{'fraction_non_alphanumeric': 0.019522776572668113, 'fraction_numerical': 0.0, 'mean_word_length': 5.786764705882353, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Security Attacks Prediction Model for Cloud Computing

Cloud computing has become an indispensable computing platform of today's Information Communication Technology (ICT) world. The virtual environment provided by the cloud computing applications is neither under control of Cloud Service Providers (CSP) or users, completely. Consequently, it elicits the challenge of preventing security breaches considering that the data shared or stored in cloud computing environment can be crucial in nature. It is required to develop a system with capabilities to anticipate future security attacks. In this paper, we propose a security attacks prediction model which is based on Bayesian Networks, a probabilistic graphical model. The proposed Security Attacks Prediction Model (SAPM) takes vulnerable root causes of security loopholes, processes them through the Bayesian network and predicts the probability of security attacks.