Summary The rapid advancement and growth of technology have rendered cloud computing services indispensable to our activities. Threats and intrusions have since multiplied exponentially across a range of industries. In such a scenario, the intrusion detection system, or simply the IDS, is deployed on the network to monitor and detect any attacks. The paper proposes a feed‐forward deep neural network (FFDNN) method based on deep learning methodology using a filter‐based feature selection model. The feature selection strategy aims to determine and select the most highly relevant subset of attributes from the feature importance score for training the deep learning model. Three benchmark data sets were used to assess the experiment: CIC‐IDS 2017, UNSW‐NB15, and NSL‐KDD. In order to justify the proposed technique, a comparison was done using other learning algorithms ranging from classical machine learning to ensemble learning methods that can detect various attacks. The experiments showed that the FFDNN model with reduced feature subsets gave the highest accuracy of 99.53% and 94.45% in the NSL‐KDD and UNSW‐NB15 data sets, while the ensemble‐based XGBoost model performed better in the CIC‐IDS 2017 data set. In addition, the results show that the overall accuracy, recall, and F1 score of the deep learning algorithm are generally better for all the data sets.

Abstract Distributed denial of service attacks (DDoS) penetrate numerous computer system and implant malicious codes thereby making them ready for launching a collaborative attack. These attacks paralyze the target system mainly the web server by exhausting their network resources of the target server. The threats posed by DDoS attacks on the Internet demands for effective detection and mitigation methods of these attacks. In the paper, we proposed an integrated method for detection and mitigation of DDoS attack using machine learning and a line of defenses respectively. The detection phase consists of feature selection through ensemble feature selection algorithm and classification using machine learning algorithm. Feature selection algorithms are important as they reduce the dimension of the dataset. The selection of an efficient classification model will improve the detection rate of the proposed system. In the mitigation phase, we introduce two lines of defense to minimize the exhaustion of the victim server's resources. Using the existing dataset, we show experimentally that it is possible to detect the presence of attacks and mitigate them to a minimum level. The proposed integrated method yields an accuracy of 97.8% in detecting the attacks and able to reduce the utilization of processors upto an average of 25.95%.

In the new era of computers, everyone relies on the internet for basic day-to-day activities to sophisticated and secret tasks. The cyber threats are increasing, not only theft and manipulation of someone's information, but also forcing the victim to deny other requests. A DDoS (Distributed Denial of Service) attack, which is one of the serious issues in today's cyber world needs to be detected and their advance towards the server should be blocked. In the article, the authors are focusing mainly on preventive measures of different types of DDoS attacks using multiple IPtables rules and Windows firewall advance security settings configuration, which would be feasibly free on any PC. The IPtables when appropriately selected and implemented can establish a relatively secure barrier for the system and the external environment.

Distributed Denial‐of‐Service (DDoS) attacks are serious threats in the data center application, mainly affecting the web server. Even though there are various techniques to detect and mitigate such attacks so far they fail to meet in the case of application layer attack and Flash Events (FE). In the paper, we aim at detecting application layer DDoS attacks and distinguish it from FE. We have considered a DDoS attack model and selected the parameters in the incoming packets that correspond in causing the attack. Based on the attack model we have analysed the statistical parameters of the incoming packets such as inter‐arrival time, the probability of uniqueness of an IP address in given time frame and the unavailability of HTTP (Hyper Text Transfer Protocol) GET acknowledgment bit in the header field. These parameters are the input to the Fuzzy classification model. We have used Genetic Algorithm (GA) to provide an optimised value range for the input parameters. The optimised values are now applied to Fuzzy logic to identify whether the web accessing clients shows the behavior of attack, normal or FE. The experimental results show that Fuzzy‐GA model provides an accuracy of 98.4% in detecting DDoS attack and 97.3% in detecting FE..