scholarly journals On jamming detection methods for satellite Internet of Things networks

2021 ◽  
Author(s):  
Giorgio Taricco ◽  
Nader Alagha
Keyword(s):  
Internet Of Things ◽  
Detection Methods ◽  
Jamming Detection

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

scholarly journals Semantic-Based Representation Binary Clone Detection for Cross-Architectures in the Internet of Things

2019 ◽  
Vol 9 (16) ◽  
pp. 3283 ◽  
Author(s):  
Zhenhao Luo ◽  
Baosheng Wang ◽  
Yong Tang ◽  
Wei Xie
Keyword(s):  
Internet Of Things ◽  
Language Processing ◽  
Semantic Representation ◽  
Compiler Optimization ◽  
Symbolic Execution ◽  
Detection Methods ◽  
Intermediate Representation ◽  
Clone Detection ◽  
Code Reuse ◽  
Representation Model

Code reuse is widespread in software development as well as internet of things (IoT) devices. However, code reuse introduces many problems, e.g., software plagiarism and known vulnerabilities. Solving these problems requires extensive manual reverse analysis. Fortunately, binary clone detection can help analysts mitigate manual work by matching reusable code and known parts. However, many binary clone detection methods are not robust to various compiler optimization options and different architectures. While some clone detection methods can be applied across different architectures, they rely on manual features based on human prior knowledge to generate feature vectors for assembly functions and fail to consider the internal associations between features from a semantic perspective. To address this problem, we propose and implement a prototype GeneDiff, a semantic-based representation binary clone detection approach for cross-architectures. GeneDiff utilizes a representation model based on natural language processing (NLP) to generate high-dimensional numeric vectors for each function based on the Valgrind intermediate representation (VEX) representation. This is the first work that translates assembly instructions into an intermediate representation and uses a semantic representation model to implement clone detection for cross-architectures. GeneDiff is robust to various compiler optimization options and different architectures. Compared to approaches using symbolic execution, GeneDiff is significantly more efficient and accurate. The area under the curve (AUC) of the receiver operating characteristic (ROC) of GeneDiff reaches 92.35%, which is considerably higher than the approaches that use symbolic execution. Extensive experiments indicate that GeneDiff can detect similarity with high accuracy even when the code has been compiled with different optimization options and targeted to different architectures. We also use real-world IoT firmware across different architectures as targets, therein proving the practicality of GeneDiff in being able to detect known vulnerabilities.

scholarly journals Research on Security Access of Power Internet of Things Terminals Based on Edge Computing Technology

2021 ◽  
Vol 2108 (1) ◽  
pp. 012014
Author(s):  
Yong Tang ◽  
Linghao Zhang ◽  
Juling Zhang ◽  
Siyu Xiang ◽  
He Cai
Keyword(s):  
Internet Of Things ◽  
Abnormal Behavior ◽  
Detection Methods ◽  
Communication Security ◽  
Stable Operation ◽  
Computing Technology ◽  
Security Authentication ◽  
Abnormal Behavior Detection ◽  
And Control ◽  
Terminal Equipment

Abstract In view of the current lack of unified security authentication and control for the power Internet of Things terminal equipment, at the perception level of the power Internet of Things, the perception layer terminal access control, front-end authentication technology realization and terminal equipment abnormal behavior detection methods are proposed. This method enhances the communication security between power equipment and edge nodes, and ensures the safe and stable operation of the power Internet of Things.

A multi-factor integration-based semi-supervised learning for address resolution protocol attack detection in SDIIoT

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110599
Author(s):  
Zhong Li ◽  
Huimin Zhuang
Keyword(s):  
Internet Of Things ◽  
Supervised Learning ◽  
Software Defined Networking ◽  
Training Data ◽  
Detection Methods ◽  
Industrial Internet Of Things ◽  
Data Set ◽  
Correct Judgment ◽  
Industrial Internet ◽  
Address Resolution Protocol

Nowadays, in the industrial Internet of things, address resolution protocol attacks are still rampant. Recently, the idea of applying the software-defined networking paradigm to industrial Internet of things is proposed by many scholars since this paradigm has the advantages of flexible deployment of intelligent algorithms and global coordination capabilities. These advantages prompt us to propose a multi-factor integration-based semi-supervised learning address resolution protocol detection method deployed in software-defined networking, called MIS, to specially solve the problems of limited labeled training data and incomplete features extraction in the traditional address resolution protocol detection methods. In MIS method, we design a multi-factor integration-based feature extraction method and propose a semi-supervised learning framework with differential priority sampling. MIS considers the address resolution protocol attack features from different aspects to help the model make correct judgment. Meanwhile, the differential priority sampling enables the base learner in self-training to learn efficiently from the unlabeled samples with differences. We conduct experiments based on a real data set collected from a deepwater port and a simulated data set. The experiments show that MIS can achieve good performance in detecting address resolution protocol attacks with F1-measure, accuracy, and area under the curve of 97.28%, 99.41%, and 98.36% on average. Meanwhile, compared with fully supervised learning and other popular address resolution protocol detection methods, MIS also shows the best performance.

scholarly journals Wormhole Attack: a Major Security Concern in Internet of Things (Iot)

2018 ◽  
Vol 7 (3.27) ◽  
pp. 147 ◽  
Author(s):  
Swetha Palacharla ◽  
M Chandan ◽  
K GnanaSuryaTeja ◽  
G Varshitha
Keyword(s):  
Systematic Review ◽  
Internet Of Things ◽  
Base Station ◽  
Wireless Sensor ◽  
Detection Methods ◽  
The Internet ◽  
Sybil Attack ◽  
Wormhole Attack ◽  
The Internet Of Things ◽  
Security Concern

