International Journal Papers

Permanent URI for this collectionhttp://dl.cerist.dz/handle/CERIST/17

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TriDroid: a triage and classification framework for fast detection of mobile threats in android markets
(Springer-Verlag, 2021) Amira, Abdelouahab; Derhab, Abdelouahid; Karbab, ElMouatez Billah; Nouali, Omar; Aslam Khan , Farrukh
The Android platform is highly targeted by malware developers, which aim to infect the maximum number of mobile devices by uploading their malicious applications to different app markets. In order to keep a healthy Android ecosystem, app-markets check the maliciousness of newly submitted apps. These markets need to (a) correctly detect malicious app, and (b) speed up the detection process of the most likely dangerous applications among an overwhelming flow of submitted apps, to quickly mitigate their potential damages. To address these challenges, we propose TriDroid, a market-scale triage and classification system for Android apps. TriDroid prioritizes apps analysis according to their risk likelihood. To this end, we categorize the submitted apps as: botnet, general malware, and benign. TriDroid starts by performing a (1) Triage process, which applies a fast coarse-grained and less-accurate analysis on a continuous stream of the submitted apps to identify their corresponding queue in a three-class priority queuing system. Then, (2) the Classification process extracts fine-grained static features from the apps in the priority queue, and applies three-class machine learning classifiers to confirm with high accuracy the classification decisions of the triage process. In addition to the priority queuing model, we also propose a multi-server queuing model where the classification of each app category is run on a different server. Experiments on a dataset with more than 24K malicious and 3K benign applications show that the priority model offers a trade-off between waiting time and processing overhead, as it requires only one server compared to the multi-server model. Also it successfully prioritizes malicious apps analysis, which allows a short waiting time for dangerous applications compared to the FIFO policy.
SHARE-ABE: an efficient and secure data sharing framework based on ciphertext-policy attribute-based encryption and Fog computing
(Springer, 2022) Saidi, Ahmed; Nouali, Omar; Amira, Abdelouahab
Attribute-based encryption (ABE) is an access control mechanism that ensures efficient data sharing among dynamic groups of users by setting up access structures indicating who can access what. However, ABE suffers from expensive computation and privacy issues in resource-constrained environments such as IoT devices. In this paper, we present SHARE-ABE, a novel collaborative approach for preserving privacy that is built on top of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Our approach uses Fog computing to outsource the most laborious decryption operations to Fog nodes. The latter collaborate to partially decrypt the data using an original and efficient chained architecture. Additionally, our approach preserves the privacy of the access policy by introducing false attributes. Furthermore, we introduce a new construction of a collaboration attribute that allows users within the same group to combine their attributes while satisfying the access policy. Experiments and analyses of the security properties demonstrate that the proposed scheme is secure and efficient especially for resource-constrained IoT devices.
Minimum redundancy and maximum relevance for single and multi-document Arabic text summarization
(Elsevier, 2014-12) Oufaida, Houda; Nouali, Omar; Blache, Philippe
Automatic text summarization aims to produce summaries for one or more texts using machine techniques. In this paper, we propose a novel statistical summarization system for Arabic texts. Our system uses a clustering algorithm and an adapted discriminant analysis method: mRMR (minimum redundancy and maximum relevance) to score terms. Through mRMR analysis, terms are ranked according to their discriminant and coverage power. Second, we propose a novel sentence extraction algorithm which selects sentences with top ranked terms and maximum diversity. Our system uses minimal language-dependant processing: sentence splitting, tokenization and root extraction. Experimental results on EASC and TAC 2011 MultiLingual datasets showed that our proposed approach is competitive to the state of the art systems.
Lifetime-Aware Backpressure : A New Delay-Enhanced Backpressure-Based Routing Protocol
(IEEE, 2019-03) Kabou, Abdelbaset; Nouali-Taboudjemat, Nadia; Djahel, Soufiene; Yahiaoui, Saïd; Nouali, Omar
Dynamic backpressure is a highly desirable family of routing protocols known for their attractive mathematical properties. However, these protocols suffer from a high end-to-end delay making them inefficient for real-time traffic with strict end-to-end delay requirements. In this paper, we address this issue by proposing a new adjustable and fully distributed backpressure-based scheme with low queue management complexity, named Lifetime-Aware Backpressure (LTA-BP). The novelty in the proposed scheme consists in introducing the urgency level as a new metric for service differentiation among the competing traffic flows in the network. Our scheme not just significantly improves the quality of service provided for real-time traffic with stringent end-to-end delay constraints, but interestingly protects also the flows with softer delay requirements from being totally starved. The proposed scheme has been evaluated and compared against other state-of-the-art routing protocol, using computer simulation, and the obtained results show its superiority in terms of the achieved end-to-end delay and throughput.
Secure Inverted Index Based Search over Encrypted Cloud Data with User Access Rights Management
(Springer, 2019-01-18) Boucenna, Fateh; Nouali, Omar; Kechid, Samir; Kechadi, M. Tahar
