Biblio

In today's world the number of consumers of cloud computing is increasing day by day. So, security is a big concern for cloud computing environment to keep user's data safe and secure. Among different types of attacks in cloud one of the harmful and frequently occurred attack is Distributed Denial of Service (DDoS) attack. DDoS is one type of flooding attack which is initiated by sending a large number of invalid packets to limit the services of the victim server. As a result, server can not serve the legitimate requests. DDoS attack can be done by a lot of strategies like malformed packets, IP spoofing, smurf attack, teardrop attack, syn flood attack, local area network denial (LAND) attack etc. This paper focuses on IP spoofing and LAND based DDoS attack. The objective of this paper is to propose an algorithm to detect and prevent IP spoofing and LAND attack. To achieve this objective a new approach is proposed combining two existing solutions of DDoS attack caused by IP spoofing and ill-formed packets. The proposed approach will provide a transparent solution, filter out the spoofed packets and minimize memory exhaustion through minimizing the number of insertions and updates required in the datatable. Finally, the approach is implemented and simulated using CloudSim 3.0 toolkit (a virtual cloud environment) followed by result analysis and comparison with existing algorithms.

SYN flood attack is a very serious cause for disturbing the normal traffic in MANET. SYN flood attack takes advantage of the congestion caused by populating a specific route with unwanted traffic that results in the denial of services. In this paper, we proposed an Adaptive Detection Mechanism using Artificial Intelligence technique named as SYN Flood Attack Detection Based on Bayes Estimator (SFADBE) for Mobile ad hoc Network (MANET). In SFADBE, every node will gather the current information of the available channel and the secure and congested free (Best Path) channel for the traffic is selected. Due to constant congestion, the availability of the data path can be the cause of SYN Flood attack. By using this AI technique, we experienced the SYN Flood detection probability more than the others did. Simulation results show that our proposed SFADBE algorithm is low cost and robust as compared to the other existing approaches.

Distributed Denial of Service (DDoS) attacks remain one of the top threats to enterprise networks and ISPs nowadays. It can cause tremendous damage by bringing down online websites or services. Existing DDoS defense solutions either brings high cost such as upgrading existing firewall or IPS, or bring excessive traffic delay by using third-party cloud-based DDoS filtering services. In this work, we propose a DDoS defense framework that utilizes Network Function Virtualization (NFV) architecture to provide low cost and highly flexible solutions for enterprises. In particular, the system uses virtual network agents to perform attack traffic filtering before they are forwarded to the target server. Agents are created on demand to verify the authenticity of the source of packets, and drop spoofed packets in order protect the target server. Furthermore, we design a scalable and flexible dispatcher to forward packets to corresponding agents for processing. A bucket-based forwarding mechanism is used to improve the scalability of the dispatcher through batching forwarding. The dispatcher can also adapt to agent addition and removal. Our simulation results demonstrate that the dispatcher can effectively serve a large volume of traffic with low dropping rate. The system can successfully mitigate SYN flood attack by introducing minimal performance degradation to legitimate traffic.

The Network Intrusion Detection Systems (NIDS) are either signature based or anomaly based. In this paper presented NIDS system belongs to anomaly based Neural Network Intrusion Detection System (NNIDS). The proposed NNIDS is able to successfully recognize learned malicious activities in a network environment. It was tested for the SYN flood attack, UDP flood attack, nMap scanning attack, and also for non-malicious communication.