An Improved Dictionary Cracking Scheme Based on Multiple GPUs for Wi-Fi Network

The Internet has penetrated all aspects of human society and has promoted social progress.Cyber-crimes in many forms are commonplace and are dangerous to society and national security.Cybersecurity has become a major concern for citizens and governments.The Internet functions and software applications play a vital role in cybersecurity research and practice.Most of the cyber-attacks are based on exploits in system or application software.It is of utmost urgency to investigate software security problems.The demand for Wi-Fi applications is proliferating but the security problem is growing,requiring an optimal solution from researchers.To overcome the shortcomings of the wired equivalent privacy(WEP)algorithm,the existing literature proposed security schemes forWi-Fi protected access(WPA)/WPA2.However,in practical applications,the WPA/WPA2 scheme still has some weaknesses that attackers exploit.To destroy a WPA/WPA2 security,it is necessary to get a PSK pre-shared key in pre-shared key mode,or an MSK master session key in the authentication mode.Brute-force cracking attacks can get a phase-shift keying(PSK)or a minimum shift keying(MSK).In real-world applications,many wireless local area networks(LANs)use the pre-shared key mode.Therefore,brute-force cracking of WPA/WPA2-PSK is important in that context.This article proposes a new mechanism to crack theWi-Fi password using a graphical processing unit(GPU)and enhances the efficiency through parallel computing of multiple GPU chips.Experimental results show that the proposed algorithm is effective and provides a procedure to enhance the security of Wi-Fi networks.

An Optimized Data Fusion Paradigm for WSN Based on Neural Networks

Wireless sensor networks(WSNs)have gotten a lot of attention as useful tools for gathering data.The energy problem has been a fundamental constraint and challenge faced by many WSN applications due to the size and cost constraints of the sensor nodes.This paper proposed a data fusion model based on the back propagation neural network(BPNN)model to address the problem of a large number of invalid or redundant data.Using three layeredbased BPNNs and a TEEN threshold,the proposed model describes the cluster structure and filters out unnecessary details.During the information transmission process,the neural network’s output function is used to deal with a large amount of sensing data,where the feature value of sensing data is extracted and transmitted to the sink node.In terms of life cycle,data traffic,and network use,simulation results show that the proposed data fusion model outperforms the traditional TEEN protocol.As a result,the proposed scheme increases the life cycle of the network thereby lowering energy usage and traffic.

Machine Learning-based Optimal Framework for Internet of Things Networks

Deep neural networks(DNN)are widely employed in a wide range of intelligent applications,including image and video recognition.However,due to the enormous amount of computations required by DNN.Therefore,performing DNN inference tasks locally is problematic for resourceconstrained Internet of Things(IoT)devices.Existing cloud approaches are sensitive to problems like erratic communication delays and unreliable remote server performance.The utilization of IoT device collaboration to create distributed and scalable DNN task inference is a very promising strategy.The existing research,on the other hand,exclusively looks at the static split method in the scenario of homogeneous IoT devices.As a result,there is a pressing need to investigate how to divide DNN tasks adaptively among IoT devices with varying capabilities and resource constraints,and execute the task inference cooperatively.Two major obstacles confront the aforementioned research problems:1)In a heterogeneous dynamic multi-device environment,it is difficult to estimate the multi-layer inference delay of DNN tasks;2)It is difficult to intelligently adapt the collaborative inference approach in real time.As a result,a multi-layer delay prediction model with fine-grained interpretability is proposed initially.Furthermore,for DNN inference tasks,evolutionary reinforcement learning(ERL)is employed to adaptively discover the approximate best split strategy.Experiments show that,in a heterogeneous dynamic environment,the proposed framework can provide considerable DNN inference acceleration.When the number of devices is 2,3,and 4,the delay acceleration of the proposed algorithm is 1.81 times,1.98 times and 5.28 times that of the EE algorithm,respectively.