Botnets and malware continue to avoid detection by static rules engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants t
hat combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the `bagging` model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, F1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large financial enterprise. In four hours of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.
In organic bulk heterojunction solar cells, the open circuit voltage ($V_mathrm{oc}$) suffers from an ultra-high loss at low temperatures. In this work we investigate the origin of the loss through calculating the $V_mathrm{oc}-T$ plots with the devi
ce model method systematically and comparing it with experimentally observed ones. When the energetic disorder is incorporated into the model by considering the disorder-suppressed and temperature-dependent charge carrier mobilities, it is found that for nonselective contacts the $V_mathrm{oc}$ reduces drastically under the low temperature regime, while for selective contacts the $V_mathrm{oc}$ keeps increasing with the decreasing temperature. The main reason is revealed that as the temperature decreases, the reduced mobilities give rise to low charge extraction efficiency and small bimolecular recombination rate for the photogenerated charge carriers, so that in the former case they can be extracted from the wrong electrode to form a leakage current which counteracts the photocurrent and increases quickly with voltage, leading to the anomalous reduction of $V_mathrm{oc}$. In addition, it is revealed that the charge generation rate is slow-varying with temperature and does not induce significant $V_mathrm{oc}$ loss. This work also provides a comprehensive picture for the $V_mathrm{oc}$ behavior under varying device working conditions.
