in order to derive a more accurate composite classifier that preserves interpretability. This solution is implemented using a genetic algorithm and applied in the context of an OSS large-scale system, namely the standard Java API. The empirical results show that our approach outperforms state-of-the-art approaches from both machine learning and software engineering.

Utilizing VIN for improved vehicular sensing

The wealth of sensor data generated by advanced vehicular sensors that are fitted in new, connected vehicles enables new applications for driver behavior, road health monitoring, and incident reporting. However, standard access mechanisms to the data restricts the services and insights that can be provided by vehicular applications. For those applications to have full access to all of the vehicular sensory data, custom hardware fitted with proprietary automaker software is needed to process raw sensor values. This limits rapid deployment at scale because of the time and costs needed for mass development across proprietary platforms. In this paper, we propose a system to provide access to the raw sensory data using the vehicles identification number in order to retrieve the vehicles sensors identification numbers, their description, and their related software libraries that house the data processing algorithms specific to the vehicles make and model. Smartphones can collect raw sensory data through the vehicles CAN-Bus interface, and use those software libraries to transform raw data into standard formats that can be used by vehicular applications. This way, many applications can be developed without having to worry about customization of hardware to process the data produced by each automakers sensor platforms.

MSN: mutual secure neighbor verification in multi-hop wireless networks

In a wireless network, mutual secure neighbor verification (MSN) is defined as the capability of a node (verifier) to verify the claim by another node (claimer) that it exists within a certain physical distance from the verifier. This problem has received great attention because it has numerous practical applications. Current state-of-the-art approaches to solve this problem, such as the use of time of flight, signal strength, and angle of arrival, suffer from impracticality in terms of application and computation cost. In this work, we propose two algorithms to mitigate the MSN problem during the incremental deployment phase of static senor networks. Each node should announce its location and the power level it uses for transmission. Cooperative and base station verifications are used to detect nodes that lie about their locations. The simulation results show that we can achieve high detection (>90%) of nodes that forge their location information using either high power transmission or by colluding with other malicious nodes. Copyright