1) IRAJ is moving to next issue from 16th April 2015.
2) IRAJ Management has issued a thank you note to all the editors for their constant endeavours for enhancement for IRAJ.
3) Chief editor of IRAJ has achieved UGC Net qualification.
|VOLUME 14, ISSUE 1, 1 October, 2018 To 31 December, 2020|
|VOLUME 13, ISSUE 1, 1 April, 2017 To 30 June, 2017|
|VOLUME 12, ISSUE 1, 1 January, 2017 To 31 March, 2017|
|VOLUME 11, ISSUE 1, 16 October, 2016 To 31 December, 2016|
|VOLUME 9, ISSUE 1, 15 April, 2016 To 14 June, 2016|
|VOLUME 8, ISSUE 1, 15 January, 2016 To 14 April, 2016|
|VOLUME 7, ISSUE 1, 15 October, 2015 To 14 January, 2016|
|VOLUME 6, ISSUE 1, 15 July, 2015 To 14 October, 2015|
|VOLUME 5, ISSUE 1, 16 April, 2015 To 15 July, 2015|
|VOLUME 4, ISSUE 1, 16 January, 2015 To 15 April, 2015|
|VOLUME 2, ISSUE 1, 16 August, 2014 To 15 November, 2014|
|VOLUME 1, ISSUE 1, 15 June, 2014 To 15 August, 2014|
during this era of networking, ton of data sharing is being done over native space networks and wide space networks. It's modified the human life to nice extents. With its unfold, challenges in networking also are increasing connected with security, information measure usage. a crucial drawback in intrusion detection is however effectively will separate the attack patterns and traditional information patterns from an oversized range of network information and the way effectively generate automatic intrusion rules once collected raw network information. To accomplish this, varied data processing techniques are used like classification, clustering, association rule mining etc. This thesis proposes to perform mining on the information collected from the IDS to reinforce the speed of detection of intrusion with automatic detection victimisation specific attributes of the intrusions.
Meta data for web site is becoming major source of advertising and promotion of the websites these days. Increasing number of Search Engines and improved search engine algorithms are also being challenges for the SEO specialists. Also involvement of the contents rich sites with images, video and audio are adding to the challenge. This work proposes to implement a Meta data generator with high efficiency and accuracy for the SEO specialists. The work is being proposed with the use of N-Gram technique using MATLAB which will allow to work on contents of the various human languages in addition to the plain ASCII text. The proposed work shall leverage the advantage of important HTML tags to retrieve important information and frequency based retrieval to decide the Meta data. Various data mining metrics shall be used to enforce the accuracy of the system.