Biblio

WSNs are extensively used in defence application for monitoring militant activities in various ways in large unknown territories. Here WSNs has to have large set of distributed systems in the form as sensors nodes. Along with security concerns, False Alarming is also a factor which may interrupt the service and downgrade the application further. Thus in our work we have made sure that when a trigger is raised to an event, images can be captured from the connected cameras so that it will be helpful for both auditing the event as well as capturing the scene which led to the triggering of the event.

We propose a crypto-aided Bayesian detection framework for detecting false data in short messages with low overhead. The proposed approach employs the Bayesian detection at the physical layer in parallel with a lightweight cryptographic detection, followed by combining the two detection outcomes. We develop the maximum a posteriori probability (MAP) rule for combining the cryptographic and Bayesian detection outcome, which minimizes the average probability of detection error. We derive the probability of false alarm and missed detection and discuss the improvement of detection accuracy provided by the proposed method.

The installation of a protection system can provide protection by either deterring or stopping an attacker. Both modes of effectiveness-deterring and stopping-are uncertain. Some have guessed that deterrence plays a much bigger role than stopping force. The force of deterrence should therefore be of considerable interest, especially if its effect could be estimated and incorporated into a larger risk analysis and business case for developing and buying new systems, but nowhere has it been estimated quantitatively. The effect of one type of deterrence, namely, influencing an attacker's choice of targets-or target shifting, biasing an attacker away from some targets toward others-is assessed quantitatively here using a game-theoretic approach. It is shown that its positive effects are significant. It features as a force multiplier on the order of magnitude or more, even for low-performance security countermeasures whose effectiveness may be compromised somewhat, of necessity, in order to keep the number of false alarms serviceably low. The analysis furthermore implies that there are certain minimum levels of stopping performance that a protection should provide in order to avoid attracting the choice of attackers (under deterrence). Nothing in the analysis argues for complacency in security. Developers must still design the best affordable systems. The analysis enters into the middle ground of security, between no protection and impossibly perfect protection. It counters the criticisms that some raise about lower-level, affordable, sustainable measures that security providers naturally gravitate toward. Although these measures might in some places be defeated in ways that a non-expert can imagine, the measures are not for that reason irresponsible or to be dismissed. Their effectiveness can be much greater than they first appear.

A frequent claim that has not been validated is that signature based network intrusion detection systems (SNIDS) cannot detect zero-day attacks. This paper studies this property by testing 356 severe attacks on the SNIDS Snort, configured with an old official rule set. Of these attacks, 183 attacks are zero-days' to the rule set and 173 attacks are theoretically known to it. The results from the study show that Snort clearly is able to detect zero-days' (a mean of 17% detection). The detection rate is however on overall greater for theoretically known attacks (a mean of 54% detection). The paper then investigates how the zero-days' are detected, how prone the corresponding signatures are to false alarms, and how easily they can be evaded. Analyses of these aspects suggest that a conservative estimate on zero-day detection by Snort is 8.2%.