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A Reinforcement learning based algorithm towards energy efficient 5G Multi-tier network

  • Authors: Islam, Nahina , Alazab, Ammar , Alazab, Mamoun
  • Date: 2019
  • Type: Text , Conference proceedings
  • Relation: 2019 Cybersecurity and Cyberforensics Conference (CCC); Melbourne, Vic; 8th-9th May, 2019 p. 96-101
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  • Description: Energy efficiency is a key factor in the next generation wireless communication systems. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for improving the energy efficiency. In this paper, we propose a novel reinforcement learning based decision making algorithm to implement sleep mode in the base stations (BSs) used in multi-tier 5G networks. We propose a Markovian Decision process (MDP) based algorithm to switch between three different power consumption modes of a BS for improving the energy efficiency of the 5G network. The MDP based approach intelligently switches between the states of the BS based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our results show that there is a significant gain in the energy efficiency when using our proposed MDP algorithm together with the three-state BSs. We have also shown the energy-delay tradeoff in order to design a delay aware network.

An optimal transportation routing approach using GIS-based dynamic traffic flows

  • Authors: Alazab, Ammar , Venkatraman, Sitalakshmi , Abawajy, Jemal , Alazab, Mamoun
  • Date: 2010
  • Type: Text , Conference proceedings
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  • Description: This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively.

Analysis of firewall log-based detection scenarios for evidence in digital forensics

  • Authors: Mukhtar, Rubiu , Al-Nemrat, Ameer , Alazab, Mamoun , Venkatraman, Sitalakshmi , Jahankhani, Hamid
  • Date: 2012
  • Type: Text , Journal article
  • Relation: International Journal of Electronic Security and Digital Forensics Vol. 4, no. 4 (2012), p. 261-279
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  • Description: With the recent escalating rise in cybercrime, firewall logs have attained much research focus in assessing their capability to serve as excellent evidence in digital forensics. Even though the main aim of firewalls is to screen or filter part or all network traffic, firewall logs could provide rich traffic information that could be used as evidence to prove or disprove the occurrence of online attack events for legal purposes. Since courts have a definition of what could be presented to it as evidence, this research investigates on the determinants for the acceptability of firewall logs as suitable evidence. Two commonly used determinants are tested using three different firewall-protected network scenarios. These determinants are: 1 admissibility that requires the evidence to satisfy certain legal requirements stipulated by the courts 2 weight that represents the sufficiency and extent to which the evidence convinces the establishment of cybercrime attack. Copyright © 2012 Inderscience Enterprises Ltd.
  • Description: 2003010400

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
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  • 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.

Cybercrime : The case of obfuscated malware

  • Authors: Alazab, Mamoun , Venkatraman, Sitalakshmi , Watters, Paul , Alazab, Moutaz , Alazab, Ammar
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: Joint 7th International Conference on Global Security, Safety and Sustainability, ICGS3 2011, and the 4th Conference on e-Democracy Vol. 99 LNICST, p. 204-211
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  • Description: Cybercrime has rapidly developed in recent years and malware is one of the major security threats in computer which have been in existence from the very early days. There is a lack of understanding of such malware threats and what mechanisms can be used in implementing security prevention as well as to detect the threat. The main contribution of this paper is a step towards addressing this by investigating the different techniques adopted by obfuscated malware as they are growingly widespread and increasingly sophisticated with zero-day exploits. In particular, by adopting certain effective detection methods our investigations show how cybercriminals make use of file system vulnerabilities to inject hidden malware into the system. The paper also describes the recent trends of Zeus botnets and the importance of anomaly detection to be employed in addressing the new Zeus generation of malware. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.
  • Description: 2003010650

