We present an investigation analysis approach for mining anonymous email content. The core idea behind our approach is concentrated on collecting various effective features from previous emails for all the possible suspects. The extracted features are then used with several machine learning algorithms to extract a unique writing style for each suspect. A sophisticated comparison between the investigated anonymous email and the suspects writing styles is employed to extract evidence of the possible email sender. Extensive experimental results on a real data sets show the improved performance of the proposed method with very limited number of features.

Cognitive radio technology enables unlicensed users (secondary users, SUs) to access the unused spectrum. In the literature, there are three spectrum sharing paradigms that enable SUs to access the licensed spectrum. These access techniques include underlay, overlay and spectrum trading, and have their own drawbacks. To combat these drawbacks, we propose a new approach for each of them and merge them into one combined system. Our overlay scheme provides quick access to the unused spectrum. We propose a new cooperative sensing protocol to reduce the likelihood of interfering with PUs. In order to enable SUs for transmitting simultaneously with PUs, we suggest using our underlay scheme. Our trading scheme allows PUs to trade the unused spectrum for the SUs that require better quality of service. The new combined scheme increases the size of spectrum in the cognitive network. Simulation results show the ability of the new scheme to serve extra traffic.

Several routing algorithms have been proposed for efficient routing in mobile ad hoc networks, most of them consider mobile nodes embedded in two-dimensional environments. However, in reality, these networks are embedded in three-dimensional environments. Usually, two-dimensional routing algorithms have several assumptions that are not valid for three-dimensional spaces. In this article, we propose four different randomized geographic-based routing algorithms that have the following properties: (1) nearly guaranteed delivery rate, by using randomize route to overcome local minimum problems; (2) low overhead, by extracting a virtual backbone of the network and then conducting the routing algorithms over the extracted backbone to decrease the search space; (3) low path dilation, by hybridizing the new algorithms with progress-based routing which have very low path dilation; and (4) works in three-dimensional environment. The first algorithm 3DRanDom chooses the next neighbor randomly from a dominating set of the network (extracted locally). The second algorithm 3DRanDomProb extracts a dominating set and sends to one of the resulted neighbors randomly with more probability for the nodes closer to the destination. The third algorithm G_3DRanDomProb tries to progress as much as possible to the destination, if the progress is not possible, the algorithm switches to 3DRanDomProb. The fourth algorithm G_3DRanDomProb_G uses progress-based routing as much as possible, then it switches to 3DRanDomProb until it overcomes the local minimum problem and then goes back to progress-based routing. We show experimentally that these hybrid randomized routing algorithms on three-dimensional mobile ad hoc networks can achieve nearly guaranteed delivery while discovering routes significantly closer in length to the shortest path and with low overhead.