Top Cited 2 Articles In 2017
International Journal Of Managing Information Technology (IJMIT)
Issn: 0975-5586 (Online); 0975-5926 (Print)
Autonomic Framework For It Security Governance
School of Engineering, Construction and Design (IT), Melbourne Polytechnic, Australia
With the recent service enhancements over the Internet, organisations are confronted with a growing magnitude of security intrusions and attacks. Current intrusion detection strategies have not been effective in the long term, as new and obfuscated security attacks keep emerging evading the surveillance mechanisms. With information technology (IT) playing a pivotal role in today’s organizational operations and value creation, security regulatory bodies have identified this situation not solely as a technology issue, rather due to the weakness of an organisation’s risk management practices and IT governance. Hence, recent attention has embarked on formulating proactive IT security governance for organisational sustenance. This paper proposes an autonomic framework for IT security governance that postulates a selflearning adaptive mechanism for an effective intrusion detection and risk management. Such a framework would facilitate autonomic ways of integrating existing context-dependent knowledge with new observed behaviour patterns gathered from network as well as host for detecting unknown security attacks effectively using mobile agents. In addition, this paper provides a roadmap for autonomic IT security governance by applying the proposed framework The roadmap employs a continuous improvement feedback loop. for achieving the targeted quality of service (QoS) in an organisation.
IT Security Governance, Intrusion Detection, Autonomic Framework, Self-learning & Mobile Agents
For More Details: http://aircconline.com/ijmit/V9N3/9317ijmit01.pdf
Volume Link: http://airccse.org/journal/ijmit/vol9.html
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Classification Of Questions And Learning Outcome Statements (Los) Into Bloom’s Taxonomy (Bt) By Similarity Measurements Towards Extracting Of Learning Outcome From Learning Material
Shadi Diab1 and Badie Sartawi2
1Information and Communication Technology Center, Al-Quds Open University, Ramallah – Palestine
2Associate Professor of Computer Science, Al-Quds University, Jerusalem – Palestine
Bloom’s Taxonomy (BT) have been used to classify the objectives of learning outcome by dividing the learning into three different domains; the cognitive domain, the effective domain and the psychomotor domain. In this paper, we are introducing a new approach to classify the questions and learning outcome statements (LOS) into Blooms taxonomy (BT) and to verify BT verb lists, which are being cited and used by academicians to write questions and (LOS). An experiment was designed to investigate the semantic relationship between the action verbs used in both questions and LOS to obtain more accurate classification of the levels of BT. A sample of 775 different action verbs collected from different universities allows us to measure an accurate and clear-cut cognitive level for the action verb. It is worth mentioning that natural language processing techniques were used to develop our rules as to induce the questions into chunks in order to extract the action verbs. Our proposed solution was able to classify the action verb into a precise level of the cognitive domain. We, on our side, have tested and evaluated our proposed solution using confusion matrix. The results of evaluation tests yielded 97% for the macro average of precision and 90% for F1. Thus, the outcome of the research suggests that it is crucial to analyse and verify the action verbs cited and used by academicians to write LOS and classify their questions based on blooms taxonomy in order to obtain a definite and more accurate classification..
Learning outcome; Natural Language Processing, Similarity Measurement; Questions Classification
For More Details: http://aircconline.com/ijmit/V9N2/9317ijmit01.pdf
Volume Link: http://airccse.org/journal/ijmit/vol9.html
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