Digital forensic techniques for static analysis of NTFS images

  • Authors: Alazab, Mamoun , Venkatraman, Sitalakshmi , Watters, Paul
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at 4th International Conference of Information Technology, ICIT 2009, AL-Zaytoonah University, Amman, Jordan : 3rd-5th June 2009
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  • Description: Static analysis of the Windows NTS File System (NTFS) which is the standard and most commonly used file system could provide useful information for digital forensics. However, since the NFTS disk image records every event in the system, forensic tools need to process an enormous amount of information related to user / kernel environment, buffer overflows, trace conditions, network stack and other related subsystems. This leads to imperfect forensic tools that are practical for implementation but not comprehensive and effective. This research discusses the analysis technique to detect data hidden based on the internal structure of the NTFS file system in the boot sector. Further, it attempts to unearth the vulnerabilities of NTFS disk image and weaknesses of the current forensic techniques. The paper argues that a comprehensive tool with improved techniques is warranted for a successful forensic analysis.
  • Description: 2003007524

Effective digital forensic analysis of the NTFS disk image

  • Authors: Alazab, Mamoun , Venkatraman, Sitalakshmi , Watters, Paul
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Ubiquitous Computing and Communication Journal Vol. 4, no. 3 (Special issue on ICIT 2009 Conference - Applied Computing) (2009), p. 551-558
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  • Description: Forensic analysis of the Windows NT File System (NTFS) could provide useful information leading towards malware detection and presentation of digital evidence for the court of law. Since NTFS records every event of the system, forensic tools are required to process an enormous amount of information related to user / kernel environment, buffer overflows, trace conditions, network stack, etc. This has led to imperfect forensic tools that are practical for implementation and hence become popular, but are not comprehensive and effective. Many existing techniques have failed to identify malicious code in hidden data of the NTFS disk image. This research discusses the analysis technique we have adopted to successfully detect maliciousness in hidden data, by investigating the NTFS boot sector. We have conducted experimental studies with some of the existing popular forensics tools and have identified their limitations. Further, through our proposed three-stage forensic analysis process, our experimental investigation attempts to unearth the vulnerabilities of NTFS disk image and the weaknesses of the current forensic techniques.
  • Description: 2003007525

Forensic identification and detection of hidden and obfuscated malware

  • Authors: Alazab, Mamoun
  • Date: 2012
  • Type: Text , Thesis , PhD
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  • Description: The revolution in online criminal activities and malicious software (malware) has posed a serious challenge in malware forensics. Malicious attacks have become more organized and purposefully directed. With cybercrimes escalating to great heights in quantity as well as in sophistication and stealth, the main challenge is to detect hidden and obfuscated malware. Malware authors use a variety of obfuscation methods and specialized stealth techniques of information hiding to embed malicious code, to infect systems and to thwart any attempt to detect them, specifically with the use of commercially available anti-malware engines. This has led to the situation of zero-day attacks, where malware inflict systems even with existing security measures. The aim of this thesis is to address this situation by proposing a variety of novel digital forensic and data mining techniques to automatically detect hidden and obfuscated malware. Anti-malware engines use signature matching to detect malware where signatures are generated by human experts by disassembling the file and selecting pieces of unique code. Such signature based detection works effectively with known malware but performs poorly with hidden or unknown malware. Code obfuscation techniques, such as packers, polymorphism and metamorphism, are able to fool current detection techniques by modifying the parent code to produce offspring copies resulting in malware that has the same functionality, but with a different structure. These evasion techniques exploit the drawbacks of traditional malware detection methods, which take current malware structure and create a signature for detecting this malware in the future. However, obfuscation techniques aim to reduce vulnerability to any kind of static analysis to the determent of any reverse engineering process. Furthermore, malware can be hidden in file system slack space, inherent in NTFS file system based partitions, resulting in malware detection that even more difficult.
  • Description: Doctor of Philosophy

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
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  • 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.

