Peer-to-peer (P2P) botnet is one of the greatest threats to digital data. It has become a common tool for performing a lot of malicious activities such as DDoS attacks, phishing attacks, spreading spam, identity theft, ransomware, extortion attack, and many other fraudulent activities. P2P botnets are very resilient and stealthy and keep mutating to evade security mechanisms. Therefore, it has become necessary to identify and detect botnet flow from the normal flow. This paper uses supervised machine learning algorithms to detect P2P botnet flow. This paper also uses an ensemble learning technique to combine the performances of various supervised machine learning models to make predictions. To validate the results, four performance metrics have been used. These are accuracy, precision, recall, and F1-score. Experimental results show that the proposed approach delivers 99.99% accuracy, 99.81% precision, 99.11% recall, and 99.32% F1 score, which outperform the previous botnet detection approaches.
Long tenure of research and development (R&D) employees helps organisations to utilise employees' knowledge over a sustained time period and strengthen their competitive advantage. It also allows organisations to benefit from the training investments made on their R&D employees. Thus, identifying the determinants of R&D employees' tenure is crucial for designing effective R&D employee retention strategies. This paper analyses the factors explaining R&D employees' tenure in the subsidiaries of multinational corporations (MNCs). Building on institutional theory, we claim that formal and informal institutional distance between MNCs' home and host country might lead to R&D employees' short tenure in subsidiaries. We further suggest that R&D employees' international experience and MNCs' host country experience play a moderating role. We find support for our hypotheses by mobilising an original database that combines patent data and the LinkedIn profiles of 939 R&D employees in 256 MNC subsidiaries in India.
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