Idris Ismaila

Work place: Department of Cyber Security, Federal University of Technology, Minna, Nigeria



Research Interests: Computational Learning Theory, Information Security, Network Security, Data Mining, Information-Theoretic Security


Dr. Ismaila Idris is with the Deparment of Cyber Security Science. He obtain his Bachelor degree with Federal University of Technology, Minna. M.Sc. with university of Ilorin and PhD degree with University of Teknologi Malaysia. His research interest are Information Security, Data Mining, Machine Learning, Evolutionary Algorithm.

Author Articles
Hybridized Technique for Copy-Move Forgery Detection Using Discrete Cosine Transform and Speeded-Up Robust Feature Techniques

By Joseph A. Ojeniyi Bolaji O. Adedayo Idris Ismaila Shafii M. Abdulhamid

DOI:, Pub. Date: 8 Apr. 2018

As the world has greatly experienced a serious advancement in the area of technological advancement over the years, the availability of lots of sophisticated and powerful image editing tools has been on the rise. These image editing tools have become easily available on the internet, which has made people who are a novice in the field of image editing, to be capable of tampering with an image easily without leaving any visible clue or trace behind, which has led to increase in digital images losing authenticity. This has led to developing various techniques for tackling authenticity and integrity of forged images. In this paper, a robust and enhanced algorithm is been developed in detecting copy-move forgery, which is done by hybridizing block-based DCT (Discrete Cosine Transform) technique and a keypoint-based SURF (Speeded-Up Robust Feature)technique using the MATLAB platform. The performance of the above technique has been compared with DCT and SURF techniques as well as other hybridized techniques in terms of precision, recall, FPR and accuracy metrics using MICC-F220 dataset. This technique works by applying DCT to the forged image, with the main goal of enhancing the detection rate of such image and then SURF is applied to the resulting image with the main goal of detecting those areas that are been tampered with on the image.  It has been observed that this paper’s technique named HDS has an effective detection rate on the MICC-F220 dataset with multiple cloning attacks and other various attacks such as rotation, scaling, a combination of scaling plus rotation, blur, compression, and noise.

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Comparative Analysis of Classification Algorithms for Email Spam Detection

By Shafii Muhammad Abdulhamid Maryam Shuaib Oluwafemi Osho Idris Ismaila John K. Alhassan

DOI:, Pub. Date: 8 Jan. 2018

The increase in the use of email in every day transactions for a lot of businesses or general communication due to its cost effectiveness and efficiency has made emails vulnerable to attacks including spamming. Spam emails also called junk emails are unsolicited messages that are almost identical and sent to multiple recipients randomly. In this study, a performance analysis is done on some classification algorithms including: Bayesian Logistic Regression, Hidden Na?ve Bayes, Radial Basis Function (RBF) Network, Voted Perceptron, Lazy Bayesian Rule, Logit Boost, Rotation Forest, NNge, Logistic Model Tree, REP Tree, Na?ve Bayes, Multilayer Perceptron, Random Tree and J48. The performance of the algorithms were measured in terms of Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operator Characteristics Area and Root Relative Squared Error using WEKA data mining tool. To have a balanced view on the classification algorithms’ performance, no feature selection or performance boosting method was employed. The research showed that a number of classification algorithms exist that if properly explored through feature selection means will yield more accurate results for email classification. Rotation Forest is found to be the classifier that gives the best accuracy of 94.2%. Though none of the algorithms did not achieve 100% accuracy in sorting spam emails, Rotation Forest has shown a near degree to achieving most accurate result.

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