Browsing by Author "ABHINAV D : 1NH20CS267 ASHISH KUMAR JHA : 1NH20CS269 ANTONY MARVIC MURERA : 1NH20CS287"
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Item ENHANCING ONLINE SECURITY:DEEP LEARNING FOR PHISING DETECTION(New Horizon College of Engineering, 2024) ABHINAV D : 1NH20CS267 ASHISH KUMAR JHA : 1NH20CS269 ANTONY MARVIC MURERA : 1NH20CS287Worldwide, the Internet is connected across the country. There are threats of network attacks in this Internet environment. The risk of integrity and confidentiality has also increased with the density of information and global reach. Security breach has become too easy. In these days the improvement of the network security is therefore highlighted. Protection of the Network allow the unintended interference to some form to network and avoid it. It consists of software for network intrusion detection that track the network. NIDS is positioned in the network in a strategic location to track traffic inside the network from source to destination apps.The machine would optimally screen both inbound and outbound traffic, but that would create a congestion that would hinder the system’s overall pace. Finally, these methods include machine learning algorithms that render the device flexible and deliver reliable performance. Intrusion activities leave evidence in the auditing data, so it is possible to learn and distinguish the pattern of ordinary and malicious activities with machine learning algorithms. Machine training techniques can learn normal, anomalous patterns from training data, and create classifiers for computer system attacks. In the area of intrusion detection for our research works, machine learning methods, such as logistic regression, Naive Bayes, K Nearest Neighbour and Decision Trees were used. The research provides a predictive computational approach to optimize intrusion detection in the Network Traffic Data along with implementing different methodologies for the evaluation of the best accuracy from the Classification and Deep - Learning Algorithms.