Mitigating Stealthy Link Flooding DDoS Attacks Using SDN-Based Moving Target Defense

With the increasing diversity and complication of Distributed Denial-of-Service (DDoS) attacks, it has become extremely challenging to design a fully protected network. For instance, recently, a new type of attack called Stealthy Link Flooding Attack (SLFA) has been shown to cause critical network disconnection problems, where the attacker targets the communication links in the surrounding area of a server. The existing defense mechanisms for this type of attack are based on the detection of some unusual traffic patterns; however, this might be too late as some severe damage might already be done. These mechanisms also do not consider countermeasures during the reconnaissance phase of these attacks. Over the last few years, moving target defense (MTD) has received increasing attention from the research community. The idea is based on frequently changing the network configurations to make it much more difficult for the attackers to attack the network. In this dissertation, we investigate several novel frameworks based on MTD to defend against contemporary DDoS attacks. Specifically, we first introduce MTD against the data phase of SLFA, where the bots are sending data packets to target links. In this framework, we mitigate the traffic if the bandwidth of communication links exceeds the given threshold, and experimentally show that our method significantly alleviates the congestion. As a second work, we propose a framework that considers the reconnaissance phase of SLFA, where the attacker strives to discover critical communication links. We create virtual networks to deceive the attacker and provide forensic features. In our third work, we consider the legitimate network reconnaissance requests while keeping the attacker confused. To this end, we integrate cloud technologies as overlay networks to our system. We demonstrate that the developed mechanism preserves the security of the network information with negligible delays. Finally, we address the problem of identifying and potentially engaging with the attacker. We model the interaction between attackers and defenders into a game and derive a defense mechanism based on the equilibria of the game. We show that game-based mechanisms could provide similar protection against SLFAs like the extensive periodic MTD solution with significantly reduced overhead. The frameworks in this dissertation were verified with extensive experiments as well as with the theoretical analysis. The research in this dissertation has yielded several novel defense mechanisms that provide comprehensive protection against SLFA. Besides, we have shown that they can be integrated conveniently and efficiently to the current network infrastructure

Scalable schemes against Distributed Denial of Service attacks

Defense against Distributed Denial of Service (DDoS) attacks is one of the primary concerns on the Internet today. DDoS attacks are difficult to prevent because of the open, interconnected nature of the Internet and its underlying protocols, which can be used in several ways to deny service. Attackers hide their identity by using third parties such as private chat channels on IRC (Internet Relay Chat). They also insert false return IP address, spoofing, in a packet which makes it difficult for the victim to determine the packet\u27s origin. We propose three novel and realistic traceback mechanisms which offer many advantages over the existing schemes. All the three schemes take advantage of the Autonomous System topology and consider the fact that the attacker\u27s packets may traverse through a number of domains under different administrative control. Most of the traceback mechanisms make wrong assumptions that the network details of a company under an administrative control are disclosed to the public. For security reasons, this is not the case most of the times. The proposed schemes overcome this drawback by considering reconstruction at the inter and intra AS levels. Hierarchical Internet Traceback (HIT) and Simple Traceback Mechanism (STM) trace back to an attacker in two phases. In the first phase the attack originating Autonomous System is identified while in the second phase the attacker within an AS is identified. Both the schemes, HIT and STM, allow the victim to trace back to the attackers in a few seconds. Their computational overhead is very low and they scale to large distributed attacks with thousands of attackers. Fast Autonomous System Traceback allows complete attack path reconstruction with few packets. We use traceroute maps of real Internet topologies CAIDA\u27s skitter to simulate DDoS attacks and validate our design

An architectural approach for mitigating next-generation denial of service attacks

