Biblio

We recently see a real digital revolution where all companies prefer to use cloud computing because of its capability to offer a simplest way to deploy the needed services. However, this digital transformation has generated different security challenges as the privacy vulnerability against cyber-attacks. In this work we will present a new architecture of a hybrid Intrusion detection System, IDS for virtual private clouds, this architecture combines both network-based and host-based intrusion detection system to overcome the limitation of each other, in case the intruder bypassed the Network-based IDS and gained access to a host, in intend to enhance security in private cloud environments. We propose to use a non-traditional mechanism in the conception of the IDS (the detection engine). Machine learning, ML algorithms will can be used to build the IDS in both parts, to detect malicious traffic in the Network-based part as an additional layer for network security, and also detect anomalies in the Host-based part to provide more privacy and confidentiality in the virtual machine. It's not in our scope to train an Artificial Neural Network ”ANN”, but just to propose a new scheme for IDS based ANN, In our future work we will present all the details related to the architecture and parameters of the ANN, as well as the results of some real experiments.

Intrusion detection is one essential tool towards building secure and trustworthy Cloud computing environment, given the ubiquitous presence of cyber attacks that proliferate rapidly and morph dynamically. In our current working paradigm of resource, platform and service consolidations, Cloud Computing provides a significant improvement in the cost metrics via dynamic provisioning of IT services. Since almost all cloud computing networks lean on providing their services through Internet, they are prone to experience variety of security issues. Therefore, in cloud environments, it is necessary to deploy an Intrusion Detection System (IDS) to detect new and unknown attacks in addition to signature based known attacks, with high accuracy. In our deliberation we assume that a system or a network ``anomalous'' event is synonymous to an ``intrusion'' event when there is a significant departure in one or more underlying system or network activities. There are couple of recently proposed ideas that aim to develop a hybrid detection mechanism, combining advantages of signature-based detection schemes with the ability to detect unknown attacks based on anomalies. In this work, we propose a network based anomaly detection system at the Cloud Hypervisor level that utilizes a hybrid algorithm: a combination of K-means clustering algorithm and SVM classification algorithm, to improve the accuracy of the anomaly detection system. Dataset from UNSW-NB15 study is used to evaluate the proposed approach and results are compared with previous studies. The accuracy for our proposed K-means clustering model is slightly higher than others. However, the accuracy we obtained from the SVM model is still low for supervised techniques.