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This paper is focused on the issue of malware detection for Android mobile system by Reverse Engineering of java code. The characteristics of malicious software were identified based on a collected set of applications. Total number of 1958 applications where tested (including 996 malware apps). A unique set of features was chosen. Five classification algorithms (Random Forest, SVM, K-NN, Nave Bayes, Logistic Regression) and three attribute selection algorithms were examined in order to choose those that would provide the most effective malware detection.

This research paper is focused on the issue of mobile application malware detection by Reverse Engineering of Android java code and use of Machine Learning algorithms. The malicious software characteristics were identified based on a collected set of total number of 1958 applications (including 996 malware applications). During research a unique set of features was chosen, then three attribute selection algorithms and five classification algorithms (Random Forest, K Nearest Neighbors, SVM, Nave Bayes and Logistic Regression) were examined to choose algorithms that would provide the most effective rate of malware detection.

Machine learning has more and more effect on our every day's life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.

The presence of androgen (AR) and estrogen (ER) receptors has been demonstrated both in normal perianal (hepatoid) glands and in perianal tumors. The aim of this study was to demonstrate the relationship between the expression of AR and ER in perianal gland tumors and the effectiveness of antihormonal treatment. The study was performed on 41 male dogs with neoplastic lesions of the anal region. Histopathological evaluation of the lesions revealed 24 adenomas, 12 epitheliomas, and five carcinomas. Treatment was administered orally with tamoxifen at a dose of 1 mg/kg BW and cyproterone acetate at a dose of 5 mg/kg. Tumor diameters were measured regularly with calipers and recorded in millimeters starting with the measurement before treatment, and then after 1, 2, 3, 6, 12, 18, and 24 months of therapy. The results show that hepatoid adenomas that are characterized by high expression of AR and ER receptors respond positively to antihormonal therapy, resulting in complete tumor regression. For locally malignant hepatoid epitheliomas and carcinomas with low expression of AR and ER receptors, antihormonal therapy makes it possible to reduce the size of the tumor, but does not make it possible to cure it completely.