It is well known that distributed denial of service attacks are a major threat to the Internet today. Surveys of network operators repeatedly show that the Internet's stakeholders are concerned, and the reasons for this are clear: the frequency, magnitude, and complexity of attacks are growing, and show no signs of slowing down. With the emergence of the Internet of Things, fifth-generation mobile networks, and IPv6, the Internet may soon be exposed to a new generation of sophisticated and powerful DDoS attacks. But how did we get here? In one view, the potency of DDoS attacks is owed to a set of underlying architectural issues at the heart of the Internet. Guiding principles such as simplicity, openness, and autonomy have driven the Internet to be tremendously successful, but have the side effects of making it difficult to verify source addresses, classify unwanted packets, and forge cooperation between networks to stop traffic. These architectural issues make mitigating DDoS attacks a costly, uphill battle for victims, who have been left without an adequate defense. Such a circumstance requires a solution that is aware of, and addresses, the architectural issues at play. Fueled by over 20 years worth of lessons learned from the industry and academic literature, Gatekeeper is a mitigation system that neutralizes the issues that make DDoS attacks so powerful. It does so by enforcing a connection-oriented network layer and by leveraging a global distribution of upstream vantage points. Gatekeeper further distinguishes itself from previous solutions because it circumvents the necessity of mutual deployment between networks, allowing deployers to reap the full benefits alone and on day one. Gatekeeper is an open-source, production-quality DDoS mitigation system. It is modular, scalable, and built using the latest advances in packet processing techniques. It implements the operational features required by today's network administrators, including support for bonded network devices, VLAN tagging, and control plane tools, and has been chosen for deployment by multiple networks. However, an effective Gatekeeper deployment can only be achieved by writing and enforcing fine-grained and accurate network policies. While the basic function of such policies is to simply govern the sending ability of clients, Gatekeeper is capable of much more: multiple bandwidth limits, punishing flows for misbehavior, attack detection via machine learning, and the flexibility to support new protocols. Therefore, we provide a view into the richness and power of Gatekeeper policies in the form of a policy toolkit for network operators. Finally, we must look to the future, and prepare for a potential next generation of powerful and costly DDoS attacks to grace our infrastructure. In particular, link flooding attacks such as Crossfire use massive, distributed sets of bots with low-rate, legitimate-looking traffic to attack upstream links outside of the victim's control. A new generation of these attacks could soon be realized as IoT devices, 5G networks, and IPv6 simultaneously enter the network landscape. Gatekeeper is able to hinder the architectural advantages that fuel link flooding attacks, bounding their effectiveness

Understanding and Advancing the Status Quo of DDoS Defense

Two decades after the first distributed denial-of-service (DDoS) attack, the Internet remains challenged by DDoS attacks as they evolve. Not only is the scale of attacks larger than ever, but they are also harder to detect and mitigate. Nevertheless, the Internet's fundamental design, based on which machines are free to send traffic to any other machines, remains the same. This thesis reinvestigates the prior DDoS defense solutions to find less studied but critical issues in existing defense solutions. It proposes solutions to improve the input, design, and evaluation of DDoS defense. Specifically, we show why DDoS defense systems need a better view of the Internet's traffic at the autonomous system (AS) level. We use a novel attack to expose the inefficiencies in the existing defense systems. Finally, we reason why a defense solution needs a sound empirical evaluation and provide a framework that mimics real-world networks to facilitate DDoS defense evaluation. This dissertation includes published and unpublished co-authored materials

On modeling and mitigating new breed of dos attacks

Denial of Service (DoS) attacks pose serious threats to the Internet, exerting in tremendous impact on our daily lives that are heavily dependent on the good health of the Internet. This dissertation aims to achieve two objectives:1) to model new possibilities of the low rate DoS attacks; 2) to develop effective mitigation mechanisms to counter the threat from low rate DoS attacks. A new stealthy DDoS attack model referred to as the quiet attack is proposed in this dissertation. The attack traffic consists of TCP traffic only. Widely used botnets in today\u27s various attacks and newly introduced network feedback control are integral part of the quiet attack model. The quiet attack shows that short-lived TCP flows used as attack flows can be intentionally misused. This dissertation proposes another attack model referred to as the perfect storm which uses a combination of UDP and TCP. Better CAPTCHAs are highlighted as current defense against botnets to mitigate the quiet attack and the perfect storm. A novel time domain technique is proposed that relies on the time difference between subsequent packets of each flow to detect periodicity of the low rate DoS attack flow. An attacker can easily use different IP address spoofing techniques or botnets to launch a low rate DoS attack and fool the detection system. To mitigate such a threat, this dissertation proposes a second detection algorithm that detects the sudden increase in the traffic load of all the expired flows within a short period. In a network rate DoS attacks, it is shown that the traffic load of all the expired flows is less than certain thresholds, which are derived from real Internet traffic analysis. A novel filtering scheme is proposed to drop the low rate DoS attack packets. The simulation results confirm attack mitigation by using proposed technique. Future research directions will be briefly discussed