Many techniques and algorithms are developed to enhance the security in the cloud environment. This helps the users to secure their server from malicious attacks. Hence the study and investigation of the performance enhanced security algorithms is a must demanded field in the research industry. When large number users using same server to store their information in cloud environment security is a must needed component to preserve the privacy and confidentiality of every individual user. This can be further strengthened by detecting the attacks in earlier stages and taking countermeasure to prevent the attack. Thus securing the data network without any leakage and loss of the information is a challenging task in the cloud environment. When the attacks or intrusion is detected after the occurrence there may be damage to the data in the form of data damage or theft. Hence it is necessary to predict and detect the attacks before the occurrence to protect the privacy and confidentiality of the user information. Keywords: Cloud security; Data privacy; Data confidentiality; Hash Algorithm; Substitutional encryption

Every day, millions of people in many institutions communicate with each other on the Internet. The past two decades have witnessed unprecedented levels of Internet use by people around the world. Almost alongside these rapid developments in the internet space, an ever increasing incidence of attacks carried out on the internet has been consistently reported every minute. In such a difficult environment, Anomaly Detection Systems (ADS) play an important role in monitoring and analyzing daily internet activities for security breaches and threats. However, the analytical data routinely generated from computer networks are usually of enormous size and of little use. This creates a major challenge for ADSs, who must examine all the functionality of a certain dataset to identify intrusive patterns. The selection of features is an important factor in modeling anomaly-based intrusion detection systems. An irrelevant characteristic can lead to overfitting which in turn negatively affects the modeling power of classification algorithms. The objective of this study is to analyze and select the most discriminating input characteristics for the construction of efficient and computationally efficient schemes for an ADS. In the first step, a heuristic algorithm called IG-BA is proposed for dimensionality reduction by selecting the optimal subset based on the concept of entropy. Then, the relevant and meaningful features are selected, before implementing Number of Classifiers which includes: (1) An irrelevant feature can lead to overfitting which in turn negatively affects the modeling power of the classification algorithms. Experiment was done on CICIDS-2017 dataset by applying (1) Random Forest (RF), (2) Bayes Network (BN), (3) Naive Bayes (NB), (4) J48 and (5) Random Tree (RT) with results showing better detection precision and faster execution time. The proposed heuristic algorithm outperforms the existing ones as it is more accurate in detection as well as faster. However, Random Forest algorithm emerges as the best classifier for feature selection technique and scores over others by virtue of its accuracy in optimal selection of features.

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