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Journal of Information and Communication Technology (JICT) Vol.17, No. 3, July 2018

KNOWLEDGE INTEGRATION AMONG FLOOD DISASTER MANAGEMENT TEAM: LESSONS FROM THE KEMAMAN DISTRICT
Nor Hidayati Zakaria, Mohammad Nazir Ahmad, Mohd Saiful Anuar Mohd Noor &Mazida Ahmad

NEW KEY EXPANSION FUNCTION OF RIJNDAEL 128-BIT RESISTANCE TO THE RELATED-KEY ATTACKS
Hassan Mansur Hussien, Zaiton Muda & Sharifah Md Yasin

HYBRID CAT SWARM OPTIMIZATION AND SIMULATED ANNEALING FOR DYNAMIC TASK SCHEDULING ON CLOUD COMPUTING ENVIRONMENT
Danlami Gabi, Abdul Samad Ismail, Anazida Zainal, Zalmiyah Zakaria & Ahmad Al-Khasawneh

A HYBRID METHOD BASED ON CUCKOO SEARCH ALGORITHM FOR GLOBAL OPTIMIZATION PROBLEMS
Mohammad Shehab, Ahamad Tajudin Khader & Makhlouf Laouchedi

PERFORMANCE ANALYSIS OF A REAL-TIME ADAPTIVE PREDICTION ALGORITHM FOR TRAFFIC CONGESTION
Khodabacchus Muhamad Nadeem & Tulsi Pawan Fowdur


KNOWLEDGE INTEGRATION AMONG FLOOD DISASTER MANAGEMENT TEAM: LESSONS FROM THE KEMAMAN DISTRICT
1Nor Hidayati Zakaria, 2Mohammad Nazir Ahmad, 3Mohd Saiful Anuar Mohd Noor &4Mazida Ahmad
1Faculty of Computing, Universiti Teknologi Malaysia, Malaysia
2Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia
3Kemaman District Office, Terengganu, Malaysia
4School of Computing, Universiti Utara Malaysia, Malaysia
hidayati@utm.mymnazir@ukm.edu.mysaifulm.noor@gmail.com; mazida@uum.edu.my

Abstract
Although flooding is a common disaster event in Malaysia, issues such as information and knowledge integration still have yet to be resolved. Flood management operations seem to be handled in an ad-hoc manner comprising issues such as miscommunication, lack of common understanding during coordination practices, and lack of smooth mutual agreement among flood management agencies. Thus, this paper discusses the flood knowledge integration measures that have been applied by the flood management team for Kemaman District, Terengganu, the first district achieved a Gold Standard award for flood disaster management in Malaysia. This study comprises a qualitative research method using a variety of techniques. These include a case study approach performed by interviewing key informants as well as studying archival documents. In addition, site visits were made to flood location areas in order to better understand the district’s flood management team’s achievement. The result of knowledge integration practice in preliminary, actual and post-flood phases is discussed in this research. This study shows the importance of knowledge integration as a successful factor for the district’s flood management plan.

Keywords: Flood disaster, flood management, knowledge integration.

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NEW KEY EXPANSION FUNCTION OF RIJNDAEL 128-BIT RESISTANCE TO THE RELATED-KEY ATTACKS
Hassan Mansur Hussien, Zaiton Muda & Sharifah Md Yasin
Faculty of Computer Science and Information Technology,
Universiti Putra Malaysia, Malaysia
hassanalobady@gmail.comzaitonm@upm.edu.myifah@upm.edu.my

Abstract
A master key of special length is manipulated based on the key schedule to create round sub-keys in most block ciphers. A strong key schedule is described as a cipher that will be more resistant to various forms of attacks, especially in related-key model attacks. Rijndael is the most common block cipher, and it was adopted by the National Institute of Standards and Technology, USA in 2001 as an Advance Encryption Standard. However, a few studies on cryptanalysis revealed that a security weakness of Rijndael refers to its vulnerability to related-key differential attack as well as the related-key boomerang attack, which is mainly caused by the lack of nonlinearity in the key schedule of Rijndael. In relation to this, constructing a key schedule that is both efficient and provably secure has been an ongoing open problem. Hence, this paper presents a method to improve the key schedule of Rijndael 128-bit for the purpose of making it more resistance to the related-key differential and boomerang attacks. In this study, two statistical tests, namely the Frequency test and the Strict Avalanche Criterion test were employed to respectively evaluate the properties of bit confusion and bit diffusion. The results showed that the proposed key expansion function has excellent statistical properties and agrees with the concept of Shannon’s diffusion and confusion bits. Meanwhile, the Mixed Integer Linear Programming based approach was adopted to evaluate the resistance of the proposed approach towards the related-key differential and boomerang attacks. The proposed approach was also found to be resistant against the two attacks discovered in the original Rijndael. Overall, these results proved that the proposed approach is able to perform better compared to the original Rijndael key expansion function and that of the previous research.

