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Rubin S.H.,SSC PAC 71730 | Bouabana-Tebibel T.,LCSI Laboratory
Intelligent Systems Reference Library | Year: 2016

The problem addressed, in this chapter, pertains to how to represent and apply knowledge to best facilitate its extension and use in problem solving. Unlike deductive logics (e.g., the predicate calculus), an inherent degree of error is allowed for so as to greatly enlarge the inferential space. This allowance, in turn, implies the application of heuristics (e.g., multiple analogies) to problem solving as well as their indirect use in inferring the heuristics themselves. This chapter is motivated by the science of inductive inference. Examples of state-space search, linguistic applications, and a focus methodology for generating novel knowledge (components) for wartime engagement for countering (cyber) threats (WAMS) are provided. © Springer International Publishing Switzerland 2016.


Chebba A.,LCSI Laboratory | Bouabana-Tebibel T.,LCSI Laboratory | Rubin S.H.,SSC PAC 71730
Advances in Intelligent Systems and Computing | Year: 2015

The ontology represents a rich model for knowledge representation that is gaining popularity over years. Its expressivity and computability promote its use in a large scale of applications. In this paper, we first present a metamodel that describes the ontology basic elements and the relationships among them. A new element is introduced to the ontology model called “Context”. This new conceptual element improves the representation quality and allows for the modeling of more complex knowledge efficiently. The context is next integrated to the proposed meta-model. © Springer International Publishing Switzerland 2015.


Hemam S.M.,University of Khenchela | Hidouci K.W.,LCSI Laboratory
2010 International Conference on Machine and Web Intelligence, ICMWI 2010 - Proceedings | Year: 2010

In this paper, we investigate a decentralized approach to timestamping transactions in a replicated database, under partial replication in Peer-To-Peer (P2P) environments. In order to solve problems of concurrent updates and node failures, we propose an architecture based on quorums, this architecture allows assigning a unique timestamp to each distributed transaction, to select the servers replicas and to coordinate the distributed execution of the transaction. © 2010 IEEE.


Bouzar-Benlabiod L.,LCSI laboratory | Benferhat S.,University of Artois | Bouabana-Tebibel T.,LCSI laboratory
Studies in Computational Intelligence | Year: 2013

Intrusion Detection Systems (IDS) are very important tools for network monitoring. However, they often produce a large quantity of alerts. The security operator who analyses IDS alerts is quickly overwhelmed. Alert correlation is a process applied to the IDS alerts in order to reduce their number. In this paper, we propose a new approach for logical based alert correlation which integrates the security operator's knowledge and preferences in order to present to him only the most suitable alerts. The representation and the reasoning on these knowledge and preferences are done using a new logic called Instantiated First Order Qualitative Choice Logic (IFO-QCL). Our modeling shows an alert as an interpretation which allows us to have an efficient algorithm that performs the correlation process in a polynomial time. Experimental results are achieved on data collected from a real system monitoring. The result is a set of stratified alerts satisfying the operators criteria. © Springer International Publishing Switzerland 2013.


Bouzar-Benlabiod L.,LCSI laboratory | Benferhat S.,University of Artois | Bouabana-Tebibel T.,LCSI laboratory
Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE | Year: 2013

Intrusion Detection Systems (IDS) are important tools for network monitoring. However, they produce a large quantity of alerts. The security operator that analyses IDS alerts is quickly overwhelmed. Alert correlation is a process applied to the IDS alerts in order to reduce their number. In this paper, we propose a new approach for logical based alert correlation which integrates the security operator's knowledge and preferences. The goal is to present to the security operator only the most suitable alerts. The representation and the reasoning on these knowledge and preferences are done using a new logic called Instantiated First Order Qualitative Choice Logic (IFO-QCL). Our algorithm performs the correlation process in a polynomial time. Experimentation are achieved on data collected from a real system monitoring. The result is a set of stratified alerts satisfying the operators criteria. Copyright © 2013 by Knowledge Systems Institute Graduate School.


Bouzar-Benlabiod L.,LCSI Laboratory | Benferhat S.,University of Artois | Bouabana-Tebibel T.,LCSI Laboratory
Intelligent Data Analysis | Year: 2014

Intrusion Detection Systems (IDS) are necessary and important tools for monitoring information systems. However they produce a huge quantity of alerts. Alerts correlation is a process that reduces the number of alerts reported by intrusion detection systems. In this paper, we propose a new algorithm for a logical-based alerts correlation approach that integrates: security operator's knowledge and preferences. The representation and the reasoning on these knowledge and preferences are done using a new logic called Instantiated First Order Qualitative Choice Logic (IFO-QCL). Our modeling views an alert as an interpretation which allows us to have an efficient algorithm that performs the correlation process in a polynomial time. This paper also provides experimental results which are achieved on datasets issued from a real monitoring system.


