Acute power crisis is an obstacle which Bangladesh is facing tremendously. So a small drive has been taken to solve the power crisis problem by opening a new arena by producing electricity through hybrid system of solar and biogas in a dense urban area. The possible biogas and solar power generation is calculated for multistoried building of Dhaka keeping the aim in mind that if this sorts of projects are made popular then it can save a huge amount of electricity by providing to the grid which may enhance our garments potential as a whole industrial potential. Environmental position of Dhaka is poorest in world perspective. So by applying hybrid system by renewable energy, we can improve the environmental aspect tremendously as well. Different types of solar modules have been analyzed in this paper. Power generation using biogas has been incorporated and the potentiality of various types of wastes has been analyzed. This paper evolves using of maximum unused area for producing power in a multistoried building of an urban area like Dhaka.

The research work titled “Smart Intrusion Detection System Comprised of Machine Learning and Deep Learning” was published in European Journal for Engineering and Technology Research (EJERS) online journal in the October edition where a smart IDS model was proposed. In this present work, validation of the IDS model is conducted. KDD Cup'99 intrusion detection dataset was used to build the IDS model. A unique method is incorporated to test the performance of the model. Here, training is conducted by using the KDD'99 dataset. But testing is done through the NSL-KDD dataset. Testing is conducted in three-stage. In the first stage, using generic 41 features the accuracy, sensitivity, and FPR of detecting attack was 95.240%, 93.103%, 1.936% respectively for Random Forest and for MLP it is 87.811%, 90.065%, and 15.168% respectively. In the second stage selective 15 features are used where accuracy, sensitivity, and FPR of detecting attack is 70.808%, 81.992%, 43.971% respectively for Random Forest and for MLP it is 67.637%, 87.660%, 54.266% respectively. In the third stage selective 22 features are used where accuracy, sensitivity, and FPR of detecting attack is 97.001%, 96.643%, 2.272% for Random Forest respectively and for MLP it is 85.442%, 82.350 and 10.472 respectively. Total 3,11,021 record is used for training and 22,544 record is used for testing purpose. The final accuracy, sensitivity and FPR of the model can be resulted as 95.24%, 70.808%, 96.988% for 41 features, 93.103%, 87.68%, 97.233% for 15 features, 1.936%, 43.97%, 3.36% for 22 features. Therefore, the IDS model is efficient and effective.

No abstract

In the present world, digital intruders can exploit the vulnerabilities of a network and are capable to collapse even a country. Attack in Estonia by digital intruders, attack in Iran's nuclear plant and intrusion of spyware in smart phone depicts the efficiency of attackers. Furthermore, centralized firewall system is not enough for ensuring a secured network. Hence, in the age of big data, where availability of data is huge and computation capability of PC is also high, there machine learning and network security have become two inseparable issues. In this thesis, KDD Cup’99 intrusion detection dataset is used. Total 3, 11,030 numbers of records with 41 features are available in the dataset. For finding the anomalies of the network four machine learning methods are used like Classification and Regression Tree (CART), Random Forest, Naive Bayes and Multi-Layer Perception. Initially all 41 features are used to find out the accuracy. Among all the methods, Random Forest provides 98.547% accuracy in intrusion detection which is maximum, and CART shows maximum accuracy (99.086%) to find normal flow of data. Gradually selective 15 features were taken to test the accuracy and it was found that Random Forest is still efficient (accuracy 98.266%) in detecting the fault of the network. In both cases MLP found to be a stable method where accuracy regarding benign data and intrusion are always close to 95% (93.387%, 94.312% and 95.0075, 93.652% respectively). Finally, an IDS model is proposed where Random Forest of ML method and MLP of DL method is incorporated, to handle the intrusion in a most efficient manner.

No abstract