ABSTRACT The Software‐Defined Networking (SDN) paradigm represents a fundamental shift in networking by decoupling the control plane from the data plane in network devices. This architectural change offers numerous advantages, including network programmability and centralized management capabilities, which improve scalability and efficiency compared to conventional network architectures. However, the dynamic nature of network traffic presents overload challenges, both temporally and spatially, especially in multi‐controller SDN settings. To address these challenges, this paper presents an approach leveraging network traffic patterns for dynamic load balancing. The proposed framework optimizes migration strategies to reduce costs and enhance in‐packet request‐response rates. By exploiting load ratio variance across controllers, the architecture identifies optimal migration triplets, encompassing migration‐in and migration‐out domains by selecting a subset of switches. The architecture utilizes online Q‐learning technology to achieve optimal controller load balancing while minimizing associated expenses. The proposed approach ensures stability and scalability by imposing limits to maintain maximum efficiency and reduce migration conflicts. It iteratively converges to an optimal policy through a comprehensive set of simulations performed on switches under a wide range of load distribution situations. These results highlight the effectiveness and adaptability of the proposed methodology in addressing the intricacies present in dynamic network settings, encouraging further progress in the field of SDN technologies and their real‐world applications.

Smishing is a combined form of short message service (SMS) and phishing in which a malicious text message or SMS is sent to mobile users. This form of attack has come to be a severe cyber-security difficulty and has triggered incredible monetary losses to the victims. Many antismishing solutions for mobile devices have been proposed till date but still, there is a lack of a full-fledged solution. Therefore, this paper proposes an efficient approach that analyzes text content and uniform resource locator (URL) presented in the SMS. We have integrated the URL phishing classifier with the text classifier to improve accuracy as some of the SMS contain the URL with no text or much less text. To find out rare words in a report, depending upon the frequency of term (TF) and the reciprocal of document frequency TF-inverse document frequency (IDF), a weighting framework TF-IDF is used. We have used two data sets for both text as well as for URL phishing classifier and used a synthetic minority oversampling technique to balance the training data. The voting classifier simply merges the findings of each classifier passed into it and predicts the output on the basis of voting. In proposed approach integrating KNN, RF, and ETC can detect smishing messages with a 99.03% accuracy and 98.94% precision rate which is relatively efficient compared with existing ones like SmiDCA model which has the given accuracy of 96.40% using Random Forest classifier in BFSA, Feature-Based it has an accuracy of 98.74% and 94.20% true positive rate and Smishing Detector it shows an overall accuracy of 96.29%.

Today, phishing is one of the biggest problems faced by the cyber-world. In this paper, we present an approach that can detect phishing attacks in commercial and e-banking websites using the link and visual similarity relations. Phisher always tries to mimic the visual design of the webpage and the fake webpage contains identity keywords and hyperlinks that point to the corresponding legitimate webpage to trap internet users. Therefore, our proposed approach analyse the keywords, hyperlinks and CSS layout of the webpage to detect phishing attack. In the proposed approach, we make a set of associate domains with the suspicious webpage and explore the link and similarity relation to identifying phishing webpages. Also, we use the login form and whitelist based filtering to increase the running time of the proposed approach. Our proposed approach is not only able to detect phishing webpages accurately but its source webpage also. Moreover, it does not require any prior training to detect zero hour phishing attack. Experiments are conducted over a 6,616 phishing and legitimate webpages and the proposed approach gives approximately 99.72% true positive rate and less than 1.89% false negative rate.

Phishing is one of the major problems faced by cyber-world and leads to financial losses for both industries and individuals. Detection of phishing attack with high accuracy has always been a challenging issue. At present, visual similarities based techniques are very useful for detecting phishing websites efficiently. Phishing website looks very similar in appearance to its corresponding legitimate website to deceive users into believing that they are browsing the correct website. Visual similarity based phishing detection techniques utilise the feature set like text content, text format, HTML tags, Cascading Style Sheet (CSS), image, and so forth, to make the decision. These approaches compare the suspicious website with the corresponding legitimate website by using various features and if the similarity is greater than the predefined threshold value then it is declared phishing. This paper presents a comprehensive analysis of phishing attacks, their exploitation, some of the recent visual similarity based approaches for phishing detection, and its comparative study. Our survey provides a better understanding of the problem, current solution space, and scope of future research to deal with phishing attacks efficiently using visual similarity based approaches.

Most of the anti-phishing solutions are having two major limitations; the first is the need of a fast access time for a real-time environment and the second is the need of high detection rate. Black-list-based solutions have the fast access time but they suffer from the low detection rate while other solutions like visual similarity and machine learning suffer from the fast access time. In this paper, we propose a novel approach to protect against phishing attacks using auto-updated white-list of legitimate sites accessed by the individual user. Our proposed approach has both fast access time and high detection rate. When users try to open a website which is not available in the white-list, the browser warns users not to disclose their sensitive information. Furthermore, our approach checks the legitimacy of a webpage using hyperlink features. For this, hyperlinks from the source code of a webpage are extracted and apply to the proposed phishing detection algorithm. Our experimental results show that the proposed approach is very effective for protecting against phishing attacks as it has 86.02 % true positive rate while less than 1.48 % false negative rate. Moreover, our proposed system is efficient to detect various other types of phishing attacks (i.e., Domain Name System (DNS) poisoning, embedded objects, zero-hour attack).