Hybrids of support vector machine wrapper and filter based framework for malware detection

  • Authors: Huda, Shamsul , Abawajy, Jemal , Alazab, Mamoun , Abdollalihiand, Mali , Islam, Rafiqul , Yearwood, John
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 55, no. (2016), p. 376-390
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  • Description: Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation Anti-Virus (AV) engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current AV engines in detecting malware. In this paper we propose a hybrid framework for malware detection by using the hybrids of Support Vector Machines Wrapper, Maximum-Relevance–Minimum-Redundancy Filter heuristics where Application Program Interface (API) call statistics are used as a malware features. The novelty of our hybrid framework is that it injects the filter’s ranking score in the wrapper selection process and combines the properties of both wrapper and filters and API call statistics which can detect malware based on the nature of infectious actions instead of signature. To the best of our knowledge, this kind of hybrid approach has not been explored yet in the literature in the context of feature selection and malware detection. Knowledge about the intrinsic characteristics of malicious activities is determined by the API call statistics which is injected as a filter score into the wrapper’s backward elimination process in order to find the most significant APIs. While using the most significant APIs in the wrapper classification on both obfuscated and benign types malware datasets, the results show that the proposed hybrid framework clearly surpasses the existing models including the independent filters and wrappers using only a very compact set of significant APIs. The performances of the proposed and existing models have further been compared using binary logistic regression. Various goodness of fit comparison criteria such as Chi Square, Akaike’s Information Criterion (AIC) and Receiver Operating Characteristic Curve ROC are deployed to identify the best performing models. Experimental outcomes based on the above criteria also show that the proposed hybrid framework outperforms other existing models of signature types including independent wrapper and filter approaches to identify malware.

Hybrids of support vector machine wrapper and filter based framework for malware detection

  • Authors: Huda, Shamsul , Abawajy, Jemal , Alazab, Mamoun , Abdollahian, Mali , Islam, Rafiqul , Yearwood, John
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 55, no. (2016), p. 376-390
  • Full Text: false
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  • Description: Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation Anti-Virus (AV) engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current AV engines in detecting malware. In this paper we propose a hybrid framework for malware detection by using the hybrids of Support Vector Machines Wrapper, Maximum-Relevance–Minimum-Redundancy Filter heuristics where Application Program Interface (API) call statistics are used as a malware features. The novelty of our hybrid framework is that it injects the filter’s ranking score in the wrapper selection process and combines the properties of both wrapper and filters and API call statistics which can detect malware based on the nature of infectious actions instead of signature. To the best of our knowledge, this kind of hybrid approach has not been explored yet in the literature in the context of feature selection and malware detection. Knowledge about the intrinsic characteristics of malicious activities is determined by the API call statistics which is injected as a filter score into the wrapper’s backward elimination process in order to find the most significant APIs. While using the most significant APIs in the wrapper classification on both obfuscated and benign types malware datasets, the results show that the proposed hybrid framework clearly surpasses the existing models including the independent filters and wrappers using only a very compact set of significant APIs. The performances of the proposed and existing models have further been compared using binary logistic regression. Various goodness of fit comparison criteria such as Chi Square, Akaike’s Information Criterion (AIC) and Receiver Operating Characteristic Curve ROC are deployed to identify the best performing models. Experimental outcomes based on the above criteria also show that the proposed hybrid framework outperforms other existing models of signature types including independent wrapper and filter approaches to identify malware.

Identifying cyber predators through forensic authorship analysis of chat logs

  • Authors: Amuchi, Faith , Al-Nemrat, Ameer , Alazab, Mamoun , Layton, Robert
  • Date: 2012
  • Type: Text , Conference proceedings
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  • Description: Online Grooming is a growing phenomenon within online environments. One of the major problems encountered in qualitative internet research of chat communication is the issue of anonymity which is being exploited and greatly enjoyed by chatters. An important question that has been asked in the literature is 'How can a researcher be sure to analyse the communication of children and adolescents and not the chat communication of adults who pretend to be under 18?'. Our reply to this question would be the field of Authorship Analysis. Authorship Analysis offers a way to unmask the anonymity of cyber predators. Stylometry, as used in this chat log analysis, is a type of Authorship Analysis that is not based on an author's handwriting but includes contextual clues from the content of their writings. This research paper will analyse the application of different authorship attribution techniques to chat log from a forensic perspective. © 2012 IEEE.
  • Description: 2003011054

