Open Radio Access Network (O-RAN) adopts a flexible, open, and virtualized architecture with standardized interfaces, reducing dependency on a single supplier. O-RAN hosts many intelligent applications known as eXtended Applications (xApps). xApps are applications deployed at the RAN Intelligent Controller (RIC) that leverage advanced Artificial Intelligence/Machine Learning (AI/ML) algorithms to make dynamic decisions for network optimization. Each application operates with distinct optimization objectives and is managed by independent operators while accessing shared network resources. Conflicts in this context occur when a deployed xApp's objective interferes with another xApp, resulting in incompatible actions or decisions that may negatively impact network performance. The lack of a unified mechanism to coordinate and prioritize the actions of different applications can create three types of conflicts (direct, indirect, and implicit). Conflict prediction in O-RAN refers to the proactive analytical process through which potential interactions or behaviors that may lead to conflicts between network applications are identified in advance, prior to their manifestation within the operational system. In our paper, we introduce a novel data-driven Graph Convolutional Network (GCN)-based method called GRAPH-based Intelligent xApp Conflict Prediction and Analysis (GRAPHICA). It predicts three types of conflicts (direct, indirect, and implicit) and pinpoints the root causes (xApps). GRAPHICA captures the complex and hidden dependencies among the xApps, controlled parameters, and key performance indicators (KPIs) in O-RAN to predict possible conflicts. Then, it identifies the root causes (xApps) contributing to the predicted conflicts. The proposed method is evaluated using highly imbalanced synthetic datasets, in which conflict instances constitute between 40% and merely 10% of the data. This evaluation setting is designed to reflect realistic operational environments where conflicts are infrequent, thereby enabling a comprehensive assessment of the model's performance under real-world conditions. Experimental results demonstrate a high F1-score over 98% for the synthesized datasets with different levels of class imbalance.

In decentralized machine learning over a network of workers, each worker updates its local model as a weighted average of its local model and all models received from its neighbors. Efficient consensus weight matrix design and communication resource allocation can increase the training convergence rate and reduce the wall-clock training time. In this paper, we jointly consider these two factors and propose a novel algorithm termed Communication-Efficient Network Topology (CENT), which reduces the latency in each training iteration by removing unnecessary communication links. CENT enforces communication graph sparsity by iteratively updating, with a fixed step size, a trade-off factor between the convergence factor and a weighted graph sparsity. We further extend CENT to one with an adaptive step size (CENT-A), which adjusts the trade-off factor based on the feedback of the objective function value, without introducing additional computation complexity. We show that both CENT and CENT-A preserve the training convergence rate while avoiding the selection of poor communication links. Numerical studies with real-world machine learning data in both homogeneous and heterogeneous scenarios demonstrate the efficacy of CENT and CENT-A and their performance advantage over state-of-the-art algorithms.

Artificial intelligence (AI) plays an imperative role in next-generation critical infrastructures like the smart grid, whose power can be harnessed by not only operators, but also cyber adversaries. This article investigates a potential threat from adversarial AI in blind false data injection attacks (FDIA) targeting the ac state estimators in the smart grid. Assuming no access to the grid topology required in most FDIA, we propose an adversarial model based on artificial neural networks (ANNs) to infer grid topology from historical measurements. Following the topology inference, a substitute bad data detector (BDD) model is further proposed in the attack model to filter the false data before injection, reducing the risk of detection given potential bad data in normal operations. We also refine the common evaluation of FDI stealthiness by including the presence of bad data among normal and false data when assessing the detection performance. Simulations on the IEEE 30-bus system reveal that significant deviations can be inflicted stealthily by the proposed blind FDI attack. Detailed analyses of the stealthiness, impacts, and parameters are also presented to shed more light on the threats for further studies and effective countermeasures.

Fifth-generation (5G) networks provide connectivity to a massive number of devices and boost a plethora of applications in several different domains. However, the large adoption of connected devices increases attack surfaces and introduces several security threats that can severely damage physical objects and risk people’s lives. Despite existing intrusion detection systems (IDSs), there are still several challenges to be addressed in the detection of cyber-attacks. For instance, while unsupervised IDSs are required to detect zero-day attacks, they usually present high false positive rates. Moreover, most existing IDSs rely on long short-term memory (LSTM) networks to consider time-dependencies among data. However, LSTM networks have recently been shown to present several drawbacks and limitations, which put into question their performance on sequence modeling tasks. Thus, in this paper, we investigate generative adversarial networks (GANs), a promising unsupervised approach to detecting attacks by implicitly modeling systems, and alternatives to LSTM networks to consider temporal dependencies among data. We propose a novel unsupervised GAN-based IDS that uses temporal convolutional networks (TCNs) and self-attention to detect cyber-attacks. The proposed IDS leverages edge computing and is proposed for edge servers, which bring computation resources closer to end nodes. Experiment results show that our proposed IDS can be configured to satisfy different detection rate and detection time requirements. Moreover, they show that our IDS is more accurate and at least 3.8 times faster than two state-of-the-art GAN-based IDSs that are used as baselines.

Wireless data aggregation (WDA) is a pivotal enabling technique for Internet of Things (IoT). Recently, an emerging WDA technique, over-the-air computation (AirComp), has been proposed to perform fast data aggregation, with enhanced spectrum utilization and shortened transmission delay. AirComp leverages the superposition property of a wireless multiple access channel to accomplish functional computation of data, which can support the model aggregation in distributed machine learning and fusion of sensing data. In this paper, we consider a multi-cell AirComp system with spectrum sharing, in which every user in a multi-cell network shares a common part of the spectrum. The mean squared error (MSE) is used as the performance metric to quantify the computation accuracy at each access point (AP). With the aim of coordinating the MSEs among multiple APs, two different objective functions, weighted sum MSE and proportional fairness, are adopted. Accordingly, to minimize the weighted sum MSE, the block coordinate descent (BCD) method is employed. The analytical solutions are also provided. To minimize the proportional fairness, a tractable solution is developed, which is based on the successive convex approximation (SCA) technique. The effectiveness of the proposed schemes is validated by our numerical results.

Mobile Edge Computing (MEC) refers to the concept of placing computational capability at the edge of the network to reduce the latency in handling the client requests. The performance of an edge server is adversely affected when it is overloaded, especially if it crashes due to overload and causes service failures. In this paper, a solution to prevent node from getting overloaded is analyzed by introducing an admission control policy. An adaptive admission control policy based low complexity RL (Reinforcement Learning) SALMUT (Structure-Aware Learning for Multiple Thresholds) is validated using several scenarios mimicking real world deployments. This approach performs as well as to the state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic), but requires an order of magnitude less time to train, and outputs easily interpretable policy.