LDoS attack detection method based on traffic classification prediction by Liang Liu, Yue Yin, Zhijun Wu, Qingbo Pan, Meng Yue

“…Abstract Aiming at the low rate and strong concealment of low‐rate Denial of Service (LDoS) attacks, the calculation of traffic Hurst index is combined with traffic classification, and a machine learning LDoS attack detection method based on search sorting is proposed. …”
Get full text

An anomaly detection scheme for data stream in cold chain logistics. by Zhibo Xie, Heng Long, Chengyi Ling, Yingjun Zhou, Yan Luo

“…A measurement anomaly detection algorithm based on the improved isolated forest algorithm is proposed. …”
Get full text

Mixed Gas Detection and Temperature Compensation Based on Photoacoustic Spectroscopy by Sun Chao, Hu Runze, Liu Niansong, Ding Jianjun

“…In response to address issues such as difficulties in judging data for classification and recognizing gas components with low accuracy, a KNN-SVM algorithm has been proposed. …”
Get full text

Hard-coded backdoor detection method based on semantic conflict by Anxiang HU, Da XIAO, Shichen GUO, Shengli LIU

“…The current router security issues focus on the mining and utilization of memory-type vulnerabilities, but there is low interest in detecting backdoors.Hard-coded backdoor is one of the most common backdoors, which is simple and convenient to set up and can be implemented with only a small amount of code.However, it is difficult to be discovered and often causes serious safety hazard and economic loss.The triggering process of hard-coded backdoor is inseparable from string comparison functions.Therefore, the detection of hard-coded backdoors relies on string comparison functions, which are mainly divided into static analysis method and symbolic execution method.The former has a high degree of automation, but has a high false positive rate and poor detection results.The latter has a high accuracy rate, but cannot automate large-scale detection of firmware, and faces the problem of path explosion or even unable to constrain solution.Aiming at the above problems, a hard-coded backdoor detection algorithm based on string text semantic conflict (Stect) was proposed since static analysis and the think of stain analysis.Stect started from the commonly used string comparison functions, combined with the characteristics of MIPS and ARM architectures, and extracted a set of paths with the same start and end nodes using function call relationships, control flow graphs, and branching selection dependent strings.If the strings in the successfully verified set of paths have semantic conflict, it means that there is a hard-coded backdoor in the router firmware.In order to evaluate the detection effect of Stect, 1 074 collected device images were tested and compared with other backdoor detection methods.Experimental results show that Stect has a better detection effect compared with existing backdoor detection methods including Costin and Stringer: 8 hard-coded backdoor images detected from image data set, and the recall rate reached 88.89%.…”
Get full text

Noncoherent multiple symbol detection of CPFSK based on decision-feedback by Qiang CHEN, Guo-sheng RUI, Wen-jun SUN, Wen-biao TIAN, Yang ZHANG

“…There is a great deal of issues in the traditional symbol detection algorithm such as high computational com-plexity and large engineering implementation difficulty. …”
Get full text

Android malware detection based on APK signature information feedback by Xin-yu LIU, Jian WENG, Yue ZHANG, Bing-wen FENG, Jia-si WENG

“…A new malware detection method based on APK signature of information feedback (SigFeedback) was proposed.Based on SVM classification algorithm,the method of eigenvalue extraction adoped heuristic rule learning to sig APK information verify screening,and it also implemented the heuristic feedback,from which achieved the purpose of more accurate detection of malicious software.SigFeedback detection algorithm enjoyed the advantage of the high detection rate and low false positive rate.Finally the experiment show that the SigFeedback algorithm has high efficiency,making the rate of false positive from 13% down to 3%.…”
Get full text

Android malware detection based on APK signature information feedback by Xin-yu LIU, Jian WENG, Yue ZHANG, Bing-wen FENG, Jia-si WENG

“…A new malware detection method based on APK signature of information feedback (SigFeedback) was proposed.Based on SVM classification algorithm,the method of eigenvalue extraction adoped heuristic rule learning to sig APK information verify screening,and it also implemented the heuristic feedback,from which achieved the purpose of more accurate detection of malicious software.SigFeedback detection algorithm enjoyed the advantage of the high detection rate and low false positive rate.Finally the experiment show that the SigFeedback algorithm has high efficiency,making the rate of false positive from 13% down to 3%.…”
Get full text

