While smart farm technologies significantly aid in reducing costs and boosting productivity for farmers, they often lack the necessary robustness against cyberattacks and adaptability to dynamic environmental changes. We propose a solar-powered sensor-based smart farm system to provide high monitoring quality while preserving sensor energy in the presence of adversarial attacks. In a smart farm system, solar-powered sensors are attached to animals (e.g., cows) to monitor their health under varying weather conditions to provide energy-adaptive and high-quality monitoring services. Further, a smart farm system should be robust against adversarial attacks aiming to disrupt monitoring quality. We use deep reinforcement learning (DRL) to identify the optimal policy for maximizing monitoring quality and prolonging the system’s lifetime while maintaining sufficient energy. We introduce transfer learning (TL) into the DRL process to achieve fast learning without experiencing a cold start problem in DRL. In addition, we develop an uncertainty-aware anomaly data detection method to filter out deceptive data caused by adversarial attacks. Via extensive comparative performance analysis conducted based on real datasets, we demonstrate the superior performance of the proposed TL-based DRL strategies over existing competitive counterparts in the system lifetime, the monitoring quality, the learning convergence time, and the energy consumption.

This work develops a node-edge co-evolution model for attributed graph transformation, where both the node and edge attributes undergo changes due to complex interactions. Due to two fundamental obstacles, learning and approximating attributed graph transformation have not been thoroughly explored: 1) the difficulty of jointly considering four types of atomic interactions including nodes-to-edges, nodes-to-nodes, edges-to-nodes, and edges-to-edges interactions. 2) the difficulty of capturing iterative long-range interactions between nodes and edges. To solve these issues, we offer a novel and scalable equilibrium model, NEC∞, with node-edge message passing and edge-node message passing. Additionally, we propose an efficient optimization algorithm that is based on implicit gradient theorem and includes a theoretical analysis of NEC∞. The effectiveness and efficiency of the proposed model have been demonstrated through extensive experiments on synthetic and real-world data sets.

Intent mining is critical for controlling the spread of false information across online social networks (OSNs). To this end, we develop deep reinforcement learning (DRL) agents guided by a delayed reward based on intent prediction using a classifier of long short-term memory (LSTM). Additionally, we incorporate an uncertainty-aware function that leverages subjective opinions derived from Subjective Logic (SL). Through evaluation using an annotated fake news tweet dataset, our results demonstrate that our intent classification framework surpasses competing methods in terms of intent accuracy. Our intent mining solutions using DRL algorithms can support effective and efficient intervention strategies for fake news spreading on OSNs.

This work proposes an energy-adaptive monitoring system for a smart farm using solar sensors attached to cows. The proposed system aims to achieve a high monitoring quality in the smart farm under fluctuating energy and cyber attacks disrupting the collection of sensed data from solar sensors, such as protocol noncompliance, false data injection, denial-of-service, and state manipulation. We adopt Subjective Logic, a belief model, to consider multidimensional uncertainty in sensed data. We employ deep reinforcement learning (DRL) for agents on gateways to collect high-quality sensed data from the solar sensors. The DRL agents aim to collect high-quality sensed data with low uncertainty and high freshness under fluctuating energy levels in solar sensors. We analyze the performance of the proposed energy-adaptive smart farm system in accumulated reward, monitoring error rate, and system overload. We conduct a comparative performance analysis of the uncertainty-aware DRL algorithms against their counterparts in choosing the number of sensed data to be updated to collect high-quality sensed data to achieve high resilience against attacks. Our results prove that multiagent proximal policy optimization (MAPPO) using the uncertainty maximization technique outperforms other counterparts, showing about 4% lower monitoring error rate and the system overload.

In this work, we propose an energy-adaptive moni-toring system for a solar sensor-based smart animal farm (e.g., cattle). The proposed smart farm system aims to maintain high-quality monitoring services by solar sensors with limited and fluctuating energy against a full set of cyberattack behaviors including false data injection, message dropping, or protocol non-compliance. We leverage Subjective Logic (SL) as the belief model to consider different types of uncertainties in opinions about sensed data. We develop two Deep Reinforcement Learning (D RL) schemes leveraging the design concept of uncertainty maximization in SL for DRL agents running on gateways to collect high-quality sensed data with low uncertainty and high freshness. We assess the performance of the proposed energy-adaptive smart farm system in terms of accumulated reward, monitoring error, system overload, and battery maintenance level. We compare the performance of the two DRL schemes developed (i.e., multi-agent deep Q-Iearning, MADQN, and multi-agent proximal policy optimization, MAPPO) with greedy and random baseline schemes in choosing the set of sensed data to be updated to collect high-quality sensed data to achieve resilience against attacks. Our experiments demonstrate that MAPPO with the uncertainty maximization technique outperforms its counterparts.

Diversity-based security approaches have been studied for several decades since the 1970s. The concept ofdiversity-by-designemerged in the 1980s. Since then, diversity-based system design research has been explored to provide more secure and dependable services in cyber-physical systems (CPSs). In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, their insights, and associated future work directions for building secure and dependable CPSs. This will allow us to provide promising ways of providing quality network and services based on key diversity-by-design principles for those who want to conduct research on developing secure and dependable CPSs using diversity as a system design feature. This survey paper mainly provides: (i) The common concept of diversity based on its multidisciplinary nature along with the historical evolution of the concept of diversity-by-design for providing secure and dependable services; (ii) the key diversity-by-design principles; (iii) the key benefits and caveats of using the diversity-by-design; (iv) the main concerns of CPS environments utilizing the diversity-by-design; (v) an extensive survey and discussions of existing diversity-based approaches based on five different classifications; (vi) the types of attacks considered by diversity-based approaches; (vii) the overall trends of evaluation methodologies used for diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) the insights, lessons, and gaps identified from this extensive survey and future work directions.

In this work, we proposed a Deep reinforcement learning (DRL)-based NETwork Adaptations for network Resilience algorithm, namely DeepNETAR, which aims to generate robust network topologies against epidemic attacks. In DeepNETAR, a DRL agent aims to generate a robust network topology against epidemic attacks by removing vulnerable edges or adding the least vulnerable edges, given multiple objectives of system security and performance. Most existing network topology adaptation algorithms have used the size of the giant component (SGC) to ensure service availability based on network connectivity. However, in real communication networks, where packets may be dropped either from the presence of inside attackers or congestion on long routes, a larger SGC does not necessarily ensure higher service availability. In addition, for the DRL agent to learn fast and handle multiple, conflicting system objectives, we considered vulnerability-based selection of adaptable edge candidates, fractal-based solution search, and diverse reward functions aiming to achieve multi-objective optimization. Via extensive simulation experiments, we analyzed what DeepNETAR-based schemes using different objectives can achieve those two conflicting system objectives and comparing existing and baseline counterparts.

By leveraging the principle of software polyculture to ensure security in a network, we propose a vulnerability-based software diversity metric to determine how a network topology can be adapted to minimize security vulnerability while maintaining maximum network connectivity. Our proposed metric estimates the software diversity of the node using the vulnerabilities of software packages installed on nearby nodes on attack paths reachable to the node. Our software diversity-based adaptation (SDA) scheme employs the diversity of each node for edge adaptations. These adaptations include the removal of edges that expose high security vulnerability as well as the potential addition of edges between certain nodes with low vulnerabilities associated with them. To validate the proposed SDA scheme, we conduct extensive experiments comparing our approach with counterpart baseline schemes in real networks. Our simulation results demonstrate that SDA outperforms these existing counterparts. We discuss insights into these findings in terms of the effectiveness and efficiency of the proposed SDA scheme under three real network topologies with vastly different network densities.