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Deep learning based approaches for object detection are heavily dependent on the nature of data used for training, especially for vehicles driving in cluttered urban environments. Consequently, the performance of Convolutional Neural Network (CNN) architectures designed and trained using data captured under clear weather and favorable conditions, could degrade rather significantly when tested under cloudy and rainy conditions. This naturally becomes a major safety issue for emerging autonomous vehicle platforms relying on CNN based object detection methods. Furthermore, despite a noticeable progress in the development of advanced visual deraining algorithms, they still have inherent limitations for improving the performance of state-of-the-art object detection. In this paper, we address this problem area by make the following contributions.

Internet worms have shown the capability to compromise millions of network hosts in a matter of seconds, thereby precluding human countermeasures. A worm over a vehicular ad hoc network (VANET) can, in addition to the well-known threats, pose a whole new class of traffic-related threats (ranging from congestion to large-scale accidents). To combat these automated adversaries, security patches can be distributed by good worms. An accurate VANET-based worm propagation model is essential to protect from malicious worms and to efficiently utilize good worms for distribution of security patches. This paper derives an approximate closed-form mathematical model of worm propagation over VANETs. Simulation results assert that the proposed model captures the VANET worm propagation dynamics with outstanding accuracy.