Cloud computing is a technology that provides users with a large storage space and an enormous computing power. However, the outsourced data are often sensitive and confidential, and hence must be encrypted before being outsourced. Consequently, classical search approaches have become obsolete and new approaches that are compatible with encrypted data have become a necessity. For privacy reasons, most of these approaches are based on the vector model which is a time consuming process since the entire index must be loaded and exploited during the search process given that the query vector must be compared with each document vector. To solve this problem, we propose a new method for constructing a secure inverted index using two key techniques, homomorphic encryption and the dummy documents technique. However, 1) homomorphic encryption generates very large ciphertexts which are thousands of times larger than their corresponding plaintexts, and 2) the dummy documents technique that enhances the index security produces lots of false positives in the search results. The proposed approach exploits the advantages of these two techniques by proposing two methods called the compressed table of encrypted scores and the double score formula. Moreover, we exploit a second secure inverted index in order to manage the users’ access rights to the data. Finally, in order to validate our approach, we performed an experimental study using a data collection of one million documents. The experiments show that our approach is many times faster than any other approach based on the vector model.
Filtrage automatique de courriels Une approche adaptative et multiniveau
(Springer-Verlag, 2005-12-01) Nouali, Omar; Blache, Philippe
Cet article propose un système de courriers électroniques paramétrable avec plusieurs niveaux de filtrage: un filtrage simple basé sur l’information contenue dans l’entête du courriel; un filtrage booléen basé sur l’existence ou non de mots clés dans le corps du courriel; un filtrage vectoriel basé sur le poids de contribution des mots clés du courriel; un filtrage approfondi basé sur les propriétés linguistiques caractérisant la structure et le contenu du courriel. Nous proposons une solution adaptative qui offre au système la possibilité d’apprendre à partir de données, de modifier ses connaissances et de s’adapter à l’évolution des intérêts de l’utilisateur et à la variation de la nature des courriels dans le temps. De plus, nous utilisons un réseau lexical permettant d’améliorer la représentation du courriel en prenant en considération l’aspect sémantique.
Classification de courriers électroniques : Une approche par apprentissage basée sur des modèles linguistiques
(Lavoisier, Cachan cedex FRANCE, 2005) Nouali, Omar; Blache, Philippe
Nous proposons une double amélioration des systèmes de filtrage de courriels existants. D’une part, en utilisant une méthode d’apprentissage automatique permettant à un système de filtrage d’élaborer des profils utilisateur. D’autre part, nous utilisons un ensemble de connaissances linguistiques sous forme de modèles réduits issues de modèles linguistiques de textes. Dans ce contexte, nous cherchons à évaluer si l’utilisation de connaissances et de traitements linguistiques peut améliorer les performances d’un système de filtrage. En effet, nous utilisons, au-delà des caractéristiques lexicales, un ensemble d’indicateurs sur le message portant sur la structure et le contenu. Ces connaissances sont indépendantes du domaine d’application et la fiabilité repose sur l’opération d’apprentissage. Pour tenter de statuer sur la faisabilité de notre approche et d’évaluer son efficacité, nous l’avons expérimenté sur un corpus de 1 200 messages. Nous présentons les résultats d’un ensemble d’expériences d’évaluation
Automatic Classification and Filtering of Electronic Information: Knowledge-Based Filtering Approach
(Zarqa Private University, Jordan, 2004) Nouali, Omar; Blache, Philippe
In this paper we propose an artificial intelligent approach focusing on information filtering problem. First, we give an overview of the information filtering process and a survey of different models of textual information filtering. Second, we present our E-mail filtering tool. It consists of an expert system in charge of driving the filtering process in cooperation with a knowledge-based model. Neural networks are used to model all system knowledge. The system is based on machine learning techniques to continuously learn and improve its knowledge all along its life cycle. This email filtering tool assists the user in managing, selecting, classify and discarding non-desirable messages in a professional or non-professional context. The modular structure makes it portable and easy to adapt to other filtering applications such as web browsing. The performance of the system is discussed.
A Semantic vector space and features-based approach for automatic information filtering
(Elsevier, 2004) Nouali, Omar; Blache, Philippe
With advances in communication technology, the amount of electronic information available to the users will become increasingly important. Users are facing increasing difficulties in searching and extracting relevant and useful information. Obviously, there is a strong demand for building automatic tools that capture, filter, control and disseminate the information that will most likely match a user's interest. In this paper we propose two kinds of knowledge to improve the efficiency of information filtering process. A features-based model for representing, evaluating and classifying texts. A semantic vector space to complement the features-based model on taking into account the semantic aspect. We used a neural network to model the user's interests (profiles) and a set of genetic algorithms for the learning process to improve filtering quality. To show the efficacy of such knowledge to deal with information filtering problem, particularly we present an intelligent and dynamic email filtering tool. It assists the user in managing, selecting, classifying and discarding non-desirable messages in a professional or non-professional context. The modular structure makes it portable and easy to adapt to other filtering applications such as the web browsing. We illustrate and discuss the system performance by experimental evaluation results
Vectorial Information Structuring for Documents Filtering and Diffusion
(Zarqa Private University, 2008) Nouali, Omar; Krinah, Abdelghani
Information retrieval tries to identify relevant documents for an information need. The problems that an IR system should deal with include document indexing (which tries to extract important content from a document), user needs analysis (similar to document indexing but applied to a query), and their internal representation which makes them suitable for being explicitly manipulated by the corresponding algorithms (i.e., matching the query with the documents). This paper describes a vectorial approach for information organization, and its application to search/retrieval systems from a vast amount of textual data

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