Abstract: Disclosed herein are systems and methods for detecting anomalous behavior (e.g., attacks) in devices within a network. In an exemplary aspect, a method includes intercepting a first plurality of packets being transmitted in a network with a plurality of devices; identifying, from the first plurality of packets, a subset of packets corresponding to a device of the network; extracting a plurality of deterministic features from the subset of packets; calculating, based on the subset of packets, a risk score associated with the device based on a deviation of the features from a deterministic profile of the device, a first probability of the subset of packets exhibiting anomalous behavior based on a per-device model, and a second probability of the plurality of packets exhibiting anomalous behavior based on a network model; classifying anomalies into attack categories, and executing a remediation action to resolve anomalous behavior in the device.

Abstract: Disclosed herein are systems and methods for selecting files for malware analysis. In one aspect, a method may include identifying, in a cloud network, a backup of a client machine; extracting, from the backup, at least one file of a given file type; determining whether to include the at least one file in a sandbox of the cloud network by performing a static analysis of the at least one file; selecting the at least one file for inclusion in the sandbox based on the static analysis; monitoring, for a period of time, a behavior of the at least one file in the sandbox by performing a dynamic analysis of the at least one file; and in response to determining that the at least one file is malicious based on the dynamic analysis, performing a remediation action on the at least one file.

Abstract: A traceback solution is provided. For a network of autonomous systems, the traceback solution traces the autonomous system path taken by traffic flows. Every link in the traceback path is created, verified, and audited by autonomous systems. Multiple autonomous systems may take part in the process, making the system robust against fake information. The database used to store the validated traceback paths is a decentralized and distributed storage. Multiple copies of the database may be maintained by the network of autonomous systems. The database may be accessible by any participating autonomous system; and is not accessible from outside the network of autonomous systems. The traceback solution achieves both validation and non-repudiation property among the ASes. The traceback solution mitigates some important attack scenarios that might be targeted specifically at the traceback solution.

Abstract: A method for IP traceback is provided comprising receiving a traceback request including the identity of a traceback-deployed autonomous system closest to the destination node in a network routing path, recursively querying a traceback server associated with the traceback-deployed autonomous system to receive the identity of a preceding traceback-deployed autonomous system in the network routing path, and determining the network routing path based on the received identities of traceback-deployed autonomous systems. Additionally, authentication for traceback request is achieved using token delivery, wherein token is fragmented and marking of a packet is performed when a field on the packet matches at least one token fragment.

Abstract: A traceback solution is provided. For a network of autonomous systems, the traceback solution traces the autonomous system path taken by traffic flows. Every link in the traceback path is created, verified, and audited by autonomous systems. Multiple autonomous systems may take part in the process, making the system robust against fake information. The database used to store the validated traceback paths is a decentralized and distributed storage. Multiple copies of the database may be maintained by the network of autonomous systems. The database may be accessible by any participating autonomous system; and is not accessible from outside the network of autonomous systems. The traceback solution achieves both validation and non-repudiation property among the ASes. The traceback solution mitigates some important attack scenarios that might be targeted specifically at the traceback solution.

Abstract: A method, a computer-readable medium, and an apparatus for IP traceback are provided. The apparatus may generate a group public key shared by a plurality of routers controlled by a service provider. The apparatus may generate a unique private signing key for a router of the plurality of routers. The private signing key may be used to generate a group signature for a session of network traffic. The group public key may be applied to the group signature to identify the service provider. The apparatus may identify the router by applying the group public key and a master secret key to the group signature. The apparatus may deploy preventive or mitigate action on the identified router.

Abstract: A method for IP traceback is provided comprising receiving a traceback request including the identity of a traceback-deployed autonomous system closest to the destination node in a network routing path, recursively querying a traceback server associated with the traceback-deployed autonomous system to receive the identity of a preceding traceback-deployed autonomous system in the network routing path, and determining the network routing path based on the received identities of traceback-deployed autonomous systems. Additionally, authentication for traceback request is achieved using token delivery, wherein token is fragmented and marking of a packet is performed when a field on the packet matches at least one token fragment.