Advanced computing innovations are rapidly evolving, resulting in the advent of new organizational and operational strategies. Cloud computing has emerged as one of the pre-eminent innovation in the recent years. Cloud computing enables its clients to access flexible, distributed computing domain via internet. Cloud has manifested itself as a viable framework that facilitates the use of application domains, data and infrastructural facilities that mainly encompasses workstations, network and storage infrastructure. Regardless of robust and comprehensive server processing capabilities in contrast to client’s processing capabilities and efficiency there are numerous security risks to the cloud from both outside and within the cloud that might exploit security flaws to cause damage. Traditional security measures have some flaws when it comes to completely shielding the networks and devices from increasingly advanced attacks. Consequently, it is all important to build an intrusion detection system to detect and prevent all kinds of intrusions in the cloud with high accuracy along with low false alarms. In this study we have suggested an anomaly-based intrusion detection system that employs ML algorithms for detection of unknown malicious attacks using an ensemble approach over the UNSW-NB15 dataset. The experimental output demonstrated the accuracy of 99.28% and 99.47% for random forest and bagging algorithms respectively.
Due to the increased use of the internet, cyber-attacks are becoming more prominent causing major difficulty in achieving and preventing security risks and threats in the network. There have been a variety of attacks (both passive and aggressive) used to compromise network security and privacy. As a result, network security is becoming an increasingly important aspect in safe guarding and maintaining network data and resources to ensure dependable, secure access and protection against vulnerabilities. For detecting such attacks quickly and accurately, a strong Intrusion Detection System is required which is a valuable means for detecting intrusions in a network or system by extensively inspecting each packet in the network in real-time, preventing any harm to the user or system resources. In this paper, we proposed a statistical method to train the model with the training data to understand complicated patterns in the dataset and to make intelligent decisions or predictions whenever it comes across new or previously unseen data instances. For the classification of data, we used five machine learning classifiers such as Support Vector Machine, Decision Tree, Random Forest, AdaBoost, and Logistic Regression. To properly grasp complicated patterns in data, machine learning models require a large amount of data, which is why NSL-KDD was utilized to develop and validate supervised machine learning models. Initially, the dataset is pre-processed to remove any unnecessary or undesired dataset features. Feature selection (extra-treeclassifier) were used which combines the qualities of both filter and wrapper methods to provide features based on their importance as a result, the dataset dimensionality is reduced, lowering the processing complexity. Finally, the overall classification accuracy of the various machine learning classifiers was evaluated to find the best optimal algorithm for detecting intrusions.
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