Jon Oberheide

Internet inter-domain traffic

In this paper, we examine changes in Internet inter-domain traffic demands and interconnection policies. We analyze more than 200 Exabytes of commercial Internet traffic over a two year period through the instrumentation of 110 large and geographically diverse cable operators, international transit backbones, regional networks and content providers. Our analysis shows significant changes in inter-AS traffic patterns and an evolution of provider peering strategies. Specifically, we find the majority of inter-domain traffic by volume now flows directly between large content providers, data center / CDNs and consumer networks. We also show significant changes in Internet application usage, including a global decline of P2P and a significant rise in video traffic. We conclude with estimates of the current size of the Internet by inter-domain traffic volume and rate of annualized inter-domain traffic growth.

Automated classification and analysis of internet malware

Numerous attacks, such as worms, phishing, and botnets, threaten the availability of the Internet, the integrity of its hosts, and the privacy of its users. A core element of defense against these attacks is anti-virus (AV) software--a service that detects, removes, and characterizes these threats. The ability of these products to successfully characterize these threats has far-reaching effects--from facilitating sharing across organizations, to detecting the emergence of new threats, and assessing risk in quarantine and cleanup. In this paper, we examine the ability of existing host-based anti-virus products to provide semantically meaningful information about the malicious software and tools (or malware) used by attackers. Using a large, recent collection of malware that spans a variety of attack vectors (e.g., spyware, worms, spam), we show that different AV products characterize malware in ways that are inconsistent across AV products, incomplete across malware, and that fail to be concise in their semantics. To address these limitations, we propose a new classification technique that describes malware behavior in terms of system state changes (e.g., files written, processes created) rather than in sequences or patterns of system calls. To address the sheer volume of malware and diversity of its behavior, we provide a method for automatically categorizing these profiles of malware into groups that reflect similar classes of behaviors and demonstrate how behavior-based clustering provides a more direct and effective way of classifying and analyzing Internet malware.

Virtualized in-cloud security services for mobile devices

Modern mobile devices continue to approach the capabilities and extensibility of standard desktop PCs. Unfortunately, these devices are also beginning to face many of the same security threats as desktops. Currently, mobile security solutions mirror the traditional desktop model in which they run detection services on the device. This approach is complex and resource intensive in both computation and power. This paper proposes a new model whereby mobile antivirus functionality is moved to an off-device network service employing multiple virtualized malware detection engines. Our argument is that it is possible to spend bandwidth resources to significantly reduce on-device CPU, memory, and power resources. We demonstrate how our in-cloud model enhances mobile security and reduces on-device software complexity, while allowing for new services such as platform-specific behavioral analysis engines. Our benchmarks on Nokias N800 and N95 mobile devices show that our mobile agent consumes an order of magnitude less CPU and memory while also consuming less power in common scenarios compared to existing on-device antivirus software.

Characterizing Dark DNS Behavior

Security researchers and network operators increasingly rely on information gathered from honeypots and sensors deployed on darknets, or unused address space, for attack detection. While the attack traffic gleaned from such deployments has been thoroughly scrutinized, little attention has been paid to DNS queries targeting these addresses. In this paper, we introduce the concept of dark DNS, the DNS queries associated with darknet addresses, and characterize the data collected from a large operational network by our dark DNS sensor. We discuss the implications of sensor evasion via DNS reconnaissance and emphasize the importance of reverse DNS authority when deploying darknet sensors to prevent attackers from easily evading monitored darknets. Finally, we present honeydns, a tool that complements existing network sensors and low-interaction honeypots by providing simple DNS services.