scholarly journals Anomalous Behavior Detection Using the Geometrical Complex Moments in Crowd Scenes of Smart Surveillance Systems

2018 ◽  
Vol 28 (3) ◽  
pp. 174
Author(s):  
Narjis Mezaal Shati
Keyword(s):  
Detection System ◽  
Research Work ◽  
Regions Of Interest ◽  
False Alarms ◽  
Surveillance Systems ◽  
Clustering Method ◽  
Interest Points ◽  
Detection Rates ◽  
Video Clips ◽  
Fast Detector

In this research work a data stream clustering method done by extracting regions of interest from the frames of video clips (UCSD pedestrian dataset (ped1 and ped2 datasets) video clips, and VIRAT VIDEO dataset video clips). In extraction process the HARRIS or FAST detector applied on the frames of video clips to extract list of pairs of interest points. From these pairs a list of features such as: distance, direction, x-coordinate, y-coordinate obtained to use as an input to the clustering method based on seed based region growing technique. From these clusters a regions of interest extracted according the pairs coordinates of each cluster. Finally, from these regions a set of geometrical complex moments obtained and then used in anomaly detection system. The results indicated that using HARRIS detector achieved detection rates are 7.88%, 51.30%, and 56.67% with false alarms are 19.39%, 32.61%, and 60.00% by using Ped1, Ped2, and VIRAT datasets respectively. For the case of using FAST detector, the best detection rates are 6.67%, 44.78%, 53.33% with false alarm rates are 33.33%, 41.74%, 70% by using the datasets respectively.

scholarly journals Intrusion Detection Based on Big Data Fuzzy Analytics

2021 ◽  
Author(s):  
Farah Jemili ◽  
Hajer Bouras
Keyword(s):  
Big Data ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
High Performance ◽  
Detection System ◽  
Training Dataset ◽  
False Alarms ◽  
Massive Datasets ◽  
Detection Rates

In today’s world, Intrusion Detection System (IDS) is one of the significant tools used to the improvement of network security, by detecting attacks or abnormal data accesses. Most of existing IDS have many disadvantages such as high false alarm rates and low detection rates. For the IDS, dealing with distributed and massive data constitutes a challenge. Besides, dealing with imprecise data is another challenge. This paper proposes an Intrusion Detection System based on big data fuzzy analytics; Fuzzy C-Means (FCM) method is used to cluster and classify the pre-processed training dataset. The CTU-13 and the UNSW-NB15 are used as distributed and massive datasets to prove the feasibility of the method. The proposed system shows high performance in terms of accuracy, precision, detection rates, and false alarms.

Towards a Real-Time Fall Detection System using Kinect Sensor

2016 ◽  
Vol 6 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Nadia Baha ◽  
Eden Beloudah ◽  
Mehdi Ousmer
Keyword(s):  
Real Time ◽  
Detection System ◽  
Fall Detection ◽  
Microsoft Kinect ◽  
False Alarms ◽  
Depth Information ◽  
Detection Rates ◽  
Major Health Problem ◽  
Major Health ◽  
Time Fall

Falls are the major health problem among older people who live alone in their home. In the past few years, several studies have been proposed to solve the dilemma especially those which exploit video surveillance. In this paper, in order to allow older adult to safely continue living in home environments, the authors propose a method which combines two different configurations of the Microsoft Kinect: The first one is based on the person's depth information and his velocity (Ceiling mounted Kinect). The second one is based on the variation of bounding box parameters and its velocity (Frontal Kinect). Experimental results on real datasets are conducted and a comparative evaluation of the obtained results relative to the state-of-art methods is presented. The results show that the authors' method is able to accurately detect several types of falls in real-time as well as achieving a significant reduction in false alarms and improves detection rates.

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

scholarly journals Anxiety

2019 ◽  
Author(s):  
Angarika Deb ◽  
Nikhil Chaudhary
Keyword(s):  
Detection System ◽  
False Alarms ◽  
Evolutionary Perspective ◽  
Uncertain Environments ◽  
Hazard Detection ◽  
Anxiety Response

From an evolutionary perspective, anxiety can be considered as a psychological hazard-detection system. In uncertain environments, the costs of responding to false cues of danger are often miniscule as compared to those resulting from undetected threats. Therefore, the anxiety response has evolved a bias towards false alarms under conditions of uncertainty. We discuss general and specific types of anxiety responses, underlining how the nature of these are often determined by the particular types of threats in the environment.

Visualization Technique for Intrusion Detection

2018 ◽  
pp. 276-290
Author(s):  
Mohamed Cheikh ◽  
Salima Hacini ◽  
Zizette Boufaida
Keyword(s):  
Intrusion Detection ◽  
Computer Security ◽  
Detection System ◽  
Denial Of Service ◽  
High Rate ◽  
False Alarms ◽  
Dos Attacks ◽  
Network Parameter ◽  
A New Technique ◽  
Parameter Values

Intrusion detection system (IDS) plays a vital and crucial role in a computer security. However, they suffer from a number of problems such as low detection of DoS (denial-of-service)/DDoS (distributed denial-of-service) attacks with a high rate of false alarms. In this chapter, a new technique for detecting DoS attacks is proposed; it detects DOS attacks using a set of classifiers and visualizes them in real time. This technique is based on the collection of network parameter values (data packets), which are automatically represented by simple geometric graphs in order to highlight relevant elements. Two implementations for this technique are performed. The first is based on the Euclidian distance while the second is based on KNN algorithm. The effectiveness of the proposed technique has been proven through a simulation of network traffic drawn from the 10% KDD and a comparison with other classification techniques for intrusion detection.

