Security Issues and Defensive Approaches in Deep Learning Frameworks

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous applications.However,the security issues of deep learning frameworks are among the main risks preventing the wide application of it.Attacks on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and life.We start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in them.We propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive approaches.Moreover,we analyze a case of the physical-world use of deep learning security issues.In addition,we discuss future directions and open issues in deep learning frameworks.We hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.

Spectrum Sensing Using Optimized Deep Learning Techniquesin Reconfigurable Embedded Systems

The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniques such as spectrum sensing,spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication.IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index,frequency bands,coding rate etc.,to accommodate the above characteristics.Implementing the above learning methods on the embedded chip leads to high latency,high power consumption and more chip area utilisation.To overcome the problems mentioned above,we present DEEP HOLE Radio sys-tems,the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency(RF)environment.DEEP HOLE Radio provides(i)an opti-mized deep learning framework with a good trade-off between latency,power and utilization.(ii)Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance.The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni direc-tional antennas.The whole spectrum of knowledge has been extracted using GNU radio.These extracted features are used to train the proposed optimized deep learning models,which run parallel on Zynq-SoC 7000,consuming less area,power,latency and less utilization area.The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn,Long Term Short Memory(LSTM),Convolutional Neural Networks(CNN)and Deep Neural Networks(DNN).The outcome shows that the proposed framework has outperformed the existing framework regarding the area,power and time.More-over,the experimental results show that the proposed framework decreases the delay,power and area by 15%,20%25%concerning the existing RFlearn and other hardware constraint frameworks.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

LuoJiaAI:A cloud-based artificial intelligence platform for remote sensing image interpretation

The rapid processing,analysis,and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms(RS-CCPs)have recently become a new trend.The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation,which ignores remote sensing data characteristics such as large image size,large-scale change,multiple data channels,and geographic knowledge embedding,thus impairing computational efficiency and accuracy.We construct a LuoJiaAI platform composed of a standard large-scale sample database(LuoJiaSET)and a dedicated deep learning framework(LuoJiaNET)to achieve state-of-the-art performance on five typical remote sensing interpretation tasks,including scene classification,object detection,land-use classification,change detection,and multi-view 3D reconstruction.The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application.In addition,LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium(OGC)standards for better developing and sharing Earth Artificial Intelligence(AI)algorithms and products on benchmark datasets.LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism,showing great potential in high-precision remote sensing mapping applications.