Biblio

We have been investigating methods for establishing an effective, immediate defense mechanism against the DDoS attacks on Web applications via hacker botnets, in which this defense mechanism can be immediately active without preparation time, e.g. for training data, usually asked for in existing proposals. In this study, we propose a new mechanism, including new data structures and algorithms, that allow the detection and filtering of large amounts of attack packets (Web request) based on monitoring and capturing the suspect groups of source IPs that can be sending packets at similar patterns, i.e. with very high and similar frequencies. The proposed algorithm places great emphasis on reducing storage space and processing time so it is promising to be effective in real-time attack response.

Denial-of-Service attack (DoS attack) is an attack on network in which an attacker tries to disrupt the availability of network resources by overwhelming the target network with attack packets. In DoS attack it is typically done using a single source, and in a Distributed Denial-of-Service attack (DDoS attack), like the name suggests, multiple sources are used to flood the incoming traffic of victim. Typically, such attacks use vulnerabilities of Domain Name System (DNS) protocol and IP spoofing to disrupt the normal functioning of service provider or Internet user. The attacks involving DNS, or attacks exploiting vulnerabilities of DNS are known as DNS based DDOS attacks. Many of the proposed DNS based DDoS solutions try to prevent/mitigate such attacks using some intelligent non-``network layer'' (typically application layer) protocols. Utilizing the flexibility and programmability aspects of Software Defined Networks (SDN), via this proposed doctoral research it is intended to make underlying network intelligent enough so as to prevent DNS based DDoS attacks.

Distributed denial of service (DDoS) attacks is a serious cyberattack that exhausts target machine's processing capacity by sending a huge number of packets from hijacked machines. To minimize resource consumption caused by DDoS attacks, filtering attack packets at source machines is the best approach. Although many studies have explored the detection of DDoS attacks, few studies have proposed DDoS attack prevention schemes that work at source machines. We propose a reliable, lightweight, transparent, and flexible DDoS attack prevention scheme that works at source machines. In this scheme, we employ a hypervisor with a packet filtering mechanism on each managed machine to allow the administrator to easily and reliably suppress packet transmissions. To make the proposed scheme lightweight and transparent, we exploit a thin hypervisor that allows pass-through access to hardware (except for network devices) from the operating system, thereby reducing virtualization overhead and avoiding compromising user experience. To make the proposed scheme flexible, we exploit a configurable packet filtering mechanism with a guaranteed safe code execution mechanism that allows the administrator to provide a filtering policy as executable code. In this study, we implemented the proposed scheme using BitVisor and the Berkeley Packet Filter. Experimental results show that the proposed scheme can suppress arbitrary packet transmissions with negligible latency and throughput overhead compared to a bare metal system without filtering mechanisms.

Server honey pots are computer systems that hide in a network capturing attack packets. As the name goes, server honey pots are installed in server machines running a set of services. Enterprises and government organisations deploy these honey pots to know the extent of attacks on their network. Since, most of the recent attacks are advanced persistent attacks there is much research work going on in building better peripheral security measures. In this paper, the authors have deployed several honey pots in a virtualized environment to gather traces of malicious activities. The network infrastructure is resilient and provides much information about hacker's activities. It is cost-effective and can be easily deployed in any organisation without specialized hardware.

Distributed Denial of Service (DDoS) attacks are one of the challenging network security problems to address. The existing defense mechanisms against DDoS attacks usually filter the attack traffic at the victim side. The problem is exacerbated when there are spoofed IP addresses in the attack packets. In this case, even if the attacking traffic can be filtered by the victim, the attacker may reach the goal of blocking the access to the victim by consuming the computing resources or by consuming a big portion of the bandwidth to the victim. This paper proposes a Trace back-based Defense against DDoS Flooding Attacks (TDFA) approach to counter this problem. TDFA consists of three main components: Detection, Trace back, and Traffic Control. In this approach, the goal is to place the packet filtering as close to the attack source as possible. In doing so, the traffic control component at the victim side aims to set up a limit on the packet forwarding rate to the victim. This mechanism effectively reduces the rate of forwarding the attack packets and therefore improves the throughput of the legitimate traffic. Our results based on real world data sets show that TDFA is effective to reduce the attack traffic and to defend the quality of service for the legitimate traffic.