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Intrusion detection system (IDS) as one of huge research problem in network security is the most effective tool of protection. It is a method of parsing network traffic data to detect security abuses. Data mining can play a very significant role in evolving an IDS. The dataset of IDSs or soft computing techniques based IDS can be classified into normal and abnormal traffic in order for generated alerts to detect threats. In this paper, we utilised the most common classification algorithms: Decision Tree (J48), Naive Bayes, OneR, and K-Nearest Neighbour (K-NN). These algorithms were chosen after investigating the most effective classification algorithms that are widely used. The aim of this study is to present a comparative study for the performance of each system that was gained from our previous experiments: SnortIDS, SuricataIDS, FL-SnortIDS, and FL-SuricataIDS in order to test which classifier algorithm is the best for our systems results, and investigate which system presents significant results. The performance of these classification algorithms was evaluated using 10-fold cross validation. Experiments and assessments of these methods were performed in the WEKA environment using the ISCX dataset.

Cloud computing is a distributed architecture that has shared resources, software, and information. There exists a great number of implementations and research for Intrusion Detection Systems (IDS) in grid and cloud environments, however they are limited in addressing the requirements for an ideal intrusion detection system. Security issues in Cloud Computing (CC) have become a major concern to its users, availability being one of the key security issues. Distributed Denial of Service (DDoS) is one of these security issues that poses a great threat to the availability of the cloud services. The aim of this research is to evaluate the performance of IDS in CC when the DDoS attack is detected in a private cloud, named Saa SCloud. A model has been implemented on three virtual machines, Saa SCloud Model, DDoS attack Model, and IDSServer Model. Through this implementation, Service Intrusion Detection System in Cloud Computing (SIDSCC) will be proposed, investigated and evaluated.

Cloud computing is a fast growing IT model for the exchange and delivery of different services through the Internet. However there is a plethora of security concerns in cloud computing which still need to be tackled (e.g. confidentiality, auditability and Privileged User Access). To detect and prevent such issues, the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) are effective mechanism against attacks such as SQL Injection. This study proposes a new service of IPS that prevents SQL injections when it comes over cloud computing website(CCW) using signature-based devices approach. A model has been implemented on three virtual machines. Through this implementation, a service-based intrusion prevention system in cloud computing (SIPSCC) is proposed, investigated and evaluated from three perspectives the vulnerability detection, average time, and false positives.