Injection attack detection using machine learning for smart IoT applications

Smart cities are a rapidly growing IoT application. These smart cities mainly rely on wireless sensors to connect their different components (smart devices) together. Smart cities rely on the integration of IoT and 5G technologies, and this has created a demand for a massive IoT network of connected devices. The data traffic coming from indoor wireless networks (e.g., smart homes, smart hospitals, smart factories , or smart school buildings) contributes to over 80% of the total data traffic of the current IoT network. As smart cities and their applications grow, security and privacy challenges have become a major concern for billions of IoT smart devices. One reason for this could be the oversight of handling security issues of IoT devices by their manufacturers, which enables attackers to exploit the vulnerabilities in these devices by performing different types of attacks, e.g., DDoS and injection attacks. Intrusion detection is one way to detect and mitigate the risk of such attacks. In this paper, an intrusion detection method was proposed to detect injection attacks in IoT applications (e.g. smart cities). In this method, two types of feature selection techniques (constant removal and recursive feature elimination) were used and tested by a number of machine learning classifiers (i.e., SVM, Random Forest, and Decision Tree). The T-Test was conducted to evaluate the quality of this proposed feature selection method. Using the public dataset, AWID, the evaluation results showed that the decision tree classifier can be used to detect injection attacks with an accuracy of 99% using only 8 features, which were selected using the proposed feature selection method. Also, the comparison with the most related work showed the advantages of the proposed intrusion detection method.     

Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.     

RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

Due to the increasing deployment of heterogeneous networks (HetNets), the selection of which radio access technologies (RATs) for Internet of Things (IoT) devices such as user equipments (UEs) has recently received extensive attention in mobility management research. Most of existing RAT selection methods only optimize the selection strategies from the UE side or network side, which results in heavy network congestion, poor user experience and system utility degradation. In this paper the UE side and the network side are considered comprehensively, based on the game theory (GT) model we propose a reinforcement learning with assisted network information algorithm to overcome the crucial points. The assisted information is formulated as a semi-Markov decision process (SMDP) provided for UEs to make accurate decisions, and we adopt the iteration approach to reach the optimal policy. Moreover, we investigate the impacts of different parameters on the system utility and handover performance. Numerical results validate that our proposed algorithm can mitigate unnecessary handovers and improve system throughputs.     

An interpretable intrusion detection method based on few-shot learning in cloud-ground interconnection

An enterprise’s private cloud may be attacked by attackers when communicating with the public cloud. Although traffic detection methods based on deep learning have been widely used, these methods rely on a large amount of sample data and cannot quickly detect new attacks such as Zero-day Attacks. Moreover, deep learning has a black-box nature and cannot interpret the detection results, which has certain security risks. This paper proposes an interpretable abnormal traffic detection method, which can complete the detection task with only a few malicious traffic samples. Specifically, it uses the covariance matrix to characterize each traffic category and then calculates the similarity between the query traffic and each category according to the covariance metric function to realize the traffic detection based on few-shot learning. After that, the traffic images processed by the random masks are input into the model to obtain the predicted probability of the corresponding traffic category. Finally, the predicted probability is linearly summed with each mask to generate the final saliency map to interpret and analyze the model decision. In this paper, experiments are carried out by simulating only 15 and 25 malicious traffic samples. The results show that the proposed method can obtain good accuracy and recall, and the interpretation analysis shows that the model is reliable and interpretable.     

A new type SVM-projected SVM

Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM(LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.     

一种新型广义RBF神经网络在混沌时间序列预测中的研究

提出了一种新颖的广义径向基函数神经网络模型,其径向基函数(RBF)的形式由生成函数确定.然后,给出了易实现的梯度学习算法,同时为了进一步提高网络的收敛速度和网络性能,又给出了基于卡尔曼滤波的动态学习算法.为了验证网络的学习性能，采用基于卡尔曼滤波算法的新型广义RBF网络预测模型对Mackey-Glass混沌时间序列和Henon映射进行了仿真.结果表明,所提出的新型广义RBF神经网络模型能快速、精确地预测混沌时间序列,是研究复杂非线性动力系统辨识和控制的一种有效方法. 关键词： 广义径向基函数神经网络 卡尔曼滤波 梯度下降学习算法 混沌时间序列 预测     

Widrow-cellular neural network and optoelectronic implementation

A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.     

稀疏阈值的超分辨率图像重建

为了解决基于字典学习的超分辨重构算法耗时过长的问题,提出了基于稀疏阈值模型的图像超分辨率重建方法。首先,将联合字典理论与图像块稀疏阈值方法相结合,训练得到高、低分辨率过完备图像字典对。接着,通过稀疏阈值OMP算法对图像特征块进行稀疏表示。然后,通过高分辨率字典重构出初始的超分辨图像。最后,通过改进迭代反投影算法对初始的超分辨图像进行全局优化,从而进一步提高图像重构质量。实验结果表明,超分辨图像重构平均峰值信噪比(PSNR)为30.1 d B,平均结构自相似度(SSIM)为0.937 9,平均计算时间为10.2 s。有效提高了超分辨重构的速度,改善了重构高分辨图像的质量。     

神经网络在棱镜摄谱仪定量分析光谱中的应用

给出了采用BP神经网络拟合标准光源在棱镜摄谱仪的谱平面位置和相应波长的非线性函数,省去了和标准铁谱比对测量未知谱线波长,简化了光谱的定量分析,提高了仪器的自动化和智能化水平.     

光谱技术结合BiPLS-GA-SPA和ELM算法的生菜冠层氮素含量检测研究

氮素是影响生菜产量和品质的重要因素,光谱技术是检测作物氮素含量最有效的手段之一。通过获取不同氮素水平下生菜冠层的反射光谱,对其进行FDSGF（first-order derivative based savitzky-golay filt）滤波后,利用后向区间偏最小二乘算法（BiPLS）、遗传算法（GA）及连续投影算法（SPA）对特征波长进行梯度提取,最终从2 151个波长点中提取了8个与生菜氮素最为相关的特征波长。分别利用多元线性回归（MLR）、径向基函数神经网络（RBFNN）及极限学习机（ELM）三种算法建立了基于特征波段或特征波长的8个生菜冠层氮素含量检测模型。结果表明： BiPLS-GA-SPA-ELM模型（RMSEC=0.241 6%,R_c=0.934 6,RMSEP=0.284 2%,R_p=0.921 8）的预测结果优于其他模型,为指导合理施肥和开发便携式仪器提供了理论基础。