The Internet of Things (IoT) is nothing but a collection of wireless and wired devices, commonly termed as nodes operated remotely. This operation is done by assuming these nodes as the sensors in a wireless sensor network (WSN) administered through a base station. We start with briefing about IoT and then briefing IoT layer models. After this, we discuss attacks with regard to IoT namely Sinkhole attack, Sybil attack, HELLO flood attack, Acknowledgement spoofing attack and their respective detection methods. This paper is systematic review of existing mechanism for the detection of wormhole attack and a new method is proposed.  

scholarly journals DEDA: An algorithm for early detection of topology attacks in the internet of things

2021 ◽  
Vol 11 (2) ◽  
pp. 1761
Author(s):  
Jalindar Karande ◽  
Sarang Joshi
Keyword(s):  
Internet Of Things ◽  
Early Detection ◽  
Power Plants ◽  
Attack Detection ◽  
Detection Methods ◽  
The Internet ◽  
Critical Systems ◽  
Control Packet ◽  
Entry Points ◽  
The Internet Of Things

The internet of things (IoT) is used in domestic, industrial as well as mission-critical systems including homes, transports, power plants, industrial manufacturing and health-care applications. Security of data generated by such systems and IoT systems itself is very critical in such applications. Early detection of any attack targeting IoT system is necessary to minimize the damage. This paper reviews security attack detection methods for IoT Infrastructure presented in the state-of-the-art. One of the major entry points for attacks in IoT system is topology exploitation. This paper proposes a distributed algorithm for early detection of such attacks with the help of predictive descriptor tables. This paper also presents feature selection from topology control packet fields. The performance of the proposed algorithm is evaluated using an extensive simulation carried out in OMNeT++. Performance parameter includes accuracy and time required for detection. Simulation results presented in this paper show that the proposed algorithm is effective in detecting attacks ahead in time.

scholarly journals Intelligent system for IoT botnet detection using SVM and PSO optimization

2021 ◽  
pp. 68-84
Author(s):  
Mahmoud A. Salam ◽  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Intelligent System ◽  
Optimization Technique ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Support Vector ◽  
Botnet Detection ◽  
Iot Devices ◽  
The One

Botnet attacks involving Internet-of-Things (IoT) devices have skyrocketed in recent years due to the proliferation of internet IoT devices that can be readily infiltrated. The botnet is a common threat, exploiting the absence of basic IoT security technologies and can perform several DDoS attacks. Existing IoT botnet detection methods still have issues, such as relying on labeled data, not being validated with newer botnets, and using very complex machine learning algorithms, making the development of new methods to detect compromised IoT devices urgent to reduce the negative implications of these IoT botnets. Due to the vast amount of normal data accessible, anomaly detection algorithms seem to promise for identifying botnet attacks on the Internet of Things (IoT). For anomaly detection, the One-Class Support vector machine is a strong method (ONE-SVM). Many aspects influence the classification outcomes of the ONE-SVM technique, like that of the subset of features utilized for training the ONE-SVM model, hyperparameters of the kernel. An evolutionary IoT botnet detection algorithm is described in this paper. Particle Swarm Optimization technique (PSO) is used to tune the hyperparameters of the ONE-SVM to detect IoT botnet assaults launched from hacked IoT devices. A new version of a real benchmark dataset is used to evaluate the proposed method's performance using traditional anomaly detection evaluation measures. This technique exceeds all existing algorithms in terms of false positive, true positive and rates, and G-mean for all IoT device categories, according to testing results. It also achieves the shortest detection time despite lowering the number of picked features by a significant amount.

scholarly journals Outlier Detection in IoT Using Generative Adversarial Network

2020 ◽  
pp. 306-311
Author(s):  
B. Joyce Beula Rani ◽  
L. Sumathi
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Outlier Detection ◽  
Large Scale ◽  
Detection Methods ◽  
Learning Approach ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Detection Approach ◽  
Iot Devices

Usage of IoT products have been rapidly increased in past few years. The large number of insecure Internet of Things (IoT) devices with low computation power makes them an easy and attractive target for attackers seeking to compromise these devices and use them to create large-scale attacks. Detecting those attacks is a time consuming task and it needs to be identified shortly since it keeps on spreading. Various detection methods are used for detecting these attacks but attack mechanism keeps on evolving so a new detection approach must be introduced to detect their presence and thus blocking their spreading. In this paper a deep learning approach called GAN – Generative Adversarial Network can be used to detect this outlier and achieve 85% accuracy.

scholarly journals Industrial Internet of Things for Mobile Phone Shell Intelligent Detection in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tianyi Zheng ◽  
Bao Peng ◽  
Guofu Zhou
Keyword(s):  
Internet Of Things ◽  
Mobile Phone ◽  
Defect Detection ◽  
Smart Cities ◽  
Detection Methods ◽  
Industrial Internet Of Things ◽  
Data Set ◽  
Open Operation ◽  
Industrial Internet ◽  
Intelligent Detection

Industrial Internet of Things is the core field of smart city. And intelligent detection is an important application field of industrial Internet of Things. Demand of the industrial is particularly urgent. In particular, the defect detection of mobile phone shells (MPS) has always been a common problem for famous mobile phone companies. A compression-free defect detection method (CFDDM) for MPS based on machine vision is proposed in this paper. Firstly, affine transformation is utilized to solve the angle deviation of MPS in different images. Then, edge detection, binarization, and open operation are combined to highlight the edge region based on the results of angle adjustment. It is convenient for region of interest (ROI) extraction and clipping. Finally, the method of gray histogram contrasting is utilized for defect detection according to the results of ROI clipping. And the detection results are obtained. In this paper, MPS data set is utilized for many tests. The results show that the proposed method can effectively detect whether there are defects in MPS data set without image compression. The recognition accuracy is 100%. The recognition time of a single image is about 4.56 s, which is better than other defect detection methods.

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