Network attacks are increasing day by day. In order to detect them, a system has been created, which actively detects intrusions and attacks in a network or an intranet. The system that detects these types of attacks and intrusions is called intrusion detection system (IDS). The attacks are of two kinds, known and unknown. The IDSs are able to protect against known attacks as they are designed specifically for them. As the usage of the Internet is growing every day, the attacks are increasing as well and all of them are not known to an IDS without proper upgradation, which is harmful as it will not be detected by the IDS and leave the system open to threats. Therefore, an IDS should not just detect the known attacks but even provide security from unknown attacks. Motivated by this, in this article, an ensemble-based IDS using XGBoost is presented. There has been previous research on the topic and with the help of improved technologies, it becomes possible to improve the efficiency and accuracy of the ensemble based IDS. This article proposes to present a scheme that shows the usage of XGBoost with ensemble based IDS can provide better results as XGBoost is based on the tree boosting machine learning algorithms, which helps dealing with a smoother "bias-variance" trade-off. The experiment is performed on the KDD-Cup99 dataset and the recorded accuracy of the proposed method through this experiment is 99.95%.

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Pandemic novel Coronavirus (Covid‐19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid‐19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid‐19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid‐19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)‐based meta‐analysis to predict the trend of epidemic Covid‐19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time‐series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid‐19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid‐19 observed symptoms, a list of Top‐20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid‐19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.

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Networking has become the most integral part of our cyber society. Everyone wants to connect themselves with each other. With the advancement of network technology, we find this most vulnerable to breach and take information and once information reaches to the wrong hands it can do terrible things. During recent years, number of attacks on networks have been increased which drew the attention of many researchers on this field. There have been many researches on intrusion detection lately. Many methods have been devised which are really very useful but they can only detect the attacks which already took place. These methods will always fail whenever there is a foreign attack which is not famous or which is new to the networking world. In order to detect new intrusions in the network, researchers have devised artificial intelligence technique for Intrusion detection prevention system. In this paper we are going to cover what types evolutionary techniques have been devised and their significance and modification.

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