PhD thesis,
Attack-aware Security Function Management
Universität Würzburg, doctoralthesis, (January 2021)
Abstract
Over the last decades, cybersecurity has become an increasingly important issue. Between 2019 and 2011 alone, the losses from cyberattacks in the United States grew by 6217%. At the same time, attacks became not only more intensive but also more and more versatile and diverse. Cybersecurity has become everyone’s concern. Today, service providers require sophisticated and extensive security infrastructures comprising many security functions dedicated to various cyberattacks. Still, attacks become more violent to a level where infrastructures can no longer keep up. Simply scaling up is no longer sufficient. To address this challenge, in a whitepaper, the Cloud Security Alliance (CSA) proposed multiple work packages for security infrastructure, leveraging the possibilities of Software-defined Networking (SDN) and Network Function Virtualization (NFV).
Security functions require a more sophisticated modeling approach than regular network functions. Notably, the property to drop packets deemed malicious has a significant impact on Security Service Function Chains (SSFCs)—service chains consisting of multiple security functions to protect against multiple at- tack vectors. Under attack, the order of these chains influences the end-to-end system performance depending on the attack type. Unfortunately, it is hard to predict the attack composition at system design time. Thus, we make a case for dynamic attack-aware SSFC reordering. Also, we tackle the issues of the lack of integration between security functions and the surrounding network infrastructure, the insufficient use of short term CPU frequency boosting, and the lack of Intrusion Detection and Prevention Systems (IDPS) against database ransomware attacks.
Current works focus on characterizing the performance of security functions and their behavior under overload without considering the surrounding infrastructure. Other works aim at replacing security functions using network infrastructure features but do not consider integrating security functions within the network. Further publications deal with using SDN for security or how to deal with new vulnerabilities introduced through SDN. However, they do not take security function performance into account. NFV is a popular field for research dealing with frameworks, benchmarking methods, the combination with SDN, and implementing security functions as Virtualized Network
Functions (VNFs). Research in this area brought forth the concept of Service Function Chains (SFCs) that chain multiple network functions after one another. Nevertheless, they still do not consider the specifics of security functions. The mentioned CSA whitepaper proposes many valuable ideas but leaves their realization open to others.
This thesis presents solutions to increase the performance of single security functions using SDN, performance modeling, a framework for attack-aware SSFC reordering, a solution to make better use of CPU frequency boosting, and an IDPS against database ransomware.
Specifically, the primary contributions of this work are:
• We present approaches to dynamically bypass Intrusion Detection Systems (IDS) in order to increase their performance without reducing the security level. To this end, we develop and implement three SDN-based approaches (two dynamic and one static).
We evaluate the proposed approaches regarding security and performance and show that they significantly increase the performance com- pared to an inline IDS without significant security deficits. We show that using software switches can further increase the performance of the dynamic approaches up to a point where they can eliminate any throughput drawbacks when using the IDS.
• We design a DDoS Protection System (DPS) against TCP SYN flood at tacks in the form of a VNF that works inside an SDN-enabled network. This solution eliminates known scalability and performance drawbacks of existing solutions for this attack type.
Then, we evaluate this solution showing that it correctly handles the connection establishment and present solutions for an observed issue. Next, we evaluate the performance showing that our solution increases performance up to three times. Parallelization and parameter tuning yields another 76% performance boost. Based on these findings, we discuss optimal deployment strategies.
• We introduce the idea of attack-aware SSFC reordering and explain its impact in a theoretical scenario. Then, we discuss the required information to perform this process.
We validate our claim of the importance of the SSFC order by analyzing the behavior of single security functions and SSFCs. Based on the results, we conclude that there is a massive impact on the performance up to three orders of magnitude, and we find contradicting optimal orders
for different workloads. Thus, we demonstrate the need for dynamic reordering.
Last, we develop a model for SSFC regarding traffic composition and resource demands. We classify the traffic into multiple classes and model the effect of single security functions on the traffic and their generated resource demands as functions of the incoming network traffic. Based on our model, we propose three approaches to determine optimal orders for reordering.
• We implement a framework for attack-aware SSFC reordering based on this knowledge. The framework places all security functions inside an SDN-enabled network and reorders them using SDN flows.
Our evaluation shows that the framework can enforce all routes as desired. It correctly adapts to all attacks and returns to the original state after the attacks cease. We find possible security issues at the moment of reordering and present solutions to eliminate them.
• Next, we design and implement an approach to load balance servers while taking into account their ability to go into a state of Central Processing Unit (CPU) frequency boost. To this end, the approach collects temperature information from available hosts and places services on the host that can attain the boosted mode the longest.
We evaluate this approach and show its effectiveness. For high load scenarios, the approach increases the overall performance and the performance per watt. Even better results show up for low load workloads, where not only all performance metrics improve but also the temperatures and total power consumption decrease.
• Last, we design an IDPS protecting against database ransomware attacks that comprise multiple queries to attain their goal. Our solution models these attacks using a Colored Petri Net (CPN).
A proof-of-concept implementation shows that our approach is capable of detecting attacks without creating false positives for benign scenarios. Furthermore, our solution creates only a small performance impact.
Our contributions can help to improve the performance of security infrastructures. We see multiple application areas from data center operators over software and hardware developers to security and performance researchers. Most of the above-listed contributions found use in several research publications.
Regarding future work, we see the need to better integrate SDN-enabled security functions and SSFC reordering in data center networks. Future SSFC should discriminate between different traffic types, and security frameworks should support automatically learning models for security functions. We see the need to consider energy efficiency when regarding SSFCs and take CPU boosting technologies into account when designing performance models as well as placement, scaling, and deployment strategies. Last, for a faster adaptation against recent ransomware attacks, we propose machine-assisted learning for database IDPS signatures.