Detecting Denial of Service Attacks on SIP Based Services and Proposing Solutions

2011 ◽  
pp. 145-167 ◽  
Author(s):  
Zoha Asgharian ◽  
Hassan Asgharian ◽  
Ahmad Akbari ◽  
Bijan Raahemi
Keyword(s):  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
False Alarms ◽  
Test Bed ◽  
Multimedia Services ◽  
Detection Rates ◽  
Signaling Protocol ◽  
Detection Techniques ◽  
Set Up

One of the main goals of employing Next Generation Networks (NGN) is an integrated access to the multimedia services like Voice over IP (VoIP), and IPTV. The primary signaling protocol in these multimedia services is Session Initiation Protocol (SIP). This protocol, however, is vulnerable to attacks, which may impact the Quality of Service (QoS), which is an important feature in NGN. One of the most frequent attacks is Denial of Service (DoS) attack, which is generated easily, but its detection is not trivial. In this chapter, a framework is proposed to detect Denial of Service attacks and a few other forms of intrusions, and then we react accordingly. The proposed detection engine combines the specification- and anomaly-based intrusion detection techniques. The authors set up a test-bed and generate a labeled dataset. The traffic generated for the test-bed is composed of two types of SIP packets: attack and normal. They then record the detection rates and false alarms based on the labeled dataset. The experimental results demonstrate that the proposed approach can successfully detect intruders and limit their accesses. The results also confirm that the framework is scalable and robust.

Association Rule-Mining-Based Intrusion Detection System With Entropy-Based Feature Selection

2020 ◽  
pp. 1-24
Author(s):  
Devaraju Sellappan ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Intrusion Detection ◽  
Association Rule ◽  
Intrusion Detection System ◽  
Association Rule Mining ◽  
Detection System ◽  
False Positive Rate ◽  
Rule Mining ◽  
Detection Rates ◽  
Mining Algorithm ◽  
Positive Rate

Intrusion detection system (IDSs) are important to industries and organizations to solve the problems of networks, and various classifiers are used to classify the activity as malicious or normal. Today, the security has become a decisive part of any industrial and organizational information system. This chapter demonstrates an association rule-mining algorithm for detecting various network intrusions. The KDD dataset is used for experimentation. There are three input features classified as basic features, content features, and traffic features. There are several attacks are present in the dataset which are classified into Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). The proposed method gives significant improvement in the detection rates compared with other methods. Association rule mining algorithm is proposed to evaluate the KDD dataset and dynamic data to improve the efficiency, reduce the false positive rate (FPR) and provides less time for processing.

An Effective Method to Reduce False Alarms in Video Face Detection

2013 ◽  
Vol 347-350 ◽  
pp. 3619-3623
Author(s):  
Bing Li ◽  
Yuan Yan Tang ◽  
Di Wen ◽  
Zhen Chao Zhang ◽  
Bo Yang Ding
Keyword(s):  
Face Detection ◽  
Detection System ◽  
False Alarms ◽  
Fusion Method ◽  
Integral Image ◽  
Paper Briefly ◽  
Model Fusion ◽  
Adaboost Algorithm ◽  
Detector Structure ◽  
Face Detector

This paper briefly introduced the development of video face detection and point out the shortage of current face detection system that may produce much of false alarms. Then we detail the classic Viola face detector which using integral image, Haar-like features and AdaBoost algorithm for training. Compared with Viola face detector, we proposed an available multi-model fusion method to reduce false alarms in video face detection that is combining head-shoulder detector with HOG features. After introduced the related knowledge of HOG features, we proposed a fusion detector structure which can improve the accuracy and efficiency of detection.

scholarly journals Developing an On-Road Object Detection System Using Monovision and Radar Fusion

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Ya-Wen Hsu ◽  
Yi-Horng Lai ◽  
Kai-Quan Zhong ◽  
Tang-Kai Yin ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Spatial Clustering ◽  
Detection System ◽  
Warning System ◽  
Recognition System ◽  
Target Object ◽  
Fusion Algorithm ◽  
Detection Rates ◽  
Distance Information

In this study, a millimeter-wave (MMW) radar and an onboard camera are used to develop a sensor fusion algorithm for a forward collision warning system. This study proposed integrating an MMW radar and camera to compensate for the deficiencies caused by relying on a single sensor and to improve frontal object detection rates. Density-based spatial clustering of applications with noise and particle filter algorithms are used in the radar-based object detection system to remove non-object noise and track the target object. Meanwhile, the two-stage vision recognition system can detect and recognize the objects in front of a vehicle. The detected objects include pedestrians, motorcycles, and cars. The spatial alignment uses a radial basis function neural network to learn the conversion relationship between the distance information of the MMW radar and the coordinate information in the image. Then a neural network is utilized for object matching. The sensor with a higher confidence index is selected as the system output. Finally, three kinds of scenario conditions (daytime, nighttime, and rainy-day) were designed to test the performance of the proposed method. The detection rates and the false alarm rates of proposed system were approximately 90.5% and 0.6%, respectively.

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