Preserving the privacy of data used for analytics in Management and Orchestration (M&O) entails using a variety of methodologies and tactics to protect sensitive data while allowing for effective analysis and decision-making. In 5G network, the Management Data Analytic Function (MDAF) in M&O layer is responsible for performing data analytics by leveraging current and historical network data, including data from the RAN (radio access network), CN (core network), TN (transport network), and other Network Functions (NFs), to provide insights for managing network performance, resource allocation, and policy enforcement. Similar to 5G network, in 6G network, there will be AI-driven management and orchestration mechanism. We call the network analytic function in this mechanism as AI-driven Data Analytic Function (AI-DAF) for performing data analytics. Thus, ensuring that the data used by AI-DAF is managed securely and in accordance with privacy standards is critical to preserving trust, compliance, and security. In this paper, we propose a privacy protection framework for AI-DAF in management and orchestration in 6G network.

Software-Defined Networking (SDN) enhances network management by offering greater adaptability, flexibility, and scalability. However, its centralized controller is susceptible to Distributed Denial of Service (DDoS) attacks, which can compromise network availability. This study proposes an innovative real-time DDoS detection mechanism integrated into the SDN controller. The approach employs frequency-domain analysis to examine Packet-In messages. A time series is created by sampling the number of Packet-In messages at specific time intervals. This time series is then transformed into one or more images using frequency-domain analysis, enabling the extraction of hidden patterns indicative of DDoS attack traffic. Converting time series data into images allows for multi-scale frequency analysis by adjusting the window size, which helps capture both short-term fluctuations and long-term trends. Additionally, different images obtained from varying window sizes are rescaled to a uniform size with minimal information loss, enhancing the effectiveness of pattern recognition. These frequency-based images, encapsulating both amplitude and phase information, are then utilized by a Convolutional Neural Network (CNN) to detect DDoS attack traffic with improved accuracy.

Software-defined networking (SDN) has revolutionized modern mobile networks by enhancing flexibility and scalability, but its centralized architecture remains a prime target for Distributed Denial of Service (DDoS) attacks. This paper presents a novel detection framework that employs frequency-domain analysis to uncover hidden attack patterns within PacketIn message fluctuations. To further refine detection accuracy, eXplainable AI (XAI) is integrated to optimize the detection accuracy of unseen types of DDoS attacks and enhance the interpretability of the model. Our approach enables a more precise attack classification while minimizing false positives using XAI-driven knowledge transfer. Experimental evaluations confirm that this method significantly strengthens SDN resilience against evolving DDoS threats, providing a more adaptive and intelligent defense mechanism.

Contemporary software systems face a severe threat from vulnerabilities, prompting exploration of innovative solutions. Machine Learning (ML) algorithms have emerged as promising tools for predicting software vulnerabilities. However, the diverse sizes of source codes pose a significant obstacle, resulting in varied numerical vector sizes. This diversity disrupts the uniformity needed for ML models, causing information loss, increased false positives, and false negatives, diminishing vulnerability analysis accuracy. In response, we propose CodeGrapher, preserving semantic relations within source code during vulnerability prediction. Our approach involves converting numerical vector representations into image sets for ML input, incorporating similarity distance metrics to maintain vital code relationships. Using Abstract Syntax Tree (AST) representation and skip-gram embedding for numerical vector conversion, CodeGrapher demonstrates potential to significantly enhance prediction accuracy. Leveraging image scalability and resizability addresses challenges from varying numerical vector sizes in ML-based vulnerability prediction. By converting input vectors to images with a set size, CodeGrapher preserves semantic relations, promising improved software security and resilient systems.

Vulnerability prediction is a data-driven process that utilizes previous vulnerability records and their associated fixes in software development projects. Vulnerability records are rarely observed compared to other defects, even in large projects, and are usually not directly linked to the related code changes in the bug tracking system. Thus, preparing a vulnerability dataset and building a predicting model is quite challenging. There exist many studies proposing software metrics-based or embedding/token-based approaches to predict software vulnerabilities over code changes. In this study, we aim to compare the performance of two different approaches in predicting code changes that induce vulnerabilities. While the first approach is based on an aggregation of software metrics, the second approach is based on embedding representation of the source code using an Abstract Syntax Tree and skip-gram techniques. We employed Deep Learning and popular Machine Learning algorithms to predict vulnerability-inducing code changes. We report our empirical analysis over code changes on the publicly available SmartSHARK dataset that we extended by adding real vulnerability data. Software metrics-based code representation method shows a better classification performance than embedding-based code representation method in terms of recall, precision and F1-Score.

Distributed Denial of Service (DDoS) attacks have become one of the most significant problems that affects the user satisfaction by degrading the availability of on-line services. Although intrusion detection systems provide effective mechanism for discriminating various DDoS attacks, they become impotent of detection when bogus packets similar to normal ones are dispatched by the attacker. One idea is to model the normal behavior of the network traffic using time series representation of that traffic together with advanced statistical analysis techniques such as frequency domain analysis for detecting the occurrence frequency (energy) of each basic element in time series. However, frequency domain analysis may become inadequate if the original frequency features are used for the detection anomalies. Therefore, in this work, we propose a hybrid approach that employs frequency domain analysis with sparse representation model to find discriminative characteristics for anomaly-based DDoS detection. The proposed algorithm distinguish abnormal traffic from the normal one based on the energy of time series for the number of packets feature, which is extracted from the time series data by using the sparse representation model. Experimental results show that performance of the proposed algorithm provides better DDoS detection results than the state-of-the-art time-series based approaches in the literature.