Federated Deep Learning for Zero-Day Botnet Attack Detection in IoT Edge Devices

Deep Learning (DL) has been widely proposed for botnet attack detection in Internet of Things (IoT) networks. However, the traditional Centralized DL (CDL) method cannot be used to detect previously unknown (zero-day) botnet attack without breaching the data privacy rights of the users. In this paper, we propose Federated Deep Learning (FDL) method for zero-day botnet attack detection to avoid data privacy leakage in IoT edge devices. In this method, an optimal Deep Neural Network (DNN) architecture is employed for network traffic classification. A model parameter server remotely coordinates the independent training of the DNN models in multiple IoT edge devices, while Federated Averaging (FedAvg) algorithm is used to aggregate local model updates. A global DNN model is produced after a number of communication rounds between the model parameter server and the IoT edge devices. Zero-day botnet attack scenarios in IoT edge devices is simulated with the Bot-IoT and N-BaIoT data sets. Experiment results show that FDL model: (a) detects zero-day botnet attacks with high classification performance; (b) guarantees data privacy and security; (c) has low communication overhead (d) requires low memory space for the storage of training data; and (e) has low network latency. Therefore, FDL method outperformed CDL, Localized DL, and Distributed DL methods in this application scenario

Multi-commodity optimization of peer-to-peer energy trading resources in smart grid

Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing (P2P-ETS). However, as more distributed energy resources integrate into the distribution network, the impact of the communication link becomes significant. We present a multi-commodity formulation that allows the dual-optimization of energy and communication resources in P2P-ETS. On one hand, the proposed algorithm minimizes the cost of energy generation and communication delay. On the other hand, it also maximizes the global utility of prosumers with fair resource allocation. We evaluate the algorithm in a variety of realistic conditions including a time-varying communication network with signal delay signal loss. The results show that the convergence is achieved in a fewer number of time steps than the previously proposed algorithms. It is further observed that the entities with a higher willingness to trade the energy acquire more satisfactions than others

Peer-to-Peer Local Energy Market: Opportunities, Barriers, Security, and Implementation Options

The concept of the peer-to-peer local energy market (P2P LEM) is no longer novel to the energy community. Yet, its large-scale implementation within the current electricity network remains a complex challenge. One key reason is the lack of understanding of the supplier licensing models in different countries. For instance, in the UK, up to year 2023, a consumer is only allowed to have a single supplier at a time under its single licence supplier model. This directly contradicts the existing P2P trading models that allow a consumer to purchase electricity from multiple sellers within the local market. Given this context, this article conducts a review of recent literature and government policies in different countries on the P2P LEM and identifies the barriers behind the lack of large-scale P2P trading implementation in today's electricity markets. We explain how these barriers can be overcome by engaging prosumers in traditional and private distribution networks through either licensed or license-exempt suppliers. Particularly, we discuss six P2P LEM frameworks that can be utilised to address the supplier licensing issue. Finally, this review presents a summary of risks, and recommendations to aid the regulatory framework to implement P2P LEM

Interlinked Computing in 2040: Safety, Truth, Ownership and Accountability

Computer systems are increasingly interconnected, magnifying benefits and risks, especially with AI integration. Using a Delphi-based method, we interviewed technology futurists about potential trends towards 2040 and their societal impacts. Our findings highlight five key forecasts related to artificial intelligence and system complexity, and suggest six interventions to mitigate negative impacts