Rubin S.H.,Space and Naval Warfare Systems Center Pacific | Bouabana-Tebibel T.,LCSI Laboratory
Studies in Computational Intelligence | Year: 2015

Software security is increasingly a concern as cyber-attacks become more frequent and sophisticated. This chapter presents an approach to counter this trend and make software more resistant through redundancy and diversity. The approach, termed Novel Naval Cyber Strategies (NNCS), addresses how to immunize component-based software. The software engineer programs defining component rule bases using a schema-based Very High Level Language (VHLL). Chance and ordered transformation are dynamically balanced in the definition of diverse components. The system of systems is shown to be relatively immune to cyber-attacks; and, as a byproduct, yield this capability for effective component generalization. This methodology offers exponential increases in cyber security; whereas, conventional approaches can do no better than linear. A sample battle management application—including rule randomization—is provided. © Springer International Publishing Switzerland 2016.


Rubin S.H.,Space and Naval Warfare Systems Center Pacific | Bouabana-Tebibel T.,LCSI Laboratory | Hoadjli Y.,LCSI Laboratory | Ghalem Z.,LCSI Laboratory
Proceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016 | Year: 2016

The Traveling Salesman Problem (TSP) was first formulated in 1930 and is one of the most studied problems in optimization. If the optimal solution to the TSP can be found in polynomial time, it would then follow that every NP-hard problem could be solved in polynomial time, proving P=NP. It will be shown that our algorithm finds P∼NP with scale. Using a δ-ϵ proof, it is straightforward to show that as the number of cities goes to infinity, P goes to NP (i.e., δ>0 ). This was demonstrated using a quadratic number of parallel processors because that speedup, by definition, is polynomial. A fastest parallel algorithm is defined. Six distinct 3-D charts of empirical results are supplied. For example, using an arbitrary run of 5,000 cities, we obtained a tour within 0.00001063 percent of the optimal using 4,166,667 virtual processors (Intel Xenon E5-1603 @ 2.8 GHz). To save the calculated extra 209 miles would take a quantum computer, the fastest possible computer, over (5,000!/(2∗ ∗4,978 ∗ 22!)) ∗ 267,782 centuries. Clearly, the automated acquisition of heuristics and the associated P∼NP solutions are an important problem warranting attention. © 2016 IEEE.


Bouabana-Tebibel T.,LCSI Laboratory | Rubin S.H.,Space and Naval Warfare Systems Center Pacific | Chebba A.,LCSI Laboratory
Proceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015 | Year: 2015

Reuse requires high-level representation models. Ontology is commonly defined as a formal specification of knowledge conceptualization in a consensual form. OWL (Web Ontology Language) is the most used and expressive ontology description language that supports handling and reasoning. However, it lacks expressivity for some specific requirements. We propose, in this paper, to augment OWL with a new construct, named AssemblyRange, to enhance representation of grouped knowledge present in design. Such knowledge is materialized in the notions of concept context and concept encapsulation. It is illustrated in our work through the specific domain of cooling systems. The added element enhances the expressivity of OWL for reuse while preserving its semantics. © 2015 IEEE.


Rubin S.H.,Space and Naval Warfare Systems Center Pacific | Bouabana-Tebibel T.,LCSI Laboratory | Hoadjli Y.,LCSI Laboratory
Information Systems Frontiers | Year: 2016

The solution of intractable problems implies the use of heuristics. Quantum computers may find use for optimization problems, but have yet to solve any NP-hard problems. This paper demonstrates results in game theory for domain transference and the reuse of problem-solving knowledge through the application of learned heuristics. It goes on to explore the possibilities for the acquisition of heuristics for the solution of the NP-hard TSP problem. Here, it is found that simple heuristics (e.g., pairwise exchange) often work best in the context of more or less sophisticated experimental designs. Often, these problems are not amenable to exclusive logic solutions; but rather, require the application of hybrid approaches predicated on search. In general, such approaches are based on randomization and supported by parallel processing. This means that heuristic solutions emerge from attempts to randomize the search space. The paper goes on to present a constructive proof of the unbounded density of knowledge in support of the Semantic Randomization Theorem (SRT). It highlights this result and its potential impact upon the community of machine learning researchers. © 2016 Springer Science+Business Media New York (outside the USA)

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