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Free Space Optical (FSO) communication systems provide tremendous potential for low-cost time-constrained highbandwidth connectivity in a variety of network scenarios. However, these communication systems are highly unreliable due to the variations in the atmospheric channel thus making carrier grade reliabilities very difficult. Previous solutions suggest the use of a backup RF channel in conjunction with the FSO channel. The transmitted message is also duplicated on the RF channel allowing data recovery during the failure of the optical wireless channel. However, this leads to a wastage of the bandwidth of the RF channel and may sometimes also lead to channel "flapping". In this paper, we propose a novel coding mechanism that optimally achieves the capacity of the combined FSO and RF channels and still provides carrier grade (99.999%) reliabilities in the FSO link. The proposed mechanism uses nonuniform and rate-compatible LDPC codes to achieve the desired reliability and capacity limits. By avoiding data duplication, we preserve to a great extent the crucial security benefit of the FSO communication. Using simulations, we provide initial results on the performance of these codes to support our claims. In particular, we show that, more than two orders of magnitude improvements in the bit error rates and many-fold decrease in the outage probabilities are possible when using hybrid channel codes.

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Abstract-In recent years, the threats and damages caused by active worms have become more and more serious. In order to reduce the loss caused by fastspreading active worms, an effective detection mechanism to quickly detect worms is desired. In this paper, we first explore various scan strategies used by worms on finding vulnerable hosts. We show that targeted worms spread much faster than random scan worms. We then present a generic worm detection architecture to monitor malicious worm activities. We propose and evaluate our detection mechanism called Victim Number Based Algorithm. We show that our detection algorithm is effective and able to detect worm events before 2% of vulnerable hosts are infected for most scenarios. Furthermore, in order to reduce false alarms, we propose an integrated approach using multiple parameters as indicators to detect worm events. The results suggest that our integrated approach can differentiate worm attacks from DDoS attacks and benign scans.

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