Information security governance: The art of detecting hidden malware

  • Authors: Alazab, Mamoun , Venkatraman, Sitalakshmi , Watters, Paul
  • Date: 2013
  • Type: Text , Book chapter
  • Relation: IT Security governance innovations: Theory and research p. 293-315
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  • Description: Detecting malicious software or malware is one of the major concerns in information security governance as malware authors pose a major challenge to digital forensics by using a variety of highly sophisticated stealth techniques to hide malicious code in computing systems, including smartphones. The current detection techniques are futile, as forensic analysis of infected devices is unable to identify all the hidden malware, thereby resulting in zero day attacks. This chapter takes a key step forward to address this issue and lays foundation for deeper investigations in digital forensics. The goal of this chapter is, firstly, to unearth the recent obfuscation strategies employed to hide malware. Secondly, this chapter proposes innovative techniques that are implemented as a fully-automated tool, and experimentally tested to exhaustively detect hidden malware that leverage on system vulnerabilities. Based on these research investigations, the chapter also arrives at an information security governance plan that would aid in addressing the current and future cybercrime situations.

Malicious code detection using penalized splines on OPcode frequency

  • Authors: Alazab, Mamoun , Al Kadiri, Mohammad , Venkatraman, Sitalakshmi , Al-Nemrat, Ameer
  • Date: 2012
  • Type: Text , Conference proceedings
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  • Description: Recently, malicious software are gaining exponential growth due to the innumerable obfuscations of extended x86 IA-32 (OPcodes) that are being employed to evade from traditional detection methods. In this paper, we design a novel distinguisher to separate malware from benign that combines Multivariate Logistic Regression model using kernel HS in Penalized Splines along with OPcode frequency feature selection technique for efficiently detecting obfuscated malware. The main advantage of our penalized splines based feature selection technique is its performance capability achieved through the efficient filtering and identification of the most important OPcodes used in the obfuscation of malware. This is demonstrated through our successful implementation and experimental results of our proposed model on large malware datasets. The presented approach is effective at identifying previously examined malware and non-malware to assist in reverse engineering. © 2012 IEEE.
  • Description: 2003011056

Malicious Spam Emails Developments and Authorship Attribution

  • Authors: Alazab, Mamoun , Layton, Robert , Broadhurst, Roderic , Bouhours, Brigitte
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: Proceedings - 4th Cybercrime and Trustworthy Computing Workshop, CTC 2013 p. 58-68
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  • Description: The Internet is a decentralized structure that offers speedy communication, has a global reach and provides anonymity, a characteristic invaluable for committing illegal activities. In parallel with the spread of the Internet, cybercrime has rapidly evolved from a relatively low volume crime to a common high volume crime. A typical example of such a crime is the spreading of spam emails, where the content of the email tries to entice the recipient to click a URL linking to a malicious Web site or downloading a malicious attachment. Analysts attempting to provide intelligence on spam activities quickly find that the volume of spam circulating daily is overwhelming; therefore, any intelligence gathered is representative of only a small sample, not of the global picture. While past studies have looked at automating some of these analyses using topic-based models, i.e. separating email clusters into groups with similar topics, our preliminary research investigates the usefulness of applying authorship-based models for this purpose. In the first phase, we clustered a set of spam emails using an authorship-based clustering algorithm. In the second phase, we analysed those clusters using a set of linguistic, structural and syntactic features. These analyses reveal that emails within each cluster were likely written by the same author, but that it is unlikely we have managed to group together all spam produced by each group. This problem of high purity with low recall, has been faced in past authorship research. While it is also a limitation of our research, the clusters themselves are still useful for the purposes of automating analysis, because they reduce the work needing to be performed. Our second phase revealed useful information on the group that can be utilized in future research for further analysis of such groups, for example, identifying further linkages behind spam campaigns.

