Know Your Enemy: Stealth Configuration-Information Gathering in SDN

Software Defined Networking (SDN) is a network architecture that aims at providing high flexibility through the separation of the network logic from the forwarding functions. The industry has already widely adopted SDN and researchers thoroughly analyzed its vulnerabilities, proposing solutions to improve its security. However, we believe important security aspects of SDN are still left uninvestigated. In this paper, we raise the concern of the possibility for an attacker to obtain knowledge about an SDN network. In particular, we introduce a novel attack, named Know Your Enemy (KYE), by means of which an attacker can gather vital information about the configuration of the network. This information ranges from the configuration of security tools, such as attack detection thresholds for network scanning, to general network policies like QoS and network virtualization. Additionally, we show that an attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk of being detected. We underline that the vulnerability exploited by the KYE attack is proper of SDN and is not present in legacy networks. To address the KYE attack, we also propose an active defense countermeasure based on network flows obfuscation, which considerably increases the complexity for a successful attack. Our solution offers provable security guarantees that can be tailored to the needs of the specific network under consideratio

MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example. The design of general defense strategies against a wide range of such attacks still remains a challenging problem. In this paper, we draw inspiration from the fields of cybersecurity and multi-agent systems and propose to leverage the concept of Moving Target Defense (MTD) in designing a meta-defense for 'boosting' the robustness of an ensemble of deep neural networks (DNNs) for visual classification tasks against such adversarial attacks. To classify an input image, a trained network is picked randomly from this set of networks by formulating the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg Game (BSG). We empirically show that this approach, MTDeep, reduces misclassification on perturbed images in various datasets such as MNIST, FashionMNIST, and ImageNet while maintaining high classification accuracy on legitimate test images. We then demonstrate that our framework, being the first meta-defense technique, can be used in conjunction with any existing defense mechanism to provide more resilience against adversarial attacks that can be afforded by these defense mechanisms. Lastly, to quantify the increase in robustness of an ensemble-based classification system when we use MTDeep, we analyze the properties of a set of DNNs and introduce the concept of differential immunity that formalizes the notion of attack transferability.Comment: Accepted to the Conference on Decision and Game Theory for Security (GameSec), 201