Malware classification has already been a prominent concern for decades, and malware attacks have proliferated at an astounding rate, constituting a significant threat to cyberspace. Deep learning (DL) and malware image approaches are becoming more prevalent in the field of malware analysis, with spectacular results. This work focuses on the challenge of classifying malware variants that are represented as images. This study employs visualization and proposes a convolutional neural network (CNN) based DL model to effectively and accurately classify malware. The proposed model is trained and tested on a very challenging and heterogeneous dataset, and it achieves accuracy of 98.179%, precision of 97.39%, a F1-score of 97.70%, and a fast classification speed (3 seconds needed to test 934 unseen malware). This demonstrates the proposed model's incredibly quick, effective and accurate performance. The proposed model outperformed existing traditional DL models in terms of various performance measures and demonstrated its usefulness in classifying malware families through visualization. This study and experimental results reveal that small-scale malware images and a simple CNN architecture alone are capable of accurately classifying malware families with high classification accuracy.

IPv6 has features, like a) "no header checksum calculation" and b) "no IP packet fragmentation at intermediate routers", which makes it better than IPv4 from router/routing point of view. Existing Internet technology supports both IPv6 and IPv4 protocols for transport of packets and hence dual addressed machines are widely present. Maximizing QoS in IPv6 networks, as compared to IPv4 networks, for sites having dual addresses is an active area of research. Results of our study on QoS gains in networks connected to IPv6 Internet as compared to IPv4 Internet for a network of about 2500 nodes are presented here. The technique used to estimate QoS gains in the migration from IPv4 to IPv6 is also presented. The test-bed data of one month with 25000 most visited websites was analyzed. The results show that an alternate IPv6 channel exists for a large number of major global websites and substantial QoS gains in terms of reduced access times – averaging up to 35% for some websites - can be expected by intelligent per site IP address selection for dual stack machines.