Malware detection based on structural and behavioural features of API calls

  • Authors: Alazab, Mamoun , Layton, Robert , Venkatraman, Sitalakshmi , Watters, Paul
  • Date: 2010
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: In this paper, we propose a five-step approach to detect obfuscated malware by investigating the structural and behavioural features of API calls. We have developed a fully automated system to disassemble and extract API call features effectively from executables. Using n-gram statistical analysis of binary content, we are able to classify if an executable file is malicious or benign. Our experimental results with a dataset of 242 malwares and 72 benign files have shown a promising accuracy of 96.5% for the unigram model. We also provide a preliminary analysis by our approach using support vector machine (SVM) and by varying n-values from 1 to 5, we have analysed the performance that include accuracy, false positives and false negatives. By applying SVM, we propose to train the classifier and derive an optimum n-gram model for detecting both known and unknown malware efficiently.

Mining malware to detect variants

  • Authors: Azab, Ahmad , Layton, Robert , Alazab, Mamoun , Oliver, Jonathan
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 5th Cybercrime and Trustworthy Computing Conference, CTC 2014; Aukland, New Zealand; 24th-25th November 2014 p. 44-53
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  • Description: Cybercrime continues to be a growing challenge and malware is one of the most serious security threats on the Internet today which have been in existence from the very early days. Cyber criminals continue to develop and advance their malicious attacks. Unfortunately, existing techniques for detecting malware and analysing code samples are insufficient and have significant limitations. For example, most of malware detection studies focused only on detection and neglected the variants of the code. Investigating malware variants allows antivirus products and governments to more easily detect these new attacks, attribution, predict such or similar attacks in the future, and further analysis. The focus of this paper is performing similarity measures between different malware binaries for the same variant utilizing data mining concepts in conjunction with hashing algorithms. In this paper, we investigate and evaluate using the Trend Locality Sensitive Hashing (TLSH) algorithm to group binaries that belong to the same variant together, utilizing the k-NN algorithm. Two Zeus variants were tested, TSPY-ZBOT and MAL-ZBOT to address the effectiveness of the proposed approach. We compare TLSH to related hashing methods (SSDEEP, SDHASH and NILSIMSA) that are currently used for this purpose. Experimental evaluation demonstrates that our method can effectively detect variants of malware and resilient to common obfuscations used by cyber criminals. Our results show that TLSH and SDHASH provide the highest accuracy results in scoring an F-measure of 0.989 and 0.999 respectively. © 2014 IEEE.

Secure passive keyless entry and start system using machine learning

Security and blockchain convergence with internet of multimedia things : current trends, research challenges and future directions

  • Authors: Jan, Mian , Cai, Jinjin , Gao, Xiang-Chuan , Khan, Fazlullah , Mastorakis, Spyridon , Usman, Muhammad , Alazab, Mamoun , Watters, Paul
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Journal of Network and Computer Applications Vol. 175, no. (2021), p.
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  • Description: The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications. © 2020 Elsevier Ltd

Six sigma approach to improve quality in e-services: An empirical study in Jordan

  • Authors: Alhyari, Salah , Alazab, Moutaz , Venkatraman, Sitalakshmi , Alazab, Mamoun , Alazab, Ammar
  • Date: 2012
  • Type: Text , Journal article
  • Relation: International Journal of Electronic Government Research Vol. 8, no. 2 (April, 2012), p. 57-74
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  • Description: This paper investigates the application of the Six Sigma approach to improve quality in electronic services (e-services) as more countries are adopting e-services as a means of providing services to their people through the Web. This paper presents a case study about the use of Six Sigma model to measure customer satisfaction and quality levels achieved in e-services that were recently launched by public sector organisations in a developing country, such as Jordan. An empirical study consisting of 280 customers of Jordan's e-services is conducted and problems are identified through the DMAIC phases of Six Sigma. The service quality levels are measured and analysed using six main criteria: Website Design, Reliability, Responsiveness, Personalization, Information Quality, and System Quality. The study indicates a 74% customer satisfaction with a Six Sigma level of 2.12 has enabled the Greater Amman Municipality to identify the usability issues associated with their e-services offered by public sector organisations. The aim of the paper is not only to implement Six Sigma as a measurement-based strategy for improving e-customer service in a newly launched e-service programme, but also widen its scope in investigating other service dimensions and perform comparative studies in other developing countries.