and attenuation models are described and the received power from the target and background is converted into intensity values and rendered as an image. The effect of various weather conditions including rain, fog, snow and dust are considered. As a result, some examples of simulated MMW and IR images generated

of novel Internet worms. Generation of the worm signatures required by an IDS—the byte patterns sought in monitored traffic to identify worms—today entails non-trivial human labor, and thus significant delay: as network operators detect anomalous behavior, they communicate with one another and manually

Fig. 3. ROC for standard and robust LRT with different contamination parameters. V. CONCLUSION This correspondence has described a procedure for testing which nonlinear dynamic model from a finite class generated a particular random process. The procedure uses extended Kalman filters (EKF’s) tuned

;yes") or new (by responding "no"). Performance on a yesno recognition test is summarized by two measures: the hit rate, the probability that the subject classifies an old item as old, and the false alarm rate, the probability that the subject classifies a new item as old. A given model

such errors at the expense of sometimes generating false alarms. Fortunately, only a small number of false alarms are reported, thereby proving that statically reducing software vulnerability is achievable. CSSV handles large programs by analyzing each procedure separately. For this, procedures

It is widely believed that content-signature-based intrusion detection systems (IDSes) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match

and probability of false alarm. The higher the probability of detection, the better the primary users are protected. However, from the secondary users ’ perspec-tive, the lower the probability of false alarm, the more chances the channel can be reused when it is available, thus the higher the achievable

can cause not only false alarms but also the depletion of the finite amount of energy in a battery powered network. In this paper we present a Statistical En-route Filtering (SEF) mechanism that can detect and drop such false reports. SEF requires that each sensing report be validated by multiple

Traditional problem determination techniques rely on static dependency models that are difficult to generate accurately in today's large, distributed, and dynamic application environments such as e-commerce systems. In this paper, we present a dynamic analysis methodology that automates

4400 human examples. The combined detector reduces the false alarm rate by a factor of 10 relative to the best appearance-based detector, for example giving false alarm rates of 1 per 20,000 windows tested at 8 % miss rate on our Test Set 1. 1