Defending Against IoT-Enabled DDoS Attacks at Critical Vantage Points on the Internet

The number of Internet of Things (IoT) devices continues to grow every year. Unfortunately, with the rise of IoT devices, the Internet is also witnessing a rise in the number and scale of IoT-enabled distributed denial-of-service (DDoS) attacks. However, there is a lack of network-based solutions targeted directly for IoT networks to address the problem of IoT-enabled DDoS. Unlike most security approaches for IoT which focus on hardening device security through hardware and/or software modification, which in many cases is infeasible, we introduce network-based approaches for addressing IoT-enabled DDoS attacks. We argue that in order to effectively defend the Internet against IoT-enabled DDoS attacks, it is necessary to consider network-wide defense at critical vantage points on the Internet. This dissertation is focused on three inherently connected and complimentary components: (1) preventing IoT devices from being turned into DDoS bots by inspecting traffic towards IoT networks at an upstream ISP/IXP, (2) detecting DDoS traffic leaving an IoT network by inspecting traffic at its gateway, and (3) mitigating attacks as close to the devices in an IoT network originating DDoS traffic. To this end, we present three security solutions to address the three aforementioned components to defend against IoT-enabled DDoS attacks

Android Malware Detection via Graphlet Sampling

Android systems are widely used in mobile & wireless distributed systems. In the near future, Android is believed to dominate the mobile distributed environment. However, with the popularity of Android-based smartphones/tablets comes the rampancy of Android-based malware. In this paper, we propose a novel topological signature of Android apps based on the function call graphs (FCGs) extracted from their Android App PacKages (APKs). Specifically, by leveraging recent advances on graphlet mining, the proposed method fully captures the invocator-invocatee relationship at local neighborhoods in an FCG without exponentially inflating the state space. Using real benign app and malware samples, we demonstrate that our method, ACTS (App topologiCal signature through graphleT Sampling), can detect malware and identify malware families robustly and efficiently. More importantly, we demonstrate that, without augmenting the FCG with any semantic features such as bytecode-based vertex typing, local topological information captured by ACTS alone can achieve a high malware detection accuracy. Since ACTS only uses structural features, which are orthogonal to semantic features, it is expected that combining them would give a greater improvement in malware detection accuracy than combining non-orthogonal semantic features

ACTS: Extracting Android App Topological Signature through Graphlet Sampling

Android systems are widely used in mobile & wireless distributed systems. In the near future, Android is believed to dominate the mobile distributed environment. However, with the popularity of Android-based smartphones/tablets comes the rampancy of Android-based malware. In this paper, we propose a novel topological signature of Android apps based on the function call graphs (FCGs) extracted from their Android App Packages (APKs). Specifically, by leveraging recent advances in graphlet sampling, the proposed method fully captures the invocator-invocatee relationship at local neighborhoods in an FCG without exponentially inflating the state space. Using real benign app and malware samples, we demonstrate that our method, ACTS (App topologiCal signature through graphleT Sampling), can detect malware and identify malware families robustly and efficiently. More importantly, we demonstrate that, without augmenting the FCG with any semantic features such as bytecode-based vertex typing, local topological information captured by ACTS alone can achieve a high malware detection accuracy. Since ACTS only uses structural features, which are orthogonal to semantic features, it is expected that combining them would give a greater improvement in malware detection accuracy than combining non-orthogonal semantic features