Recent Papers

ABSTRACT: Security and Privacy are the biggest issues in widespread cloud systems due to increasing number of Internet-connected devices. A secure cloud system is a major concern for everyone includes government, consumers and business. However attacks on any system are never completely stopped, as a result, real time attacks and threats detection become essential for effective system defense. Intrusion Detection System(IDS) is an enhanced mechanism which is used to control the traffic within the networks and to detect the abnormal activities. Only limited numbers of research works were done on Intrusion Detection Systems (IDS) for Internet of Things (IoT) and cloud. To solve these issues, certain solutions have been designed to improve the security of cloud while monitoring the networks, services and resources and to detect the attacks. On the other hand, Machine Learning techniques are capable for the identification of unknown and known attacks. Over the years, different ML algorithms are used for IDS. However, still there is a lot of scope to achieve better performance for IDS. To fulfill this gap, a novel Efficient Intrusion detection system in cloud using Hybrid Machine Learning classifier is presented. The combination of Support Vector Machine (SVM) with Artificial Neural Network (ANN) is presented. The performance of presented IDS is evaluated in terms of Accuracy, F1-score, Recall and Precision.

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