Abstract:
The proliferation of spam poses a significant challenge to email users and organizations, leading to compromised security, loss of productivity and potential financial losses. Traditional spam detection methods often cannot accurately distinguish between legitimate and malicious emails, resulting in increased phishing attacks and malware distribution. This underscores the urgent need for an advanced solution that can effectively detect and filter spam while minimizing false positives and negatives, ensuring email security and user trust. To solve the above-mentioned problem, a combination of Artificial Neural Networks (ANN) and Natural Language Processing (NLP) techniques are used. Through careful experimentation and iterative refinement, a sophisticated ANN architecture tailored for spam detection has been developed. By harnessing the power of deep learning, this aimed to capture complex patterns and semantics that indicate spam content, thereby improving the accuracy and robustness of our detection system. The effectiveness of our CyberGuardian spam detection system was evaluated here. Experiments have been conducted to measure key performance indicators such as precision, accuracy, recall and F1 score using a diverse data set that includes both genuine and spam. CyberGuardian achieved high levels of accuracy and precision in distinguishing between legitimate and spam, revealing significant improvements over baseline methods. Moreover, the system was shown to be robust against adversarial attacks and resilient to evolving spam tactics to protect email communications.
