Signature-based malware detection algorithms are facing challenges to cope with the massive number of threats in the Android environment. In this paper, conversation-level network traffic features are extracted and used in a supervised-based model. This model was used to enhance the process of Android malware detection, categorization, and family classification. The model employs the ensemble learning technique in order to select the most useful features among the extracted features. A real-world dataset called CICAndMal2017 was used in this paper. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75%, 79.97%, and 66.71%for malware detection, malware categorization, and malware family classification respectively. A comparison with another study that uses the same dataset was made. This study has achieved a significant enhancement in malware family classification and malware categorization. For malware family classification, the enhancement was 39.71% for precision and 41.09% for recall. The rate of enhancement for the Android malware categorization was 30.2% and 31.14‬% for precision and recall, respectively

Online communication offers organizations greater efficiency. However, online processes increase the threat level during message transfer. This necessitates researchers to develop and improve security protocols in order to enhance the security of communication lines. Despite the evaluation and acceptance of many authentication protocols, online communications remain insecure. we propose to add the user's physical location as a new authentication factor into Kerberos protocol and call it N-Kerberos protocol, and we validate the new form of Kerberos (N-Kerberos).

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