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Security Framework Article Comparison, Essay Example
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A net work security framework is an organized program within the network to ensure data protection from intruders. Data security is vital for purposes of privacy and confidentially of information. The main aim of every security framework is to hedge the organization against security risks that provide grounds for data loss and interference with integrity.
In the light of the above fact, this excerpt compares the operational mode of Common Security Framework (CSF) with Layer Based Intrusion Detection System (LBIDS). CSF as a security framework is mostly applied in health care organizations. The framework provides for a clear risk management approach which is done by a risk assessment tool that is able to appraise the level of risk exposure. The tool has pertinent features that enable data cryptography in the event of intrusion. Just like the LIBIDS framework, CSF approach towards detection of intrusion is based on the application of blockage at any detected point. The level of detection in both frameworks depends on the number of network layers that can correspond to the varied features of different attack in question. The CSF intrusion detection system ensures data integrity, availability and confidentiality; the features tend to conform to the goals of the LBIDS framework (Denning, 2004).
Unlike LBIDS which operates in a tiered layer form to ensure data availability, integrity and confidentiality, CSF through its risk assessment tool employ features such as intrusion detection platform that is customized with security Protocol systems to ensure the three vital elements of data. The protocol provides for automatic inspection of data packets that are transmitted into and out of the system. The system can enable a free interplay between the application systems and packet transmission systems (Shah, 2003).
Works cited
Denning. An intrusion-detection model. IEEE Transactions on Software Engineering, vol. (13), no. 2004
Shah, J. Undercoffer, Fuzzyclustering for intrusion detection. In The 12th IEEE International Conference on Fuzzy Systems IEEE Press, vol. (2) 2003.
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