154 research outputs found
A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense
Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric
field of attack and defense, and shuffling-based MTD has been regarded as one
of the most effective ways to mitigate DDoS attacks. However, previous work
does not acknowledge that frequent shuffles would significantly intensify the
overhead. MTD requires a quantitative measure to compare the cost and
effectiveness of available adaptations and explore the best trade-off between
them. In this paper, therefore, we propose a new cost-effective shuffling
method against DDoS attacks using MTD. By exploiting Multi-Objective Markov
Decision Processes to model the interaction between the attacker and the
defender, and designing a cost-effective shuffling algorithm, we study the best
trade-off between the effectiveness and cost of shuffling in a given shuffling
scenario. Finally, simulation and experimentation on an experimental software
defined network (SDN) indicate that our approach imposes an acceptable
shuffling overload and is effective in mitigating DDoS attacks
A Survey of Network Requirements for Enabling Effective Cyber Deception
In the evolving landscape of cybersecurity, the utilization of cyber
deception has gained prominence as a proactive defense strategy against
sophisticated attacks. This paper presents a comprehensive survey that
investigates the crucial network requirements essential for the successful
implementation of effective cyber deception techniques. With a focus on diverse
network architectures and topologies, we delve into the intricate relationship
between network characteristics and the deployment of deception mechanisms.
This survey provides an in-depth analysis of prevailing cyber deception
frameworks, highlighting their strengths and limitations in meeting the
requirements for optimal efficacy. By synthesizing insights from both
theoretical and practical perspectives, we contribute to a comprehensive
understanding of the network prerequisites crucial for enabling robust and
adaptable cyber deception strategies
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
Towards an Uncertainty-Aware Adaptive Decision Engine for Self-Protecting Software: an POMDP-based Approach
The threats posed by evolving cyberattacks have led to increased research
related to software systems that can self-protect. One topic in this domain is
Moving Target Defense (MTD), which changes software characteristics in the
protected system to make it harder for attackers to exploit vulnerabilities.
However, MTD implementation and deployment are often impacted by run-time
uncertainties, and existing MTD decision-making solutions have neglected
uncertainty in model parameters and lack self-adaptation. This paper aims to
address this gap by proposing an approach for an uncertainty-aware and
self-adaptive MTD decision engine based on Partially Observable Markov Decision
Process and Bayesian Learning techniques. The proposed approach considers
uncertainty in both state and model parameters; thus, it has the potential to
better capture environmental variability and improve defense strategies. A
preliminary study is presented to highlight the potential effectiveness and
challenges of the proposed approach
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