Ahmed Adeeb Jalal
Al-Iraqia University

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Journal : International Journal of Electrical and Computer Engineering

Data loss prevention (DLP) by using MRSH-v2 algorithm Basheer Husham Ali; Ahmed Adeeb Jalal; Wasseem N. Ibrahem Al-Obaydy Al-Obaydy
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (362.378 KB) | DOI: 10.11591/ijece.v10i4.pp3615-3622

Abstract

Sensitive data may be stored in different forms. Not only legal owners but also malicious people are interesting of getting sensitive data. Exposing valuable data to others leads to severe Consequences. Customers, organizations, and /or companies lose their money and reputation due to data breaches. There are many reasons for data leakages. Internal threats such as human mistakes and external threats such as DDoS attacks are two main reasons for data loss. In general, data may be categorized based into three kinds: data in use, data at rest, and data in motion. Data Loss Prevention (DLP) are good tools to identify important data. DLP can do analysis for data content and send feedback to administrators to make decision such as filtering, deleting, or encryption. Data Loss Prevention (DLP) tools are not a final solution for data breaches, but they consider good security tools to eliminate malicious activities and protect sensitive information. There are many kinds of DLP techniques, and approximation matching is one of them. Mrsh-v2 is one type of approximation matching. It is implemented and evaluated by using TS dataset and confusion matrix. Finally, Mrsh-v2 has high score of true positive and sensitivity, and it has low score of false negative.
A web content mining application for detecting relevant pages using Jaccard similarity Ahmed Adeeb Jalal; Abdulrahman Ahmed Jasim; Amar A. Mahawish
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6461-6471

Abstract

The tremendous growth in the availability of enormous text data from a variety of sources creates a slew of concerns and obstacles to discovering meaningful information. This advancement of technology in the digital realm has resulted in the dispersion of texts over millions of web sites. Unstructured texts are densely packed with textual information. The discovery of valuable and intriguing relationships in unstructured texts demands more computer processing. So, text mining has developed into an attractive area of study for obtaining organized and useful data. One of the purposes of this research is to discuss text pre-processing of automobile marketing domains in order to create a structured database. Regular expressions were used to extract data from unstructured vehicle advertisements, resulting in a well-organized database. We manually develop unique rule-based ways of extracting structured data from unstructured web pages. As a result of the information retrieved from these advertisements, a systematic search for certain noteworthy qualities is performed. There are numerous approaches for query recommendation, and it is vital to understand which one should be employed. Additionally, this research attempts to determine the optimal value similarity for query suggestions based on user-supplied parameters by comparing MySQL pattern matching and Jaccard similarity.