Phishing leverages people’s tendency to share personal information online. Phishing attacks often begin with an email and can be used for a variety of purposes. The cybercriminal will employ social engineering techniques to get the target to click on the link in the phishing email, which will take them to the infected website. These attacks become more complex as hackers personalize their fraud and provide convincing messages. Phishing with a malicious URL is an advanced kind of cybercrime. It might be challenging even for cautious users to spot phishing URLs. The researchers displayed different techniques to address this challenge. Machine learning models improve detection by using URLs, web page content and external features. This article presents the findings of an experimental study that attempted to enhance the performance of machine learning models to obtain improved accuracy for the two phishing datasets that are used the most commonly. Three distinct types of tuning factors are utilized, including data balancing, hyper-parameter optimization and feature selection. The experiment utilizes the eight most prevalent machine learning methods and two distinct datasets obtained from online sources, such as the UCI repository and the Mendeley repository. The result demonstrates that data balance improves accuracy marginally, whereas hyperparameter adjustment and feature selection improve accuracy significantly. The performance of machine learning algorithms is improved by combining all fine-tuned factors, outperforming existing research works. The result shows that tuning factors enhance the efficiency of machine learning algorithms. For Dataset-1, Random Forest (RF) and Gradient Boosting (XGB) achieve accuracy rates of 97.44% and 97.47%, respectively. Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) achieve accuracy values of 98.27% and 98.21%, respectively, for Dataset-2.

Cyberattacks are still challenging since they are increasing day by day. Cybercriminals employ a variety of strategies to manipulate and exploit their targets vulnerabilities. Malicious URLs are one such strategy which is used to target large groups on various social media platforms. To draw internet users, these web addresses are disguised as being safe. Deliberate or inadvertent use of such URLs exposes the user or the organization in the cyberspace and opens the way for further attacks. Systems that use rules-based or machine learning algorithms to find malicious URLs usually rely on feature engineering. This requires domain expertise and experience. Sometimes, even after extracting features from a dataset, it may not completely leverage the potential of the dataset. The proposed method employs Natural Language Processing (NLP) approaches to vectorize the words in the URLs and applies machine learning and deep learning models for classification. Vectorization technique in NLP reduces the effort of feature engineering and maximizing the use of the dataset. For the experiment, two separate datasets are used. To vectorize the URL text, three different vectorization methods are used. To evaluate the performance of the proposed method, two different datasets (D1 and D2) that are regularly utilized in the research domain were used. The results demonstrate that the superior accuracy of 92.4 % with the D1 dataset is achieved by the Decision Tree (DT) with count vectorizer and the Random Forest (RF) with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer. With the D2 dataset, DT with TF-IDF vectorizer obtains a greater accuracy of 99.5 %. The Artificial Neural Network (ANN) model achieves 89.6 % accuracy with the D1 dataset and 99.2 % accuracy with the D2 dataset.

Vehicle Ad-hoc Networks (VANETs) are a kind of Internet of Things system where groups of vehicles connect and vehicle tracking infrastructure to enhance driver and public security as well as lifestyles. In a VANET, vehicles oversee the communicating status to the controller and other drivers for processing, as well as the statuses of the road, traffic, and atmosphere around them. In these actual VANET network traffic scenarios, this research presents the mixing of enhanced genetic algorithms (EGA) based on ant colony optimization (ACO) techniques (EGAACO) to create an optimized routing algorithm. This research evaluates the conventional algorithm with metaheuristic possibilities and includes the experimental VANET simulation scenario. To verify the outcomes, the proposed approach is tested using open-source network and traffic simulation tools. On Simulation of Urban Mobility (SUMO), all three different traffic scenarios were simulated and examined using NS3.2. The results of these analyses were satisfactory, and it emerged EGAACO algorithm, performed better than the others in each of the three traffic scenarios. The four real-time traffic network scenarios include average throughput, packet delivery ratio, end-to-end delay, and packet loss in a network, which is collected from the city of Bangalore, and the performance metric measures utilized in traffic network conditions. The experimental result proved that the proposed EGAACO algorithm outperforms the Ad-hoc on- Demand Distance Vector Routing (AODV), Particle Swarm Intelligence (PSO), and ACO routing protocols in all settings.