With the enormous rise in the usage of computer networks, the necessity for safeguarding these networks is also increased. Network intrusion detection systems (NIDS) are designed to monitor and inspect the activities in a network. NIDS mainly depends on the features of the input network data as these features give information on the behaviour nature of the network traffic. The irrelevant and redundant network features negatively affect the efficacy and quality of NIDS, particularly its classification accuracy, detection time and processing complexity. In this paper, several feature selection techniques are applied to optimize the efficiency of NIDS. The categories of the applied feature selection techniques are the filter, wrapper and hybrid. Support vector machine (SVM) is employed as the detection model to classify the network connections behaviour into normal and abnormal traffic. NIDS is trained and tested on the benchmark NSL-KDD dataset. The performance of the applied feature selection techniques is compared with each other and the results are discussed. Evaluation results demonstrated the superiority of the wrapper techniques in providing the highest classification accuracy with the lowest detection time and false alarms of the NIDS.

Cloud computing provides and delivers a pool of on-demand and configurable resources and services that are delivered across the usage of the internet. Providing privacy and security to protect cloud assets and resources still a very challenging issue, since the distributed architecture of the cloud makes it vulnerable to the intruders. To mitigate this issue, intrusion detection systems (IDSs) play an important role in detecting the attacks in the cloud environment. In this paper, an anomaly-based network intrusion detection system (NIDS) is proposed which can monitor and analyze the network traffics flow that targets a cloud environment. The network administrator should be notified about the nature of these traffics to drop and block any intrusive network connections. Support vector machine (SVM) is employed as the classifier of the network connections. The binary-based Particle Swarm Optimization (BPSO) is adopted for selecting the most relevant network features, while the standard-based Particle Swarm Optimization (SPSO) is adopted for tuning the SVM control parameters. The benchmark NSL-KDD dataset is used as the network data source to build and evaluate the proposed system. Acceptable evaluation results state that the proposed system is characterized by detecting the intrusive network connections with high detection accuracy and low false alarm rates (FARs).

Cloud computing has its characteristics along with some important issues that should be handled to improve the performance and increase the efficiency of the cloud platform. These issues are related to resources management, fault tolerance, and security. The purpose of this research is to handle the resource management problem, which is to allocate and schedule virtual machines of cloud computing in a way that help providers to reduce makespan time of tasks. In this paper, a hybrid algorithm for dynamic tasks scheduling over cloud's virtual machines is introduced. This hybrid algorithm merges the behaviors of three effective techniques from the swarm intelligence techniques that are used to find a near optimal solution to difficult combinatorial problems. It exploits the advantages of ant colony behavior, the behavior of particle swarm and honeybee foraging behavior. Experimental results reinforce the strength of the proposed hybrid algorithm. They also prove that the proposed hybrid algorithm is the best and outperformed ant colony optimization, particle swarm optimization, artificial bee colony and other known algorithms.

Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. With the increasing demand and benefits of cloud computing infrastructure, different computing can be performed on cloud environment. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimization algorithm for load balancing compared with different scheduling algorithms has been proposed. Ant Colony Optimization (ACO) is random optimization search approach that will be used for allocating the incoming jobs to the virtual machines. The main contribution of our work is to balance the system load while trying to minimizing the make span of a given tasks set. The load balancing factor, related to the job finishing rate, is proposed to make the job finishing rate at different resource being similar and the ability of the load balancing will be improved. The proposed scheduling strategy was simulated using Cloudsim toolkit package. Experimental results showed that, the proposed algorithm outperformed scheduling algorithms that are based on the basic ACO or Modified Ant Colony Optimization (MACO).