scholarly journals IMLADS: Intelligent Maintenance and Lightweight Anomaly Detection System for Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 958 ◽  
Author(s):  
Tao Qin ◽  
Bo Wang ◽  
Ruoya Chen ◽  
Zunying Qin ◽  
Lei Wang
Keyword(s):  
Mobile Agent ◽  
Time Complexity ◽  
Detection System ◽  
System Level ◽  
Detection Accuracy ◽  
Security Monitoring ◽  
Intelligent Maintenance ◽  
Node Level ◽  
Data Collection And Analysis ◽  
Level Monitoring

System security monitoring has become more and more difficult with the ever-growing complexity and dynamicity of the Internet of Things (IoT). In this paper, we develop an Intelligent Maintenance and Lightweight Anomaly Detection System (IMLADS) for efficient security management of the IoT. Firstly, unlike the traditional system use static agents, we employ the mobile agent to perform data collection and analysis, which can automatically transfer to other nodes according to the pre-set monitoring task. The mobility is handled by the mobile agent running platform, which is irrelevant with the node or its operation system. Combined with this technology, we can greatly reduce the number of agents running in the system while increasing the system stability and scalability. Secondly, we design different methods for node level and system level security monitoring. For the node level security monitoring, we develop a lightweight data collection and analysis method which only occupy little local computing resources. For the system level security monitoring, we proposed a parameter calculation method based on sketch, whose computational complexity is constant and irrelevant with the system scale. Finally, we design agents to perform suitable response policies for system maintenance and abnormal behavior control based on the anomaly mining results. The experimental results based on the platform constructed show that the proposed method has lower computational complexity and higher detection accuracy. For the node level monitoring, the time complexity is reduced by 50% with high detection accuracy. For the system level monitoring, the time complexity is about 1 s for parameter calculation in a middle scale IoT network.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

scholarly journals Anomaly Detection Using Deep Neural Network for IoT Architecture

2021 ◽  
Vol 11 (15) ◽  
pp. 7050
Author(s):  
Zeeshan Ahmad ◽  
Adnan Shahid Khan ◽  
Kashif Nisar ◽  
Iram Haider ◽  
Rosilah Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Traffic Monitoring ◽  
Detection Accuracy ◽  
Learning Models ◽  
Prime Concern ◽  
Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

Assessment of UV Sensors for Flameout Detection

2017 ◽  
Author(s):  
Edouard Bahous ◽  
Ram Srinivasan ◽  
Priyank Saxena ◽  
John Bowen
Keyword(s):  
Response Time ◽  
Detection System ◽  
Signal Strength ◽  
Sensor Response ◽  
System Level ◽  
Fuel Composition ◽  
Engine Load ◽  
Uv Sensor ◽  
Uv Sensors ◽  
Load Conditions

UV sensors were tested to evaluate the response and reliability as a flameout detection system to reduce system level risks. In this study, UV sensors from two manufacturers were tested on high pressure experimental rigs and on a 15MW gas turbine engine with annular diffusion flame combustion system. Tests were run to investigate the effect of fuel composition, engine load, and sensor circumferential position. The effect of each variable on sensor signal strength and response time is presented in this paper. The response time of the sensor is evaluated against the rate of change of combustor pressure and the time for fuel-air mixture to reach lean extinction limit in the primary zone. Results show that the UV sensor response is not affected by engine load, circumferential location of the sensors, or fuel composition down to Wobbe index of 18.7 MJ/Sm3. At lower Wobbe indices, the signal strength decreased significantly. This result has been attributed to the movement of flame location away from the line of sight of the sensor. Furthermore, it was found that the UV sensor responded before the bulk average reactant mixture reached lean blow out fuel-air ratios. When compared to the baseline detection system the UV sensor performs faster at low load conditions (800 milliseconds) but slower at full load conditions (400 milliseconds). Experimental rig testing led to similar conclusions for sensor response time and signal strength. Future testing of UV sensors on hydrogen blends is planned.

scholarly journals Detection System of HTTP DDoS Attacks in a Cloud Environment Based on Information Theoretic Entropy and Random Forest

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Mohamed Idhammad ◽  
Karim Afdel ◽  
Mustapha Belouch
Keyword(s):  
Random Forest ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Detection System ◽  
Cloud Services ◽  
Detection Accuracy ◽  
Cloud Environment ◽  
Ddos Attacks ◽  
Information Theoretic ◽  
Computing Services

Cloud Computing services are often delivered through HTTP protocol. This facilitates access to services and reduces costs for both providers and end-users. However, this increases the vulnerabilities of the Cloud services face to HTTP DDoS attacks. HTTP request methods are often used to address web servers’ vulnerabilities and create multiple scenarios of HTTP DDoS attack such as Low and Slow or Flooding attacks. Existing HTTP DDoS detection systems are challenged by the big amounts of network traffic generated by these attacks, low detection accuracy, and high false positive rates. In this paper we present a detection system of HTTP DDoS attacks in a Cloud environment based on Information Theoretic Entropy and Random Forest ensemble learning algorithm. A time-based sliding window algorithm is used to estimate the entropy of the network header features of the incoming network traffic. When the estimated entropy exceeds its normal range the preprocessing and the classification tasks are triggered. To assess the proposed approach various experiments were performed on the CIDDS-001 public dataset. The proposed approach achieves satisfactory results with an accuracy of 99.54%, a FPR of 0.4%, and a running time of 18.5s.

scholarly journals Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 11-19
Author(s):  
Yadgar Sirwan Abdulrahman
Keyword(s):  
Intrusion Detection ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Detection System ◽  
Ant Colony ◽  
Detection Accuracy ◽  
Ant Colony Optimization Algorithm ◽  
Network Intrusion ◽  
Secure Network ◽  
Main Component

As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.

scholarly journals Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems

2021 ◽  
Vol 11 (22) ◽  
pp. 10976
Author(s):  
Rana Almohaini ◽  
Iman Almomani ◽  
Aala AlKhayer
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Hybrid System ◽  
State Of The Art ◽  
Detection System ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
Hybrid Detection ◽  
Dynamic Analyses ◽  
Data Files

Android ransomware is one of the most threatening attacks that is increasing at an alarming rate. Ransomware attacks usually target Android users by either locking their devices or encrypting their data files and then requesting them to pay money to unlock the devices or recover the files back. Existing solutions for detecting ransomware mainly use static analysis. However, limited approaches apply dynamic analysis specifically for ransomware detection. Furthermore, the performance of these approaches is either poor or often fails in the presence of code obfuscation techniques or benign applications that use cryptography methods for their APIs usage. Additionally, most of them are unable to detect ransomware attacks at early stages. Therefore, this paper proposes a hybrid detection system that effectively utilizes both static and dynamic analyses to detect ransomware with high accuracy. For the static analysis, the proposed hybrid system considered more than 70 state-of-the-art antivirus engines. For the dynamic analysis, this research explored the existing dynamic tools and conducted an in-depth comparative study to find the proper tool to integrate it in detecting ransomware whenever needed. To evaluate the performance of the proposed hybrid system, we analyzed statically and dynamically over one hundred ransomware samples. These samples originated from 10 different ransomware families. The experiments’ results revealed that static analysis achieved almost half of the detection accuracy—ranging around 40–55%, compared to the dynamic analysis, which reached a 100% accuracy rate. Moreover, this research reports some of the high API classes, methods, and permissions used in these ransomware apps. Finally, some case studies are highlighted, including failed running apps and crypto-ransomware patterns.

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