Detecting Attacks Using Big Data with Process Mining
Abstract- In current digital world, Security has become the major issue for the organization. Every day the amount of data is growing in the world. Processing and analyzing of the data is becoming the new challenge for the analyzers. For this purpose, big data is useful to process the high volume of data in less time. Current security tools like existing firewalls and Intrusion Detection Systems are still not able to detect and prevent the attacks and intrusions in full proof manner and giving many false alarms. Big Data analytics concept could be very useful for analyzing, detection and providing full security to the organization because of the ability of handling the large amount of data. In this paper, we have described the concept and the roll of big data. We have also proposed a model using process mining to generate the alerts in the case of attacks.
Index Terms— Big Data, Process Mining, Intrusion Detection System, Logs.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright & License
All Research Plus Journals (RPJ) publish open access articles under the terms of the Creative Commons Attribution (CC BY-SA 4.0) https://creativecommons.org/licenses/by-sa/4.0/ License which permits use, distribution and reproduction in any medium, provided the original work is properly cited & ShareAlike terms followed.
Copyright on any research article in a journal published by a RPJ is retained by the author(s). Authors grant RPJ a license to publish the article and identify itself as the original publisher. Upon author(s) by giving permission to RPJ either via RPJ journal portal or other channel to publish their research work in RPJ agrees to all the terms and conditions of https://creativecommons.org/licenses/by-sa/4.0/ License and terms & condition set by RPJ.
3rd party copyright: It is the responsibility of author(s) to secure all necessary copyright permissions for the use of 3rd-party materials in their manuscript.
Research Plus Journals Open Access articles posted to repositories or websites are without warranty from RPJ of any kind, either express or implied, including, but not limited to, warranties of merchantability, fitness for a particular purpose, or non-infringement. To the fullest extent permitted by law RPJ disclaims all liability for any loss or damage arising out of, or in connection, with the use of or inability to use the content.