GOM: New Genetic Optimizing Model for broadcasting tree in MANET
- Authors: Elaiwat, Said , Alazab, Ammar , Venkatraman, Sitalakshmi , Alazab, Mamoun
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Data broadcasting in a mobile ad-hoc network (MANET) is the main method of information dissemination in many applications, in particular for sending critical information to all hosts. Finding an optimal broadcast tree in such networks is a challenging task due to the broadcast storm problem. The aim of this work is to propose a new genetic model using a fitness function with the primary goal of finding an optimal broadcast tree. Our new method, called Genetic Optimisation Model (GOM) alleviates the broadcast storm problem to a great extent as the experimental simulations result in efficient broadcast tree with minimal flood and minimal hops. The result of this model also shows that it has the ability to give different optimal solutions according to the nature of the network. © 2010 IEEE.
Applying genetic alogorithm for optimizing broadcasting process in ad-hoc network
- Authors: Elaiwat, Said , Alazab, Ammar , Venkatraman, Sitalakshmi , Alazab, Mamoun
- Date: 2011
- Type: Text , Journal article
- Relation: International Journal of Recent Trends in Engineering & Technology Vol. 4, no. 1 (2011), p. 68-72
- Full Text: false
- Reviewed:
- Description: Optimizing broadcasting process in mobile ad hoc network (MANET) is considered as a main challenge due to many problems, such as Broadcast Storm problem and high complexity in finding the optimal tree resulting in an NP-hard problem. Straight forward techniques like simple flooding give rise to Broadcast Storm problem with a high probability. In this work, genetic algorithm (GA) that searches over a population that represents a distinguishable ‘structure’ is adopted innovatively to suit MANETs. The novelty of the GA technique adopted here to provide the means to tackle this MANET problem lies mainly on the proposed method of searching for a structure of a suitable spanning tree that can be optimized, in order to meet the performance indices related to the broadcasting problem. In other words, the proposed genetic model (GM) evolves with the structure of random trees (individuals) ‘genetically’ generated using rules that are devised specifically to capture MANET behaviour in order to arrive at a minimal spanning tree that satisfies certain fitness function. Also, the model has the ability to give different solutions depending on the main factors specified such as, ‘time’ (or speed) in certain situations and ‘reachability’ in certain others.
Skype Traffic Classification Using Cost Sensitive Algorithms
- Authors: Azab, Azab , Layton, Robert , Alazab, Mamoun , Watters, Paul
- Date: 2013
- Type: Text , Conference paper
- Relation: Proceedings - 4th Cybercrime and Trustworthy Computing Workshop, CTC 2013 p. 14-21
- Full Text: false
- Reviewed:
- Description: Voice over IP (VoIP) technologies such as Skype are becoming increasingly popular and widely used in different organisations, and therefore identifying the usage of this service at the network level becomes very important. Reasons for this include applying Quality of Service (QoS), network planning, prohibiting its use in some networks and lawful interception of communications. Researchers have addressed VoIP traffic classification from different viewpoints, such as classifier accuracy, building time, classification time and online classification. This previous research tested their models using the same version of a VoIP product they used for training the model, giving generalizability only to that version of the product. This means that as new VoIP versions are released, these classifiers become obsolete. In this paper, we address if this approach is applicable to detecting new, untrained, versions of Skype. We suggest that using cost-sensitive classifiers can help to improve the accuracy of detecting untrained versions, by testing compared to other algorithms. Our experiment demonstrates promising preliminary results to detect Skype version 4, by building a cost sensitive classifier on Skype version 3, achieving an F-measure score of 0.57. This is a drastic improvement from not using cost sensitivity, which scores an F-measure of 0. This approach may be enhanced to improve the detection results and extended to improve detection for other applications that change protocols from version to version.