Authorship attribution has become increasingly accurate, posing a serious privacy risk for programmers who wish to remain anonymous. In this article, we introduce SHIELD to examine the robustness of different code authorship attribution approaches against adversarial code examples. We define four attacks on attribution techniques, which include targeted and non-targeted attacks, and realize them using adversarial code perturbation. We experimented with a dataset of 200 programmers from the Google Code Jam competition to validate our methods. We target six state-of-the-art authorship attribution methods that adopt various techniques for extracting authorship traits from source code, including RNN, CNN, and code stylometry. Our experiments demonstrate the vulnerability of current authorship attribution methods against adversarial attacks. For the non-targeted attack, our experiments demonstrate the vulnerability of current authorship attribution methods against the attack with an attack success rate exceeding 98. 5% accompanied by a degradation of the identification confidence exceeding 13%. For targeted attacks, we show the possibility of impersonating a programmer using targeted adversarial perturbations with a success rate ranging from 66% to 88% for different authorship attribution techniques under several adversarial scenarios.

In-network/in-switch Access Control List (ACL) is an essential security component of modern networks. In high-speed networks, ACL rules are often placed in a switch's Ternary Content-Addressable Memory (TCAM) for timely ACL match-action and management (e.g. insertion and deletion). However, TCAM-based ACL systems are encountering an scalability issue owing to increasing demand on AI-powered autonomous defenses that detect and block attacks online, which inevitably derives finer-grained ACL rules. Existing solutions minimize the TCAM usage by partially offloading ACL matching into larger Static Random-Access Memory (SRAM) or customized hardware. Nevertheless, current SRAM-based solutions induce high management costs, especially a high rule-deployment latency, which delays time-sensitive defense actions. Also, the customized hardware approaches have its own scalability issue. To support autonomous defenses at a scale, in this paper, we propose an in-switch ACL system called PortCatcher, which breaks the trade-off between scalability and rule management latency. System-wise, we detach layer-4 port matching from TCAM for improving its memory efficiency. Algorithm-wise, we introduce a novel port (range) rule representation concept, called linear range map (LRM), which enables port (range) matching in SRAM-based hash tables. LRM guarantees not only fast and scalable port matching but also low-latency ACL management for timely defenses. With real-world ACL datasets, we show that PortCatcher saves 74%-90% TCAM space compared to state-of-the-art approaches by adding small overhead to SRAM (0.49 SRAM entry per ACL rule). Also, we deploy PortCatcher on a programmable switch to demonstrate that PortCatcher can serve 5-tuple rule matching at a line rate, where port rules are completely matched in SRAM. With a use case study, namely autonomous attack mitigation, we show that PortCatcher has a negligible rule management latency to block attack flows (i.e. 94.42% of rules deployed within 10 ms).

Cardinality estimation has become an essential building block of modern network monitoring systems due to the increasing concerns of cyberattacks (e.g., Denial-of-Service, worm, spammer, scanner, etc.). However, the ever-increasing attack scale and the diversity of patterns (i.e., flow size distribution) will produce a biased estimation of existing solutions if apply a monotonic hypothesis for network traffic. The most representative solution is virtual HyperLogLog (vHLL), which extended the proven HLL, a single element cardinality estimation solution, to a multi-tenant version using a memory random sharing and noise elimination approach. In this paper, we show that the assumption made by vHLL’s does not work for large-scale network traffic with diverse flow distributions. To resolve the issue, we propose a novel noise elimination method, called Rank Recovery-based Spread Estimator (RRSE), which is tolerant to both attack and normal traffic scenarios while using limited computation and storage. We show that our recovery function is more reliable than state-of-the-art approaches. Moreover, we implemented RRSE in a programmable switch to show the feasibility.

Abstract As augmented reality technologies develop, real-time interactions between objects present in the real world and virtual space are required. Generally, recognition and location estimation in augmented reality are carried out using tracking techniques, typically markers. However, using markers creates spatial constraints in simultaneous tracking of space and objects. Therefore, we propose a system that enables camera tracking in the real world and visualizes virtual visual information through the recognition and positioning of objects. We scanned the space using an RGB-D camera. A three-dimensional (3D) dense point cloud map is created using point clouds generated through video images. Among the generated point cloud information, objects are detected and retrieved based on the pre-learned data. Finally, using the predicted pose of the detected objects, other information may be augmented. Our system estimates object recognition and 3D pose based on simple camera information, enabling the viewing of virtual visual information based on object location.

In this paper, we provide the design and implementation of UOIT, a text entry method optimized for smartwatches. UOIT uses only one page where a user can see and tap directly for entry without any additional actions, such as zoom-in/zoom-out and swipes, which are required in the existing entry methods. To fully utilize the constrained screen space and to address the “fat finger” problem, we use a technique called “drawing-like typing”, which reduces the 26 small alphabetic keys into 13 large keys with a dual input property. To evaluate the performance of UOIT, we conducted two user studies while varying the learning period. In the short-term experiments (i.e., two days), we observed a fast learning curve of users when using the UOIT keyboard. Moreover, with the long-term experiments (i.e., a month), we show that users can type as fast as QWERTY keyboard but with much less errors. Moreover, UOIT outperforms the state-of-the-art keyboard in both speed and error rate.

We introduce DigitalSeal, a transaction authentication tool that works in both online and offline use scenarios. Digi-talSeal is a digital scanner that reads transaction information sent by an issuing entity of the DigitalSeal reader for authentication, and the information is encoded using a specially crafted bar-code. DigitalSeal views various pieces of transaction information for users to verify and proceed with transaction authentication. DigitalSeal is generic, and is capable of reading information viewed on paper, computer monitors (similarly, kiosk monitors), and mobile phones. A prototype of DigitalSeal is built using a Arduino UNO, four LLS05-A sensors, four TCRT5000 sensors, a 1602 LCD and a 9V battery.