Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.

A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications

Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain constraints.To this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless channels.To this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline learning.Then,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power.Finally,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems.

MCRO-PUF:A Novel Modified Crossover RO-PUF with an Ultra-Expanded CRP Space

With the expanding use of the Internet of Things(IoT)devices and the connection of humans and devices to the Internet,the need to provide security in this field is constantly growing.The conventional cryptographic solutions need the IoT device to store secret keys in its non-volatile memory(NVM)leading the system to be vulnerable to physical attacks.In addition,they are not appropriate for IoT applications due to their complex calculations.Thus,physically unclonable functions(PUFs)have been introduced to simultaneously address these issues.PUFs are lightweight and easy-toaccess hardware security primitives which employ the unique characteristics of integrated circuits(ICs)to generate secret keys.Among all proposed PUFs,ring oscillator PUF(RO-PUF)has had amore suitable structure for hardware implementation because of its high reliability and easier providing of circuital symmetry.However,RO-PUF has not been so attractive for authentication purposes due to its limited supported challenge-response pairs(CRPs).A few efforts have been made in recent years that could successfully improve the RO-PUF CRP space,such as configurable RO-PUF(CRO-PUF).In this paper,by considerably improving the CRO-PUF structure and adding spare paths,we propose a novel strong RO-PUF structure that exponentially grows the CRP space and dramatically reduces the hardware cost.We implement our design on a simple and low-cost FPGA chip named XC6SLX9-2tqg144,stating that the proposed design can be used in IoT applications.In addition,to improve the CRP space,our design creates a suitable improvement in different security/performance terms of the generated responses,and dramatically outperforms the state-of-the-art.The average reliability,uniqueness,and uniformity of the responses generated are 99.55%,48.49%,and 50.99%,respectively.

A Novel Attack on Complex APUFs Using the Evolutionary Deep Convolutional Neural Network

As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools for enhancing IoT security.PUFs leverage the inherent randomness found in the embedded hardware of IoT devices.However,it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches.In this paper,a new deep learning(DL)-based modeling attack is introduced to break the resistance of complex XAPUFs.Because training DL models is a problem that falls under the category of NP-hard problems,there has been a significant increase in the use of meta-heuristics(MH)to optimize DL parameters.Nevertheless,it is widely recognized that finding the right balance between exploration and exploitation when dealing with complex problems can pose a significant challenge.To address these chal-lenges,a novel migration-based multi-parent genetic algorithm(MBMPGA)is developed to train the deep convolutional neural network(DCNN)in order to achieve a higher rate of accuracy and convergence speed while decreas-ing the run-time of the attack.In the proposed MBMPGA,a non-linear migration model of the biogeography-based optimization(BBO)is utilized to enhance the exploitation ability of GA.A new multi-parent crossover is then introduced to enhance the exploration ability of GA.The behavior of the proposed MBMPGA is examined on two real-world optimization problems.In benchmark problems,MBMPGA outperforms other MH algorithms in convergence rate.The proposed model are also compared with previous attacking models on several simulated challenge-response pairs(CRPs).The simulation results on the XAPUF datasets show that the introduced attack in this paper obtains more than 99%modeling accuracy even on 8-XAPUF.In addition,the proposed MBMPGA-DCNN outperforms the state-of-the-art modeling attacks in a reduced timeframe and with a smaller number of required sets of CRPs.The area under the curve(AUC)of MBMPGA-DCNN outperforms other architectures.MBMPGA-DCNN achieved sensitivities,specificities,and accuracies of 99.12%,95.14%,and 98.21%,respectively,in the test datasets,establishing it as the most successful method.