H.3.15.3. Evolutionary computing and genetic algorithms
Mahdieh Maazalahi; Soodeh Hosseini
Abstract
Detecting and preventing malware infections in systems is become a critical necessity. This paper presents a hybrid method for malware detection, utilizing data mining algorithms such as simulated annealing (SA), support vector machine (SVM), genetic algorithm (GA), and K-means. The proposed method combines ...
Read More
Detecting and preventing malware infections in systems is become a critical necessity. This paper presents a hybrid method for malware detection, utilizing data mining algorithms such as simulated annealing (SA), support vector machine (SVM), genetic algorithm (GA), and K-means. The proposed method combines these algorithms to achieve effective malware detection. Initially, the SA-SVM method is employed for feature selection, where the SVM algorithm identifies the best features, and the SA algorithm calculates the SVM parameters. Subsequently, the GA-K-means method is utilized to identify attacks. The GA algorithm selects the best chromosome for cluster centers, and the K-means algorithm has applied to identify malware. To evaluate the performance of the proposed method, two datasets, Andro-Autopsy and CICMalDroid 2020, have been utilized. The evaluation results demonstrate that the proposed method achieves high true positive rates (0.964, 0.985), true negative rates (0.985, 0.989), low false negative rates (0.036, 0.015), and false positive rates (0.022, 0.043). This indicates that the method effectively detects malware while reasonably minimizing false identifications.
Zahra Asghari Varzaneh; Soodeh Hosseini
Abstract
This paper proposed a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD) and then the fuzzy membership functions are tuned using the Harris Hawks optimization (HHO). The experimental data set, ...
Read More
This paper proposed a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD) and then the fuzzy membership functions are tuned using the Harris Hawks optimization (HHO). The experimental data set, PIDD with the age group from 25-30 is initially processed and the crisp values are converted into fuzzy values in the stage of fuzzification. The improved fuzzy expert system increases the classification accuracy which outperforms several famous methods for diabetes disease diagnosis. The HHO algorithm is applied to tune fuzzy membership functions to determine the best range for fuzzy membership functions and increase the accuracy of fuzzy rule classification. The experimental results in terms of accuracy, sensitivity, and specificity prove that the proposed expert system has a higher ability than other data mining models in diagnosing diabetes.
S. Hosseini; M. Khorashadizade
Abstract
High dimensionality is the biggest problem when working with large datasets. Feature selection is a procedure for reducing the dimensionality of datasets by removing additional and irrelevant features; the most effective features in the dataset will remain, increasing the algorithms’ performance. ...
Read More
High dimensionality is the biggest problem when working with large datasets. Feature selection is a procedure for reducing the dimensionality of datasets by removing additional and irrelevant features; the most effective features in the dataset will remain, increasing the algorithms’ performance. In this paper, a novel procedure for feature selection is presented that includes a binary teaching learning-based optimization algorithm with mutation (BMTLBO). The TLBO algorithm is one of the most efficient and practical optimization techniques. Although this algorithm has fast convergence speed and it benefits from exploration capability, there may be a possibility of trapping into a local optimum. So, we try to establish a balance between exploration and exploitation. The proposed method is in two parts: First, we used the binary version of the TLBO algorithm for feature selection and added a mutation operator to implement a strong local search capability (BMTLBO). Second, we used a modified TLBO algorithm with the self-learning phase (SLTLBO) for training a neural network to show the application of the classification problem to evaluate the performance of the procedures of the method. We tested the proposed method on 14 datasets in terms of classification accuracy and the number of features. The results showed BMTLBO outperformed the standard TLBO algorithm and proved the potency of the proposed method. The results are very promising and close to optimal.
