A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed

Testing a positional model of the Hebb effect

Original article can be found at: http://www.informaworld.com/smpp/title~content=t713683358 Copyright Informa / Taylor and Francis GroupIn two experiments, we investigate the hypothesis that a strengthening of position –item associations underlies the improvement seen in performance on an immediate serial recall task, when a given in list is surreptitiously repeated every third trial. Having established a strong effect of repetition, performance was tested on transfer lists in which half the items held the same position as in the repeated list (S-items), the remainder moved (D-items). In Experiment 1, S-items showed a small advantage over control and D-items, in order errors. A second experiment tested whether a design element in Experiment 1 underlay this advantage. When the experimental design was better controlled, no improvement was shown for either S- or D-items over controls. These data were shown to be inconsistent with the results of computer simulations of a positional model. An alternative model is outlined.Peer reviewe

A comparison of pnp and npn SiGe HBTs fabricated by Ge implantation

A study is made of npn and pnp SiGe HBTs produced using Ge implantation. The Ge is implanted into a complementary bipolar process after active area definition. Increased collector currents are observed in both npn and pnp transistors due to the presence of the Ge. The implanted Ge has opposing effects on the emitter dopant diffusion: increasing the arsenic diffusion coefficient in the npn devices and decreasing the boron diffusion coefficient in the pnp devices