Abstract This paper proposes a multi-document summarization model using an optimization algorithm named CAVIAR Sun Flower Optimization (CAV-SFO). In this method, two classifiers, namely: Generative Adversarial Network (GAN) classifier and Deep Recurrent Neural Network (Deep RNN), are utilized to generate a score for summarizing multi-documents. Initially, the simHash method is applied for removing the duplicate/real duplicate contents from sentences. Then, the result is given to the proposed CAV-SFO based GAN classifier to determine the score for individual sentences. The CAV-SFO is newly designed by incorporating CAVIAR with Sun Flower Optimization Algorithm (SFO). On the other hand, the pre-processing step is done for duplicate-removed sentences from input multi-document based on stop word removal and stemming. Afterward, text-based features are extracted from pre-processed documents, and then CAV-SFO based Deep RNN is introduced for generating a score; thereby, the internal model parameters are optimally tuned. Finally, the score generated by CAV-SFO based GAN and CAV-SFO based Deep RNN is hybridized, and the final score is obtained using a multi-document compression ratio. The proposed TaylorALO-based GAN showed improved results with maximal precision of 0.989, maximal recall of 0.986, maximal F-Measure of 0.823, maximal Rouge-Precision of 0.930, and maximal Rouge-recall of 0.870.

Summary Today's computer era has paved the way for innovations like self‐driving cars, quantum computing, and other ingenious advancements. As technology advances at a rapid pace, some issues yet remain unresolved. One of the issues is phishing, which dates back to the 1980s. Phishing is an art used by cybercriminals from the 1980s until to date targeting online users to harvest financial, confidential, and other sensitive information. The art and the methodologies used by cybercriminals have not evolved much from the AOL (American online) heydays. However, the counter mechanisms to defeat phishing have undergone considerable changes over the past two decades. Although sophisticated antiphishing systems are in place, statistics shows that phishing is a major threat. Our practical research proves that one of the state‐of‐the‐art antiphishing systems can be bypassed using simple techniques. The research further demonstrates why today's antiphishing mechanisms fail and the need for a novel mechanism that will identify the authenticity of the website. In this manuscript, an antiphishing algorithm, PhishSec (PH‐Sec), is introduced. PhishSec will not consider the URL of the website as the primary factor to determine the authenticity, rather take a reverse approach where the URL of the website is derived by analyzing the content of the visited website to establish its authenticity. To accomplish this, the HTML DOM (document object model) of a given web page on load is considered. This manuscript quotes the research results of the analysis of a state‐of‐the‐art antiphishing system along with the introduced algorithm to counter‐attack phishing. The introduced system detects phishing attacks with 99.21% of accuracy.

Denial of service (DoS) attack is one of the common threats to the availability of critical infrastructure and services. As more and more services are online enabled, the attack on the availability of these services may have a catastrophic impact on our day-to-day lives. Unlike the traditional volumetric DoS, the slow DoS attacks use legitimate connections with lesser bandwidth. Hence, it is difficult to detect slow DoS by monitoring bandwidth usage and traffic volume. In this paper, a novel machine learning model called ‘SCAFFY' to classify slow DoS on HTTP traffic using flow level parameters is explained. SCAFFY uses a multistage approach for the feature section and classification. Comparison of the classification performance of decision tree, random forest, XGBoost, and KNN algorithms are carried out using the flow parameters derived from the CICIDS2017 and SUEE datasets. A comparison of the result obtained from SCAFFY with two recent works available in the literature shows that the SCAFFY model outperforms the state-of-the-art approaches in classification accuracy.