[This article belongs to Volume - 38, Issue - 01]

DeGATraMoNN: Deep Learning Memetic Ensemble to Detect Spam Threats via a Content-Based Processing

Technological growth is targeted at advancing society to higher sophistication with ease. The birth and advances of the Internet to ease sharing of resources can be attributed to features such as its ease of use, low transaction cost, ubiquitous nature, and user-trust level in adoption. These feats, while advancing its popularity and adoption, also threaten data integrity with a rise in the birth of adversaries. Thus, the quest of detecting intrusion and provisioning countermeasures is now a continuous task. Our study advances a memetic deep-learning modular neural network to detect phishing attacks. Results show model accurately detects malicious contents using a heuristic approach of text identification processing to detect compromised links within an email. Our results show model performance with 98 percent sensitivity, an 87-percent specificity, and 95 percent accuracy, respectively. The model also yields a 4-percent misclassification error rate, for dataset acquisition alongside the generation of ruleset addition to the knowledgebase that was not originally included and used for training of the model from the outset.