@conference {
	title = {Multi-scale Analysis of Long Range Dependent Traffic for Anomaly Detection in Wireless Sensor Networks},
	author = {Zheng, Shanshan., Baras, John S.},
	year = {2011},
	month = {12/2011},
	abstract = {Anomaly detection is important for the correct functioning of wireless sensor networks. Recent studies have shown that node mobility along with spatial correlation of the monitored phenomenon in sensor networks can lead to observation data that have long range dependency, which could significantly increase the difficulty of anomaly detection. In this paper, we develop an anomaly detection scheme based on multiscale analysis of the long range dependent traffic to address this challenge. In this proposed detection scheme, discrete wavelet transform is used to approximately de-correlate the traffic data and capture data characteristics in different time scales. The remaining dependencies are then captured by a multi-level hidden Markov model in the wavelet domain. To estimate the model parameters, we propose an online discounting Expectation Maximization (EM) algorithm, which also tracks variations of the estimated models over time. Network anomalies are detected as abrupt changes in the tracked model variation scores. We evaluate our detection scheme numerically using typical long range dependent time series.},
	address = {Orlando USA},
	booktitle = {50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011)},
	pages = {pp. 4060-4065}
}
@conference {
	title = {Trust-Assisted Anomaly Detection and Localization in Wireless Sensor Networks},
	author = {Zheng, Shanshan., Baras, John S.},
	year = {2011},
	month = {06/2011},
	abstract = {Fast anomaly detection and localization is critical to ensure effective functioning of wireless sensor networks. The low bandwidth and power constraints in wireless sensor networks are the main challenges for achieving this task, especially for large scale networks. In this paper, we propose a trust-assisted framework for detecting and localizing network anomalies in a hierarchical sensor network. The proposed method makes inference based on end-to-end measurements collected by a set of measurement nodes. Network heterogeneity is exploited for better bandwidth and energy efficiency. The trustworthiness of network links is utilized to design an efficient two-phase probing strategy that can achieve a flexible tradeoff between inference accuracy and probing overhead. We performed experiments with different network settings and demonstrated the effectiveness of our proposed algorithms.},
	address = {Salt Lake City, Utah},
	booktitle = {8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON) 2011},
	pages = {pp.  386-394}
}
@conference {
	title = {Sequential Anomaly Detection in Wireless Networks and Effects of Long Range Dependent Data},
	author = {Baras, John S., Zheng, Shanshan.},
	year = {2011},
	month = {06/2011},
	abstract = {Anomaly detection is an important but difficult task for wireless networks such as mobile ad hoc networks (MANET) and wireless sensor networks (WSN). We present various sequential detection schemes for anomaly detection in such networks including protocol violation, wormhole attacks, fraudulent data injection or algorithm modification. We present both parametric and non parametric algorithms, as well as centralized and distributed schemes. Recent studies have shown that node mobility along with spatial correlations of the monitored traffic in such wireless networks can lead to long range dependent (LRD) traffic, which could significantly increase the difficulty of anomaly detection. We next analyze the effects of LRD traffic on these sequential anomaly detection schemes. Various models of LRD traffic are considered. In the proposed schemes, wavelet transforms are used to approximately de-correlate the traffic data and capture data characteristics at different time scales. The remaining dependencies are then captured by a multilevel hidden Markov model in the wavelet domain. To estimate the model parameters, we proposed an online discounting Expectation Maximization (EM) algorithm, which also tracks variations of the estimated models over time. Network anomalies are then detected as abrupt changes in the tracked model variation scores. We develop statistical properties of these detection schemes as well as performance results, especially stemming from using incorrect short range dependent traffic models.},
	address = {Palo Alto, CA},
	booktitle = {International Workshop on Sequential Methodologies (IWSM 2011)},
	organization = {Stanford University}
}
@conference {
	title = {Exploiting Trust Relations for Nash Equilibrium Efficiency in Ad Hoc Networks},
	author = {Zheng, Shanshan., Jiang, Tao., Baras, John S.},
	year = {2011},
	month = {06/2011},
	abstract = {Ad hoc networks rely on the mutual cooperation among individual nodes to achieve network-wide objectives. However, individual nodes may behave selfishly in order to maximize their own benefits without considering the global benefits of the network. One approach to incentivize nodes cooperation for better global benefits is to establish trust relations among nodes to guide their decision making. In this paper, we present a game theoretic analysis for the efficiency of establishing trust for improving node cooperation. The trust relations among nodes are modeled as a trust-weighted network, and we study a graphical game in this network where the nodes{\textquoteright} payoffs are affected by their trust relations. We characterize the Nash equilibrium and the social optimum of this game and show that the game efficiency has a close relationship to the Bonacich centralities of nodes in the trust-weighted network. Furthermore, we propose to improve game efficiency by introducing heterogeneous resources to nodes according to their centralities. We provide both experimental and theoretical analysis on the improvement of the game efficiency.},
	address = {Kyoto, Japan},
	booktitle = {IEEE International Conference on Communications (ICC 2011)},
	pages = {pp.1-5}
}
@conference {
	title = {A Robust Collaborative Filtering Algorithm Using Ordered Logistic Regression},
	author = {Zheng, Shanshan., Jiang, Tao., Baras, John S.},
	year = {2011},
	month = {06/2011},
	abstract = {The Internet offers tremendous opportunities for information sharing and content distribution. However, without proper filtering and selection, the large amount of information may likely swarm the users rather than benefit them. Collaborative filtering is a technique for extracting useful information from the large information pool generated by interconnected online communities. In this paper, we develop a probabilistic collaborative filtering algorithm, which is based on ordered logistic regression and takes into account both similarities among the users and similarities among the items. We make inference with maximum likelihood and Bayesian frameworks, and propose a Markov Chain Monte Carlo based Expectation Maximization algorithm to optimize model parameters. The power of our proposed algorithm is its extensibility. We show that it can incorporate content and contextual information. More importantly, it can be easily extended to include the trustworthiness of users, thus being more robust to malicious data manipulation. The experimental results on a real world data set show that our proposed algorithm with the trust extension is robust under different types of attacks in recommendation systems.},
	address = {Kyoto, Japan},
	booktitle = {IEEE International Conference on Communications (ICC 2011)},
	pages = {pp. 1-6}
}
