• BHAGYA VISWANATH
• Mrs. Anitha Jose

As the rapid growth of computer technology, the spread of cyber-attacks increasing. DGAs are Domain Generation Algorithm that uses malware for making command and control connection. DGAS are programs to generate different domain names in different ways. Many traditional methods have limitations. In this paper, we propose a DNN and Machine learning framework for identifying DGA based malware. Initially we collect the dataset from the real traffic. Here the dataset are URLs. For detection of DGA from normal domain the classification is done using Deep Neural network. Next step is to identify the DGA domain by using the clustering technique. The k-means clustering is used for this. The feature generation is needed for both detection and identification processes. Using DNN we can easily classify DGA and normal domain and attain a 90% above accuracy and identify the DGA family using k-means clustering with 92% accuracy.

DGA, Deep Neural Network, K-means clustering, Machine Learning

"IDENTYFYING DGA BASED MALWARE USING DEEP NEURAL NETWORK AND MACHINE LEARNING MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.a380-a387, July-2021, Available :http://www.jetir.org/papers/JETIR2107048.pdf

"IDENTYFYING DGA BASED MALWARE USING DEEP NEURAL NETWORK AND MACHINE LEARNING MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppa380-a387, July-2021, Available at : http://www.jetir.org/papers/JETIR2107048.pdf