TECHNOLOGY: DOTNET
DOMAIN: NETWORKING
| S. No. | IEEE TITLE | ABSTRACT | IEEE YEAR |
| 1. | Behavioral Malware Detection in Delay Tolerant Networks | Abstract—The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets. We identify two unique challenges for extending Bayesian malware detection to DTNs (“insufficient evidence versus evidence collection risk” and “filtering false evidence sequentially and distributedly”), and propose a simple yet effective method, look ahead, to address the challenges. Furthermore, we propose two extensions to look ahead, dogmatic filtering, and adaptive look ahead, to address the challenge of “malicious nodes sharing false evidence.” Real mobile network traces are used to verify the effectiveness of the proposed methods. | 2014 |
| 2. | LocaWard: A Security and Privacy Aware Location-Based Rewarding System | Abstract—The proliferation of mobile devices has driven the mobile marketing to surge in the past few years. Emerging as a new type of mobile marketing, mobile location-based services (MLBSs) have attracted intense attention recently. Unfortunately, current MLBSs have a lot of limitations and raise many concerns, especially about system security and users’ privacy. In this paper, we propose a new location-based rewarding system, called LocaWard, where mobile users can collect location-based tokens from token distributors, and then redeem their gathered tokens at token collectors for beneficial rewards. Tokens act as virtual currency. The token distributors and collectors can be any commercial entities or merchants that wish to attract customers through such a promotion system, such as stores, restaurants, and car rental companies. We develop a security and privacy aware location-based rewarding protocol for the LocaWard system, and prove the completeness and soundness of the protocol. Moreover, we show that the system is resilient to various attacks and mobile users’ privacy can be well protected in the meantime. We finally implement the system and conduct extensive experiments to validate the system efficiency in terms of computation, communication, energy consumption, and storage costs. | 2014 |
| 3. | Power Cost Reduction in Distributed Data Centers: A Two-Time-Scale Approach for Delay Tolerant Workloads | Abstract—This paper considers a stochastic optimization approach for job scheduling and server management in large-scale, geographically distributed data centers. Randomly arriving jobs are routed to a choice of servers. The number of active servers depends on server activation decisions that are updated at a slow time scale, and the service rates of the servers are controlled by power scaling decisions that are made at a faster time scale. We develop a two-time-scale decision strategy that offers provable power cost and delay guarantees. The performance and robustness of the approach is illustrated through simulations. | 2014 |
| 4. | Traffic Pattern-Based Content Leakage Detection for Trusted Content Delivery Networks | Abstract—Due to the increasing popularity of multimedia streaming applications and services in recent years, the issue of trusted video delivery to prevent undesirable content-leakage has, indeed, become critical. While preserving user privacy, conventional systems have addressed this issue by proposing methods based on the observation of streamed traffic throughout the network. These conventional systems maintain a high detection accuracy while coping with some of the traffic variation in the network (e.g., network delay and packet loss), however, their detection performance substantially degrades owing to the significant variation of video lengths. In this paper, we focus on overcoming this issue by proposing a novel content-leakage detection scheme that is robust to the variation of the video length. By comparing videos of different lengths, we determine a relation between the length of videos to be compared and the similarity between the compared videos. Therefore, we enhance the detection performance of the proposed scheme even in an environment subjected to variation in length of video. Through a testbed experiment, the effectiveness of our proposed scheme is evaluated in terms of variation of video length, delay variation, and packet loss. | 2014 |