Moving Target Detection and Active Tracking with a Multicamera Network by Long Zhao, Li Fei Liu, Qing Yun Wang, Tie Jun Li, Jian Hua Zhou

“…The proposed framework consists of low-cost static and PTZ cameras, target detection and tracking algorithms, and a low-cost PTZ camera feedback control algorithm based on target information. …”
Get full text

Improved Crack Detection and Recognition Based on Convolutional Neural Network by Keqin Chen, Amit Yadav, Asif Khan, Yixin Meng, Kun Zhu

“…There are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. …”
Get full text

The Line Pressure Detection for Autonomous Vehicles Based on Deep Learning by Xuexi Zhang, Ying Li, Ruidian Zhan, Jiayang Chen, Junxian Li

“…However, these algorithms also have shortcomings such as low detection accuracy or relying on specific scenarios. …”
Get full text

An optimization-inspired intrusion detection model for software-defined networking by Hui Xu, Longtan Bai, Wei Huang

“…Currently, more and more intrusion detection systems based on machine learning and deep learning are being applied to SDN, but most have drawbacks such as complex models and low detection accuracy. …”
Get full text

Semi-supervised permutation invariant particle-level anomaly detection by Gabriel Matos, Elena Busch, Ki Ryeong Park, Julia Gonski

“…Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. …”
Get full text

BLSTM based night-time wildfire detection from video. by Ahmet K Agirman, Kasim Tasdemir

“…To this end, a BLSTM based night-time wildfire event detection from a video algorithm is proposed. It is shown in the experiments that the proposed algorithm attains 95.15% of accuracy when tested against a wide variety of actual recordings of night-time wildfire incidents and 23.7 ms per frame detection time. …”
Get full text

Utilizing Calibration Model for Water Distribution Network Leakage Detection by Geumchae Shin, Soon Ho Kwon, Suhyun Lim, Seungyub Lee

“…The proposed model comprises two distinct algorithms: (1) PRC estimation using MCMC and (2) a leakage detection algorithm employing a Kolmogorov–Smirnov test. …”
Get full text

Simulation of Ground Visibility Based on Atmospheric Boundary Layer Data Using K-Nearest Neighbors and Ensemble Model Algorithms by Ruolan Liu, Shujie Yuan, Duanyang Liu, Lin Han, Fan Zu, Hong Wu, Hongbin Wang

“…This study introduces a machine learning approach for simulating visibility, utilizing the K-Nearest Neighbors algorithm and an ensemble model, which incorporate data from atmospheric boundary layer detection and conventional ground meteorological observations as simulation inputs. …”
Get full text

A lightweight personnel detection method for underground coal mines by Shuai WANG, Wei YANG, Yuxiang LI, Jiaqi WU, Wei YANG

“…Commonly used detection algorithms have large parameter counts, high requirements on equipment arithmetic, and are not satisfactory for application in low illumination environments in coal mines. …”
Get full text

A novel transformer-based approach for cardiovascular disease detection by Nimra Noor, Muhammad Bilal, Saadullah Farooq Abbasi, Omid Pournik, Theodoros N. Arvanitis

Get full text

PUE Attack Detection in CWSN Using Collaboration and Learning Behavior by Javier Blesa, Elena Romero, Alba Rozas, Alvaro Araujo, Octavio Nieto-Taladriz

“…A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. …”
Get full text

Leather Defect Detection Based on Improved YOLOv8 Model by Zirui Peng, Chen Zhang, Wei Wei

“…Addressing the low accuracy and slow detection speed experienced by algorithms based on deep learning for a leather defect detection task, a lightweight and improved leather defect detection algorithm, dubbed YOLOv8-AGE, has been proposed based on YOLOv8n. …”
Get full text

Energy optimization using adaptive control algorithm to enhance the performance of SDN_IOT environment by I. Varalakshmi, M. Thenmozhi

“…This paper presents an algorithm for detecting DDoS attacks earlier from the network using the entropy method and mitigating the attacks earlier using stochastic techniques. …”
Get full text