Andrea J. Cullen

e‐Government in Jordan: challenges and opportunities

Purpose – The purpose of this paper is to examine the challenges encountered in e‐government implementation, as well as the potential opportunities available in the context of Jordanian society.Design/methodology/approach – A detailed examination and analysis of Jordans published e‐government vision and strategy is presented, together with a review of other relevant literature.Findings – The findings and implications of this study reveal Jordan is still lagging behind in utilising information and communication technologies for delivering government services online.Practical implications – An understanding of the current status of e‐government in Jordan can help policy makers in the country pursue development of the public sector organisations on the one hand, and would be of importance for Jordans economic future success on the other.Originality/value – This is believed to be the most up‐to‐date and comprehensive analysis of Jordans plans and assessment of its level of readiness for delivery of e‐gover...

A model of B2B e‐commerce, based on connectivity and purpose

Purpose – To present a complete and comprehensive model by which business‐to‐business (B2B) e‐commerce transactions for sales and purchases between organisations may be categorised.Design/methodology/approach – Literature from the e‐commerce and operations management fields was studied, and the findings were synthesised to develop a preliminary conceptual model of B2B interaction. The conceptual model was tested empirically using a qualitative research procedure involving focus groups. From this, its structure and content were validated and refined.Findings – The research found that the developed model, incorporating nine exclusive e‐commerce trading scenarios, covers all B2B selling and purchase transactions, which suggests that it is comprehensive. It further found that trading occurs in each of the nine scenarios within the model, thus suggesting that it is complete. These findings support the conclusion that the model represents a valid taxonomy for the classification of B2B e‐commerce transactions.Re...

Critical success factors for B2B e‐commerce use within the UK NHS pharmaceutical supply chain

Purpose – The purpose of this paper is to determine those factors perceived by users to influence the successful on‐going use of e‐commerce systems in business‐to‐business (B2B) buying and selling transactions through examination of the views of individuals acting in both purchasing and selling roles within the UK National Health Service (NHS) pharmaceutical supply chain.Design/methodology/approach – Literature from the fields of operations and supply chain management (SCM) and information systems (IS) is used to determine candidate factors that might influence the success of the use of e‐commerce. A questionnaire based on these is used for primary data collection in the UK NHS pharmaceutical supply chain. Factor analysis is used to analyse the data.Findings – The paper yields five composite factors that are perceived by users to influence successful e‐commerce use. “System quality,” “information quality,” “management and use,” “world wide web – assurance and empathy,” and “trust” are proposed as potentia...

Assessing information quality of e-learning systems: a web mining approach

E-learning systems provide a promising solution as an information exchanging channel. Improved technologies could mean faster and easier access to information but do not necessarily ensure the quality of this information; for this reason it is essential to develop valid and reliable methods of quality measurement and carry out careful information quality evaluations. This paper proposes an assessment model for information quality in e-learning systems based on the quality framework we proposed previously: the proposed framework consists of 14 quality dimensions grouped in three quality factors: intrinsic, contextual representation and accessibility. We use the relative importance as a parameter in a linear equation for the measurement scheme. Formerly, we implemented a goal-question-metrics approach to develop a set of quality metrics for the identified quality attributes within the proposed framework. In this paper, the proposed metrics were computed to produce a numerical rating indicating the overall information quality published in a particular e-learning system. The data collection and evaluation processes were automated using a web data extraction technique and results on a case study are discussed. This assessment model could be useful to e-learning systems designers, providers and users as it provides a comprehensive indication of the quality of information in such systems.

MARS: Multi-stage Attack Recognition System

Network Intrusion Detection Systems (NIDS) are considered as essential mechanisms to ensure reliable security. Intrusive model is used in signature-based NIDS by defining attack patterns and applying signature-matching on incoming traffic packets. Thousands of signatures and rules are created to specify different attacks and variations of a single attack. As a result, enormous data with less efficiency is produced that overwhelms the network administrator. Most of the generated alerts are false-positives; this is due to the redundancy caused by the detection techniques, and due to low-level processing capacity. Moreover, detection of novel and multi-stage attacks are not efficiently achieved by the current systems. Hence, high-level view of the attacker’s behaviour has become a stressing demand. Alerts correlation techniques have been widely used to provide intelligent and stateful detection methodologies. This is to understand attack steps and predict the expected sequence of events. However, most of the proposed systems are based on rules libraries specified by security experts, which is a cumbersome and error prone task. Other methods are based on statistical models; these are unable to identify causal relationships between the events. In this paper, we identify the limitations of the current techniques and propose a framework for alert correlation that overcomes these shortcomings. An improved “cause and effect” model will be presented cooperating with statistical model to achieve higher detection rate with minimum false positives. Knowledge-based model with vulnerability and extensional consequences parameters has been developed to provide manageable and meaningful graph. The proposed system is evaluated using DARPA 2000 and collected real life data sets. The results have shown an improvement in respect to detection rate and reduction of false positives.

