In the SETTI architecture, we design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and ASelf-MDS.
In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment.
Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19\% accuracy when compared with the state-of-the-art method in the literature.
We test our experiments in a different type of features: API, intent, and permission features on these three datasets.
We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix.
Ever increasing number of Android malware, has always been a concern for cybersecurity professionals.
In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost.