A model of data forwarding in MANETs for lightweight detection of malicious packet dropping
This work introduces a model of data forwarding in MANETs which is used for recognizing malicious packet dropping behaviors. First, different legitimate packet discard situations are modeled, such as those generated by collisions, channel errors or mobility related droppings. Second, we propose an anomaly-based IDS system based on an enhanced windowing method to carry out the collection and analysis of selected cross-layer features. Third, a real deployment of the IDS is also considered by suggesting a methodology for the collection of the selected features in a distributed manner. We evaluate our proposal in a simulation framework and the experimental results show a considerable enhancement in detection results when compared with other approaches in the literature. For instance, our scheme shows a 22% improvement in terms of true positives rate and a remarkable 83% improvement in terms of false positives rate when compared to previous well-known statistical solutions. Finally, it is notable the simplicity and lightweightness of the proposal.