Keywords: Jey expansion function, related-key attacks, Rijndael Cipher, Mixed Integer Linear Programming, active s-boxes.

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HYBRID CAT SWARM OPTIMIZATION AND SIMULATED ANNEALING FOR DYNAMIC TASK SCHEDULING ON CLOUD COMPUTING ENVIRONMENT
1Danlami Gabi, 2Abdul Samad Ismail, 2Anazida Zainal, 2Zalmiyah Zakaria & 3Ahmad Al-Khasawneh
1Department of Kebbi State University of Science and Technology, Aliero, Nigeria
2Faculty of Computing, Universiti Teknologi Malaysia, Malaysia
3Faculty of Prince Al-Hussein bin Abdullah II of Information Technology, Hashemite University, Zarqa, Jordan
gabsonley@gmail.comabdsamad@utm.myanazida@gmail.comzalmiyah@utm.myakhasawneh@hu.edu.jo

Abstract
The unpredictable number of task arriving at cloud datacentre and the rescaling of virtual processing elements can affect the provisioning of better Quality of Service expectations during task scheduling in cloud computing. Existing researchers have contributed several task scheduling algorithms to provide better QoS expectations but are characterized with entrapment at the local search and high dimensional breakdown due to slow convergence speed and imbalance between global and local search, resulting from lack of scalability. Dynamic task scheduling algorithms that can adjust to long-time changes and continue facilitating the provisioning of better QoS are necessary for cloud computing environment. In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. In the proposed method, the orthogonal Taguchi approach is applied to enhance the SA which is incorporated into the local search of the proposed CSMCSOSA algorithm for scalability performance. A multi-objective QoS model based on execution time and execution cost criteria is presented to evaluate the efficiency of the proposed algorithm on CloudSim tool with two different datasets. Quantitative analysis of the algorithm is carried out with metrics of execution time, execution cost, QoS and performance improvement rate percentage. Meanwhile, the scalability analysis of the proposed algorithm using Isospeed-efficiency scalability metric is also reported. The results of the experiment show that the proposed CSM-CSOSA has outperformed Multi-Objective Genetic Algorithm, Multi-Objective Ant Colony and Multi-Objective Particle Swarm Optimization by returning minimum execution time and execution cost as well as better scalability acceptance rate of 0.4811−0.8990 respectively. The proposed solution when implemented in real cloud computing environment could possibly meet customers QoS expectations as well as that of the service providers.

Keywords: Cloud computing; multi-objective optimization; task scheduling; cat swarm optimization; simulated annealing.

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A HYBRID METHOD BASED ON CUCKOO SEARCH ALGORITHM FOR GLOBAL OPTIMIZATION PROBLEMS
1Mohammad Shehab, 1Ahamad Tajudin Khader & 2Makhlouf Laouchedi
1School of Computer Sciences, Universiti Sains Malaysia, Malaysia
2Université des Sciences et de Technologies Houari Boumediene, Algeria
moh.shehab12@gmail.comtajudin@usm.mylaoumakhl@yahoo.fr

Abstract
Cuckoo search algorithm is considered one of the promising metaheuristic algorithms applied to solve numerous problems in different fields. However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimization algorithm, which is a combination of Cuckoo search algorithmand Hill climbing called CSAHC discovers many local optimum traps by using local and global searches, although the local search method is trapped at the local minimum point. In other words, CSAHC has the ability to balance between the global exploration of the CSA and the deep exploitation of the HC method. The validation of the performance is determined by applying 13 benchmarks. The results of experimental simulations prove the improvement in the efficiency and the effect of the cooperation strategy and the promising of CSAHC.

Keywords: Cuckoo search algorithm, Hill climbing, optimization problems, slow convergence, exploration and exploitation.

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PERFORMANCE ANALYSIS OF A REAL-TIME ADAPTIVE PREDICTION ALGORITHM FOR TRAFFIC CONGESTION
Khodabacchus Muhamad Nadeem & Tulsi Pawan Fowdur
Department of Electrical and Electronic Engineering
University of Mauritius, Réduit, Mauritius
muhamad.khodacchus1@umail.uom.ac.mup.fowdur@uom.ac.mu

Abstract
Traffic congestion is a major factor to consider in the development of a sustainable urban road network. In the past, several mechanisms have been developed to predict congestion, but few have considered an adaptive real-time congestion prediction. This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. The implementation consisted of a Global Positioning System based transmitter connected to an Arduino board with a Global System for Mobile/General Packet Radio Service shield that relays the vehicle’s position to a cloud server. A control station then accesses the vehicle’s position in real-time, computes its speed. Based on the calculated speed, it estimates the congestion level and it applies the prediction algorithms to the congestion level to predict the congestion for future time intervals. The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states.

Keywords: Adaptive prediction, cloud server, Global Positioning System, real-time, traffic congestion.

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