Our paramount task is to examine and detect network attacks that are one of the daunting tasks because the variety of attacks are day by day existing in colossal number. The proposed system identifies the botnet attacks using the latest cyber dataset CSE-CIC-IDS2018 which is released by Canadian Establishment for Cybersecurity (CIC). The cyber dataset can be accessed on AWS (Amazon Web Services). The Cybersecurity datasets by CIC is world-wide well known. The realistic network dataset consists of all the modern and existing attacks such as Brute-force attacks and password cracking, Heartbleed, Botnet, DoS (Denial of Service), DDoS also known as Distributed Denial of Service, Web attacks i.e. vulnerable web app attacks, and infiltration of the network from inside. The objective of the proposed research is to identify one class classification of Botnet attacks. Botnet attack is a Trojan Horse malware attack which poses a serious security threat to the banking and financial sectors. Since a specific classifier could possibly work for such datasets so it is crucial to finish a comparative examination of classifiers in order to achieve the most noteworthy execution in such basic detection of network attacks. The proposed framework is to incorporate different classifier methods such as KNearset Neighbor classifier, Naïve Bayes, Adaboost with Decision Tree, Support Vector Machine classifier, Random Forest classifier, and Artificial Intelligence to distinguish a portrayal of botnet attacks on the recent cyber dataset CSE-CIC-IDS2018. Classifier results are provided as accurate precision of different classifiers. And furthermore, the proposed framework uses the Calibration curve is a standard approach in analytical methods which generates reliability diagrams to check the predicted probabilities of various classifiers are well calibrated or not. Finally, the displayed graph proves how well the artificial intelligence technique outperforms all the other classifiers. which generates reliability diagrams to check the predicted probabilities of various classifiers are well calibrated or not. Finally, the displayed graph proves how well the artificial intelligence technique outperforms all the other classifiers.
Today, network congestion is a common occurrence that needs to be focused on and effectively addressed, particularly in Wireless Sensor Networks (WSN) for packed type networks. The main causes of congestion in WSN are a lack of channel capacity and energy waste. This study's major goal is to develop Energy Efficient Congestion Free Path Selection Protocol (ECFPSP) protocol, which aims to reduce network congestion. By selecting the most appropriate main cluster head (PCH) and secondary cluster head (SCH), the ECFPSP protocol is proposed to decrease end-to-end delay time and extend the network lifetime. The suggested protocol implements a routing protocol that provides security by avoiding hostile nodes and reducing data loss. It also routes the nodes. Hence, a Congestion-Free Cluster Formation is provided to increase the lifetime of the network by proposed ButPCNN approach. To decrease packet loss and conserve energy, this research also uses brand-new cluster-based WSNs. In comparison to other standard protocols, the simulation results reveal that ButPCNN has a reduced packet drop rate, which increases the ratio of packet distribution, network life, and residual energy. As a result, the suggested method enhances congestion control performance while using less energy and a recently developed strategy is suggested to successfully enhance network performance. The proposed ButPCNN gives 25 percent improvement to optimize traffic on overloaded node than the other traditional approaches.
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