The evolution of personal computers and electronic mail has resulted in significant shifts when compared to the past. Even when using them maliciously, modern technologies provide enormous advantages to people, businesses, and governments. For instance, large data security, data stadium security, information accessibility, etc. One of the most pressing issues of our day may be digitally-enabled fear-based tyranny. In light of the many challenges posed by the rise of digital technologies, both individuals and institutions have begun to worry that criminal organizations, government agencies, and other non-state actors (also known as "hacktivists") could use these vulnerabilities to launch attacks on the United States. By design, IDSs are isolated from any potential harm caused by cyberattacks. Currently available intrusion detection systems (IDS) employ learning the calculation of the SVM (Bolster Support Vector Machine) to identify port sweep attempts that rely on a new data set of CICIDS 2017, with a combined success rate of 69.79%. Instead of SVM, we might use techniques like Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Random Forests to improve accuracy to levels like SVM (93.29), CNN (63.52), Random Forest (99.93), and ANN (99.11).