The design of the IP protocol makes it difficult to reliably identify the originator of an IP packet. Even in the absence of any deliberate attempt to disguise a packet's origin, wide-spread packet forwarding techniques such as NAT and encapsulation may obscure the packet's true source. Techniques have been developed to determine the source of large packet flows, but, to date, no system has been presented to track individual packets in an efficient, scalable fashion.We present a hash-based technique for IP traceback that generates audit trails for traffic within the network, and can trace the origin of a single IP packet delivered by the network in the recent past. We demonstrate that the system is effective, space-efficient (requiring approximately 0.5% of the link capacity per unit time in storage), and implementable in current or next-generation routing hardware. We present both analytic and simulation results showing the system's effectiveness.

Abstract-The design of the IP protocol makes it difficult to reliably identify the originator of an IP packet. Even in the absence of any deliberate attempt to disguise a packet's origin, wide-spread packet forwarding techniques such as NAT and encapsulation may obscure the packet's true source. Techniques have been developed to determine the source of large packet flows, but, to date, no system has been presented to track individual packets in an efficient, scalable fashion. We present a hash-based technique for IP traceback that generates audit trails for traffic within the network, and can trace the origin of a single IP packet delivered by the network in the recent past. We demonstrate that the system is effective, space-efficient (requiring approximately 0.5% of the link capacity per unit time in storage), and implementable in current or next-generation routing hardware. We present both analytic and simulation results showing the system's effectiveness.

To date, techniques to couanter cyber-attacks have predominantly been reactive; they focus on monitoring network traffic, detecting anomalies and cyber-attack traffic patters, and, a posteriori, combating the cyber-attacks and riitigating their effects. Contrary to such approclhes, we advocate proactively detecting and identifying botnets prior to their being used as part ofa cyber-attack [12]. In this paper, we present our work on using machine leamring-based classification techniques to identify the coniniand and con-

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