Software-defined networking (SDN) has been recognized for its potential in network programming and centralized control. However, this advancement brings forth critical security vulnerabilities. It is essential to understand that vulnerabilities, by their inherent nature, may lead to potential attacks if not addressed timely and appropriately. In this paper, we introduce a novel multi-modal deep transfer learning (MMDTL) framework tailored for effective attack detection in SDN environments that helps us to investigate a diverse spectrum of attack types. MMDTL framework comprehensively incorporates diverse data modalities — encompassing network traffic patterns, system logs, and user behavior analytic. A pivotal feature of this framework is its transfer learning approach, which enables the assimilation of insights from pre-trained models that subsequently increases the detection performance of attacks. We rigorously analyze the proposed framework with experiments on the intrusion detection evaluation dataset (CIC-IDS2017). Analyses results show the superiority of our framework with a detection accuracy 99.97%.Thus, MMDTL framework has a significant potential to support security in SDNs.

The widespread use of computing technology has been changing relationships among people in societies. Criminals are aware of the power of the technology so that many criminal activities involve more computing systems. Money laundering has been a significant criminal activity within financial computing systems for many decades. The dynamic nature of information systems has reduced the effectiveness of existing money laundering detection mechanisms that is an important challenge for societies. In this paper, we consider machine learning algorithms as complementary solutions to existing money laundering detection mechanisms. We have focused on graph-based representation of data with Node2Vec to have better classification results for money laundering detections with machine learning algorithms. Our experimental analyses show that Node2Vec enable us to select the most convenient machine learning algorithm for money laundering detections.

Since web technologies are getting more advanced with longer codes, the number of vulnerabilities has increased considerably. Cross-site scripting (XSS) attacks are one of the most common attacks that use vulnerabilities in web applications. There are three types of cross-site scripting attacks namely, reflected, stored, and DOM-based attacks. Reflected XSS attacks are the most common type that is usually implemented by injecting a malicious code into the URL and then sending the URL to the targeted system by using phishing methods, which is a significant threat for recent web applications. Our motivation is the lack of a high performance detection method of reflected XSS attacks with high accuracy. In this paper, we propose a hybrid machine learning model to detect vulnerabilities related to reflected XSS attacks for a given URL of a website. Our model uses a scanner to discover vulnerabilities in a web site and convolutional neural networks to predict the most common vulnerabilities that may be used for reflected XSS attacks, which makes the proposed model hybrid. We analyzed the model experimentally. Analyses results show that the proposed model is able to detect vulnerable attack surfaces with 99 % accuracy.