Analytical modeling for spectrum handoff decision in cognitive radio networks

Cognitive Radio (CR) is an emerging technology used to significantly improve the efficiency of spectrum utilization. Although some spectrum bands in the primary users licensed spectrum are intensively used, most of the spectrum bands remain underutilized. The introduction of open spectrum and dynamic spectrum access lets the secondary (unli- censed) users, supported by cognitive radios; opportunistically utilize the unused spec- trum bands. However, if a primary user returns to a band occupied by a secondary user, the occupied spectrum band is vacated immediately by handing off the secondary users call to another idle spectrum band. Multiple spectrum handoffs can severely degrade qual- ity of service (QoS) for the interrupted users. To avoid multiple handoffs, when a licensed primary user appears at the engaged licensed band utilized by a secondary user, an effec- tive spectrum handoff procedure should be initiated to maintain a required level of QoS for secondary users. In other words, it enables the channel clearing while searching for target vacant channel(s) for completing unfinished transmission. This paper proposes prioritized proactive spectrum handoff decision schemes to reduce the handoff delay and the total ser- vice time. The proposed schemes have been modeled using a preemptive resume priority (PRP) M/G/1 queue to give a high priority to interrupted users to resume their transmission ahead of any other uninterrupted secondary user. The performance of proposed handoff schemes has been evaluated and compared against the existing spectrum handoff schemes. Experimental results show that the schemes developed here outperform the existing schemes in terms of average handoff delay and total service time under various traffic arri- val rates as well as service rates.

Evaluating Intrusion Detection Systems in High Speed Networks

The recent era has witnessed tremendous increase in the usage of computer network applications. Users of any type and requirement are compelled to be on a network. Today, the computer has become a network machine rather than a standalone system. This has generated challenges to the network security devices in terms of accuracy and reliability.Intrusion Detection Systems (IDS) are designed for the security needs of networks. Existing Network Intrusion Detection Systems (NIDS) are found to be limited in performance and utility especially once subjected to heavy traffic conditions. It has been observed that NIDS become less effective even when presented with a bandwidth of a few hundred megabits per second. In this work, we have endeavored to identify the causes which lead to unsatisfactory performance of NIDSs. In this regard, we have conducted an extensive performance evaluation of an open source intrusion detection system (Snort). This has been done on a highly sophisticated test-bench with different traffic conditions. We have also used different hardware and software platforms to determine the efficacy of the NIDS under test. Finally, in our results/ analysis, we have identified the factors responsible for the limited performance of Snort. We have also recommended few solutions for improving the performance of Snort.

Social Engineering Detection Using Neural Networks

Social Engineering (SE) is considered to be one of the most common problems facing information security today. Detecting social engineering is important because it attempts to secure organisations, consumers and systems from attempts to gain unauthorized access or to reveal some secrets by manipulating employees. The aim of this work is to introduce a new technique for detecting social engineering using neural networks. In this work we have used benchmark data and developed a new technique to extract features that can be used for neural network testing and training. Initial results are encouraging and indicate that machine learning can add an extra layer of security to protect individuals and organisations from social engineering attacks. Future work includes expanding the data set to include additional attack scenarios and benchmark data.

Factors mediating the routinisation of e-learning within a traditional university education environment

Technology-enhanced or Computer Aided Learning (e-learning) can be institutionally integrated and supported by learning management systems or Virtual Learning Environments (VLEs) to offer efficiency gains, effectiveness and scalability of the e-leaning paradigm. However this can only be achieved through integration of pedagogically intelligent approaches and lesson preparation tools environment and VLE that is well accepted by both the students and teachers. The aim of this study is to consider how effective and efficient e-learning is in a web platform environment. The paper concludes that blended learning approaches offer the most flexible and scalable route to e-learning.

Detection of coordinated attacks using alert correlation model

Alerts correlation techniques have been widely used to provide intelligent and stateful detection methodologies. This is to understand attack steps and predict the expected sequence of events. However, most of the proposed systems are based on rule-based mechanisms which are tedious and error prone. Other methods are based on statistical modeling; these are unable to identify causal relationships between the events. In this paper, an improved “requires/provides” model is presented which established a cooperation between statistical and knowledge-based model, to achieve higher detection rate with the minimal false positives. A knowledge-based model with vulnerability and extensional conditions provide manageable and meaningful attack graphs. The proposed model has been implemented in real-time and has successfully generated security events on establishing a correlation between attack signatures. The system has been evaluated to detect one of the most serious multi-stage attacks in cyber crime - Botnet. Zeus Botnet is analyzed within the realm of simulated malicious activities normally used by cyber criminals.