In comparison to the competitors, engineers must provide quick, low-cost, and dependable solutions. The advancement of intelligence generated by machines and its application in almost every field has created a need to reduce the human role in image processing while also making time and labor profit. Lepidopterology is the discipline of entomology dedicated to the scientific analysis of caterpillars and the three butterfly superfamilies. Students studying lepidopterology must generally capture butterflies with nets and dissect them to discover the insect’s family types and shape. This research work aims to assist science students in correctly recognizing butterflies without harming the insects during their analysis. This paper discusses transfer-learning-based neural network models to identify butterfly species. The datasets are collected from the Kaggle website, which contains 10,035 images of 75 different species of butterflies. From the available dataset, 15 unusual species were selected, including various butterfly orientations, photography angles, butterfly lengths, occlusion, and backdrop complexity. When we analyzed the dataset, we found an imbalanced class distribution among the 15 identified classes, leading to overfitting. The proposed system performs data augmentation to prevent data scarcity and reduce overfitting. The augmented dataset is also used to improve the accuracy of the data models. This research work utilizes transfer learning based on various convolutional neural network architectures such as VGG16, VGG19, MobileNet, Xception, ResNet50, and InceptionV3 to classify the butterfly species into various categories. All the proposed models are evaluated using precision, recall, F-Measure, and accuracy. The investigation findings reveal that the InceptionV3 architecture provides an accuracy of 94.66%, superior to all other architectures.

IP traceback is a mechanism for tracing IP packets back to their sources. Tracing mechanisms include packet marking and logging. Log based traceback has the ability to backtrack a single packet by logging each packet at intermediate nodes in the networks. Marking based traceback helps to embed the path information of the intermediate nodes in the packets and the embedded information is used by a victim to reconstruct the attack path. Recent researches show that the performance of hybrid methods comprising logging and marking are appreciable as they help to traceback a single attack packet with less storage overhead on routers. In this study, we use a hybrid approach based on marking and logging to traceback single attack packet with less storage and traceback overhead on routers. We show this through a mathematical analysis. We also evaluate the traceback accuracy of our system and other hybrid approaches. Additionally, the simulation results are also presented to verify the effectiveness of the proposed system.