Advances in lattice-based cryptography are enabling the use of public key algorithms (PKAs) in power-constrained ad hoc and sensor network devices. Unfortunately, while many wireless networks are dominated by group communications, PKAs are inherently unicast—i.e., public/private key pairs are generated by data destinations. To fully realize public key cryptography in these networks, lightweight PKAs should be augmented with energy-efficient mechanisms for group key agreement. We consider a setting where master keys are loaded on clients according to an arbitrary distribution. We present a protocol that uses session keys derived from those master keys to establish a group key that is information-theoretically secure. When master keys are distributed randomly, our protocol requires$O(\log_b t)$multicasts, where$1-1/b$is the probability that a given client possesses a given master key. The minimum number of public multicast transmissions required for a set of clients to agree on a secret key in our setting was recently characterized. The proposed protocol achieves the best possible approximation to that optimum that is computable in polynomial time. Moreover, the computational requirements of our protocol compare favorably to multi-party extensions of Diffie-Hellman key exchange.

The use of biometric sensors for monitoring an individual’s health and related behaviors, continuously and in real time, promises to revolutionize healthcare in the near future. In an effort to better understand the complex interplay between one’s medical condition and social, environmental, and metabolic parameters, this article presents the KNOWME platform, a complete, end-to-end, body area sensing system that integrates off-the-shelf biometric sensors with a Nokia N95 mobile phone to continuously monitor the metabolic signals of a subject. With a current focus on pediatric obesity, KNOWME employs metabolic signals to monitor and evaluate physical activity. KNOWME development and in-lab deployment studies have revealed three major challenges: (1) the need for robustness to highly varying operating environments due to subject-induced variability, such as mobility or sensor placement; (2) balancing the tension between achieving high fidelity data collection and minimizing network energy consumption; and (3) accurate physical activity detection using a modest number of sensors. The KNOWME platform described herein directly addresses these three challenges. Design robustness is achieved by creating a three-tiered sensor data collection architecture. The system architecture is designed to provide robust, continuous, multichannel data collection and scales without compromising normal mobile device operation. Novel physical activity detection methods which exploit new representations of sensor signals provide accurate and efficient physical activity detection. The physical activity detection method employs personalized training phases and accounts for intersession variability. Finally, exploiting the features of the hardware implementation, a low-complexity sensor sampling algorithm is developed, resulting in significant energy savings without loss of performance.

This paper develops parametric methods to detect network anomalies using only aggregate traffic statistics, in contrast to other works requiring flow separation, even when the anomaly is a small fraction of the total traffic. By adopting simple statistical models for anomalous and background traffic in the time domain, one can estimate model parameters in real time, thus obviating the need for a long training phase or manual parameter tuning. The proposed bivariate parametric detection mechanism (bPDM) uses a sequential probability ratio test, allowing for control over the false positive rate while examining the tradeoff between detection time and the strength of an anomaly. Additionally, it uses both traffic-rate and packet-size statistics, yielding a bivariate model that eliminates most false positives. The method is analyzed using the bit-rate signal-to-noise ratio (SNR) metric, which is shown to be an effective metric for anomaly detection. The performance of the bPDM is evaluated in three ways. First, synthetically generated traffic provides for a controlled comparison of detection time as a function of the anomalous level of traffic. Second, the approach is shown to be able to detect controlled artificial attacks over the University of Southern California (USC), Los Angeles, campus network in varying real traffic mixes. Third, the proposed algorithm achieves rapid detection of real denial-of-service attacks as determined by the replay of previously captured network traces. The method developed in this paper is able to detect all attacks in these scenarios in a few seconds or less.