IoT-Shield: A Novel DDoS Detection Approach for IoT-Based Devices

Efficient network monitoring is an important basis work for network management. Generally, many management applications require accurate and timely statistics about network states at different aggregation levels at low cost, such as malicious traffic detection, traffic engineering, etc. Moreover, the network environment to be monitored is constantly changing and expanding, including not only the data center for cloud computing but also the Internet of Things (IoT) for smart urban sensing, which requires the intensive study of more fine-grained network monitoring. As is well known, the development of efficient network monitoring approaches greatly relies on a flexible monitoring framework. Software defined network (SDN) can provide dramatic advantages for network management by separating the control plane and data plane. Therefore, it is a good choice to design a flexible monitoring framework based on the advantages of SDN. However, most research works only take advantage of the centralized control feature in SDN, which leads to limited improvement in the flexibility of the monitoring framework. This paper proposes a flexible monitoring framework named FlexMonitor, which can realize greater flexibility based on not only the centralized control feature, but also the high programmability in the controller and the limited programmability in the openflow switches in SDN. There are two key parts in FlexMonitor, namely the monitoring strategy deployment part and the monitoring data collection part, which can enrich the deployment methods of monitoring strategies and increase the kinds of monitoring data sources, respectively. Based on the NetMagic platform, this monitoring framework was implemented and evaluated through realizing a distributed denial of service (DDoS) detection approach. The experimental results show that the proposed DDoS detection approach has a better detection performance compared with other related approaches as well as indirectly show that FlexMonitor can flexibly support a variety of efficient monitoring approaches.

Download Full-text

LSTM-BA: DDoS Detection Approach Combining LSTM and Bayes

2019 Seventh International Conference on Advanced Cloud and Big Data (CBD) ◽

10.1109/cbd.2019.00041 ◽

2019 ◽

Cited By ~ 3

Author(s):

Yan Li ◽

Yifei Lu

Keyword(s):

Detection Approach ◽

Ddos Detection

Download Full-text

A DDoS Detection Approach Based on CNN in Cloud Computing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.579 ◽

2014 ◽

Vol 513-517 ◽

pp. 579-584 ◽

Cited By ~ 4

Author(s):

Zhong Xue Yang ◽

Xiao Lin Qin ◽

Wen Rui Li ◽

Ying Jie Yang

Keyword(s):

Neural Network ◽

Cloud Computing ◽

False Positive Rate ◽

Cellular Neural Network ◽

Attack Detection ◽

Ddos Attacks ◽

Information Platform ◽

Security Issues ◽

Detection Approach ◽

Ddos Detection

A novel DDoS detection approach based on Cellular Neural Network (CNN) model in cloud computing is proposed in this paper. Cloud computing is a new generation of computation and information platform, which faces many security issues owing to the characteristics such as widely distributed and heterogeneous environment, voluminous, noisy and volatile data, difficulty in communication, changing attack patterns. CNN is an artificial neural network which features a multi-dimensional array of neurons and local interconnections among cells and CNN can be used to solve the cloud security difficulties according to the nature of non-linear and dynamic. RPLA and Tabu optimized algorithm is employed to learn the CNN classifier templates and bias for DDoS intrusion detection in cloud computing. Experiments on DDoS attacks detection show that whether RPLA-CNN or Tabu-CNN models are effective for DDoS Attacks detection. Results show that CNN model for DDoS attacks detection in cloud computing exhibits an excellent performance with the higher attack detection rate with lower false positive rate.

Download Full-text

New mechanisms for the less-is-more effect: A signal detection approach

PsycEXTRA Dataset ◽

10.1037/e520562012-100 ◽

2009 ◽

Author(s):

Konstantinos V. Katsikopoulos

Keyword(s):

Signal Detection ◽

Less Is More ◽

Detection Approach

Download Full-text

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207430 ◽

2020 ◽

pp. 129-139

Author(s):

Weihai Sun ◽

Lemei Han

Keyword(s):

Fault Detection ◽

Signal Analysis ◽

Energy Analysis ◽

Detection Method ◽

Sound Signal ◽

Practical Significance ◽

Time Frequency ◽

Detection Approach ◽

Machine Fault ◽

Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Download Full-text

Associations between collectivism and perception of social threat: a signal detection approach

10.31234/osf.io/das69 ◽

2019 ◽

Author(s):

Tuong-Van Vu ◽

Catrin Finkenauer ◽

Lydia Krabbendam

Keyword(s):

Signal Detection ◽

Response Bias ◽

Social Adjustment ◽

Emotional Expressions ◽

Social Threat ◽

Threat Detection ◽

Perceptual Sensitivity ◽

Detection Approach ◽

Self Construal ◽

The Individual

Collectivistic orientation, which entails interdependent self-construal and concern for interpersonal harmony and social adjustment, has been suggested to be associated with detecting emotional expressions that signal social threat than individualistic orientation, which entails independent self-construal. The present research tested if this detection is a result of enhanced perceptual sensitivity or of response bias. We used country as proxy of individualism and collectivism (Country IC), measured IC of individuals with a questionnaire (Individual IC) and manipulated IC with culture priming (Situational IC). Dutch participants in the Netherlands (n = 143) and Chinese participants in China (n = 151) performed a social threat detection task where they had to categorize ambiguous facial expressions as “angry” or “not angry”. As the stimuli varied in degrees of scowling and frequency of presentation, we were able to measure the participants' perceptual sensitivity and response bias following the principles of the Signal Detection Theory. On the Country IC level, the results indicated that individualism-representative Dutch participants had higher perceptual sensitivity than collectivism-representative Chinese participants; whereas, Chinese participants were more biased towards categorizing a scowling face as “angry” than the Dutch (i.e. stronger liberal bias). In both groups, collectivism on the Individual IC was associated with a bias towards recognizing a scowling face as “not angry” (i.e. stronger conservative bias). Culture priming (Situational IC) affected neither perceptual sensitivity nor response bias. Our data suggested that cultural differences were in the form of behavioral tendency and IC entails multiple constructs linked to different outcomes in social threat detection.

Download Full-text