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Shunmuganathan, K. L.
- Versioning File System in Cloud Computing with Intrusion Detection using Mobile Agent
Authors
1 Sathyabama University, Chennai, IN
2 R.M.K. Engineering College, Thiruvallur, IN
Source
Wireless Communication, Vol 5, No 4 (2013), Pagination: 163-169Abstract
Versioning file system is a file system to store the computer file in various several versions in the timeline. Snapshots of the gradual changes of the files are recorded in this file system. A gradual backup of the file directory is virtually stored in cloud. Intrusion detection for user versioning file handles a new way to track changes provided with the versioning metadata is hidden from the cloud host Mobile agent which reincarnates for each user file is used to govern the communication and parallel execution in cloud host. Each version of the files is stored and can be retrieved on user demand to perform replacement, difference viewing with other versioned file.
Keywords
Versioning File System, Intrusion Detection, Cloud Computing, Mobile Agent, Storage Service.
- Intrusion Detection System with Dynamic Training Model
Authors
1 Department of Computer Science and Engineering, Jeppiaar Engineering College, Tamilnadu–600119, IN
2 Department of Computer Science and Engineering, RMK Engineering College, Tamilnadu-601206, IN
Source
Wireless Communication, Vol 3, No 11 (2011), Pagination: 772-777Abstract
Intrusion detection relies on the extensive knowledge of security experts, particularly, on their familiarity with the computer systems to be protected. To reduce this dependency, various machine learning techniques and data mining techniques have been deployed for intrusion detection. An IDS is usually deployed in a dynamically changing environment, which requires continuous training of the intrusion detection model, in order to sustain sufficient performance. The manual training process carried out in the current systems depends on the system administrators in working out the training solution and in integrating it into the intrusion detection model.
In this paper, an automatically training IDS is proposed which will automatically train the detection model on-the-fly according to the feedback provided by operators when false predictions are encountered. The proposed system is evaluated using the KDDCup’99 intrusion detection dataset. Experimental results show that the system achieves up to 31% improvement in terms of misclassification cost when compared with a system lacking the tuning feature. If only 12% false predictions are used to train the model, the system still achieves about 32% improvement. Administrators can focus on verification of predictions with low confidence level, as only those predictions determined to be false will be used to train the detection model.