Biblio

Named Data Networking (NDN) is the most mature proposal of the Information Centric Networking paradigm, a clean-slate approach for the Future Internet. Although NDN was designed to tackle security issues inherent to IP networks natively, newly introduced security attacks in its transitional phase threaten NDN's practical deployment. Therefore, a security monitoring plane for NDN is indispensable before any potential deployment of this novel architecture in an operating context by any provider. We propose an approach for the monitoring and anomaly detection in NDN nodes leveraging Bayesian Network techniques. A list of monitored metrics is introduced as a quantitative measure to feature the behavior of an NDN node. By leveraging the hypothesis testing theory, a micro detector is developed to detect whenever the metric significantly changes from its normal behavior. A Bayesian network structure that correlates alarms from micro detectors is designed based on the expert knowledge of the NDN specification and the NFD implementation. The relevance and performance of our security monitoring approach are demonstrated by considering the Content Poisoning Attack (CPA), one of the most critical attacks in NDN, through numerous experiment data collected from a real NDN deployment.

Today, beyond a legitimate usage, the numerous advantages of cloud computing are exploited by attackers, and Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use. Such a phenomena is a major issue since it strongly increases the power of distributed massive attacks while involving the responsibility of cloud service providers that do not own appropriate solutions. In this paper, we present an original approach that enables a source-based de- tection of UDP-flood DDoS attacks based on a distributed system behavior analysis. Based on a principal component analysis, our contribution consists in: (1) defining the involvement of system metrics in a botcoud's behavior, (2) showing the invariability of the factorial space that defines a botcloud activity and (3) among several legitimate activities, using this factorial space to enable a botcloud detection.

Today, beyond a legitimate usage, the numerous advantages of cloud computing are exploited by attackers, and Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use. Such a phenomena is a major issue since it strongly increases the power of distributed massive attacks while involving the responsibility of cloud service providers that do not own appropriate solutions. In this paper, we present an original approach that enables a source-based de- tection of UDP-flood DDoS attacks based on a distributed system behavior analysis. Based on a principal component analysis, our contribution consists in: (1) defining the involvement of system metrics in a botcoud's behavior, (2) showing the invariability of the factorial space that defines a botcloud activity and (3) among several legitimate activities, using this factorial space to enable a botcloud detection.

Cloud computing is gaining ground and becoming one of the fast growing segments of the IT industry. However, if its numerous advantages are mainly used to support a legitimate activity, it is now exploited for a use it was not meant for: malicious users leverage its power and fast provisioning to turn it into an attack support. Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use since they can be setup on demand and at very large scale without requiring a long dissemination phase nor an expensive deployment costs. For cloud service providers, preventing their infrastructure from being turned into an Attack as a Service delivery model is very challenging since it requires detecting threats at the source, in a highly dynamic and heterogeneous environment. In this paper, we present the result of an experiment campaign we performed in order to understand the operational behavior of a botcloud used for a DDoS attack. The originality of our work resides in the consideration of system metrics that, while never considered for state-of-the-art botnets detection, can be leveraged in the context of a cloud to enable a source based detection. Our study considers both attacks based on TCP-flood and UDP-storm and for each of them, we provide statistical results based on a principal component analysis, that highlight the recognizable behavior of a botcloud as compared to other legitimate workloads.

Cloud computing is gaining ground and becoming one of the fast growing segments of the IT industry. However, if its numerous advantages are mainly used to support a legitimate activity, it is now exploited for a use it was not meant for: malicious users leverage its power and fast provisioning to turn it into an attack support. Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use since they can be setup on demand and at very large scale without requiring a long dissemination phase nor an expensive deployment costs. For cloud service providers, preventing their infrastructure from being turned into an Attack as a Service delivery model is very challenging since it requires detecting threats at the source, in a highly dynamic and heterogeneous environment. In this paper, we present the result of an experiment campaign we performed in order to understand the operational behavior of a botcloud used for a DDoS attack. The originality of our work resides in the consideration of system metrics that, while never considered for state-of-the-art botnets detection, can be leveraged in the context of a cloud to enable a source based detection. Our study considers both attacks based on TCP-flood and UDP-storm and for each of them, we provide statistical results based on a principal component analysis, that highlight the recognizable behavior of a botcloud as compared to other legitimate workloads.