Reproducing the effects of large-scale worm attacks in a laboratory setup in a realistic and reproducible manner is an important issue for the development of worm detection and defense systems. In this paper, we describe a worm simulation model we are developing to accurately model the largescale spread dynamics of a worm and many aspects of its detailed effects on the network. We can model slow or fast worms with realistic scan rates on realistic IP address spaces and selectively model local detailed network behavior. We show how it can be used to generate realistic input traffic for a working prototype worm detection and tracking system, the Dartmouth ICMP BCC: System/Tracking and Fusion Engine (DIB:S/TRAFEN), allowing performance evaluation of the system under realistic conditions. Thus, we can answer important design questions relating to necessary detector coverage and noise filtering without deploying and operating a full system. Our experiments indicate that the tracking algorithms currently implemented in the DIB:S/TRAFEN system could detect attacks such as Code Red v2 and Sapphire/Slammer very early, even when monitoring a quite limited portion of the address space, but more sophisticated algorithms are being constructed to reduce the risk of false positives in the presence of significant "background noise" scanning.

Large-scale worm infestations, such as last year 's Code Red, Code Red II, and Nimda, have

Abstract-As mobile phones increasingly become the target of propagating malware, their use of direct pair-wise communication mechanisms, such as Bluetooth and WiFi, pose considerable challenges to malware detection and mitigation. Unlike malware that propagates using the network, where the provider can employ centralized defenses, proximity malware can propagate in an entirely distributed fashion. In this paper we consider the dynamics of mobile phone malware that propagates by proximity contact, and we evaluate potential defenses against it. Defending against proximity malware is particularly challenging since it is difficult to piece together global dynamics from just pair-wise device interactions. Whereas traditional network defenses depend upon observing aggregated network activity to detect correlated or anomalous behavior, proximity malware detection must begin at the device. As a result, we explore three strategies for detecting and mitigating proximity malware that span the spectrum from simple local detection to a globally coordinated defense. Using insight from a combination of real-world traces, analytic epidemic models, and synthetic mobility models, we simulate proximity malware propagation and defense at the scale of a university campus. We find that local proximity-based dissemination of signatures can limit malware propagation. Globally coordinated strategies with broadcast dissemination are substantially more effective, but rely upon more demanding infrastructure within the provider.

The RINSE simulator is being developed to support large-scale network security preparedness and training exercises, involving hundreds of players and a modeled network composed of hundreds of LANs. The simulator must be able to present a realistic rendering of network behavior as attacks are launched and players diagnose events and try counter measures to keep network services operating. We describe the architecture and function of RINSE and outline how techniques like multiresolution traffic modeling and new routing simulation methods are used to address the scalability challenges of this application. We also describe in more detail new work on CPU/memory models necessary for the exercise scenarios and a latency absorption technique that will help when extending the range of client tools usable by the players.

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