UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the literature.In this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for MCS.The main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an Information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data samples.Finally,we conducted extensive experiments to evaluate the UAV-ITD scheme.The results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.

An intelligent active probing and trace-back scheme for IoT anomaly detection

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

The extreme imbalanced data problem is the core issue in anomaly detection.The amount of abnormal data is so small that we cannot get adequate information to analyze it.The mainstream methods focus on taking fully advantages of the normal data,of which the discrimination method is that the data not belonging to normal data distribution is the anomaly.From the view of data science,we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method.In this kind of technologies,Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones,which generate synthetic examples randomly in selected line segments.In our work,we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method.In theory,our method covers a wider range.In experiment with real world data,our method performs better than the SMOTE and its meliorations.

Feature Selection Method Based on Class Discriminative Degree for Intelligent Medical Diagnosis

By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in diagnosis.Therefore,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain.In this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the performance.To select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test.We evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and accuracy.Extensive experiments have conducted on a real-world Chinese electronic medical record database.The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.

Securing Display Path for Security-Sensitive Applications on Mobile Devices

While smart devices based on ARM processor bring us a lot of convenience,they also become an attractive target of cyber-attacks.The threat is exaggerated as commodity OSes usually have a large code base and suffer from various software vulnerabilities.Nowadays,adversaries prefer to steal sensitive data by leaking the content of display output by a security-sensitive application.A promising solution is to exploit the hardware visualization extensions provided by modern ARM processors to construct a secure display path between the applications and the display device.In this work,we present a scheme named SecDisplay for trusted display service,it protects sensitive data displayed from being stolen or tampered surreptitiously by a compromised OS.The TCB of SecDisplay mainly consists of a tiny hypervisor and a super light-weight rendering painter,and has only^1400 lines of code.We implemented a prototype of SecDisplay and evaluated its performance overhead.The results show that SecDisplay only incurs an average drop of 3.4%.

Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

For many Internet companies,a huge amount of KPIs(e.g.,server CPU usage,network usage,business monitoring data)will be generated every day.How to closely monitor various KPIs,and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge,especially for unlabeled data.The generated KPIs can be detected by supervised learning with labeled data,but the current problem is that most KPIs are unlabeled.That is a time-consuming and laborious work to label anomaly for company engineers.Build an unsupervised model to detect unlabeled data is an urgent need at present.In this paper,unsupervised learning DBSCAN combined with feature extraction of data has been used,and for some KPIs,its best F-Score can reach about 0.9,which is quite good for solving the current problem.