Entropy-based input-output traffic mode detection scheme for DoS/DDoS attacks

2010 ◽  
Author(s):  
Suratose Tritilanunt ◽  
Suphannee Sivakorn ◽  
Choochern Juengjincharoen ◽  
Ausanee Siripornpisan
Keyword(s):  
Ddos Attacks ◽  
Input Output ◽  
Detection Scheme ◽  
Mode Detection

Traffic Classification Features and its Application in DDoS Detection

2013 ◽  
Vol 380-384 ◽  
pp. 2673-2676
Author(s):  
Ze Yu Xiong
Keyword(s):  
Detection Method ◽  
Single Point ◽  
Attack Detection ◽  
Traffic Classification ◽  
Ddos Attacks ◽  
Detection Scheme ◽  
Ddos Detection ◽  
Point Detection ◽  
Ddos Attack Detection ◽  
Collaborative Detection

DDoS attacks have relatively low proportion of normal flow in the boundary network at the attack traffic,In this paper,we establish DDoS attack detection method based on defense stage and defensive position, and design and implement collaborative detection of DDoS attacks. Simulation results show that our approach has good timeliness, accuracy and scalability than the single-point detection and route-based distributed detection scheme.

A novel standby mode detection scheme with light load efficiency improvement

2016 ◽  
Author(s):  
Jiajun Hu ◽  
Houpeng Chen ◽  
Qian Wang ◽  
Xi Li ◽  
Xi Fan ◽  
...  
Keyword(s):  
Efficiency Improvement ◽  
Detection Scheme ◽  
Mode Detection ◽  
Light Load ◽  
Standby Mode

A jointed mode detection and symbol detection scheme for DVB-T

2008 ◽  
Vol 54 (2) ◽  
pp. 336-341 ◽  
Author(s):  
Chen Wei-Ting ◽  
Chang Liu-Wei ◽  
Jye Jou-Shyh
Keyword(s):  
Detection Scheme ◽  
Mode Detection ◽  
Symbol Detection

scholarly journals IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

2021 ◽  
Vol 19 (2) ◽  
pp. 1280-1303
Author(s):  
Jiushuang Wang ◽  
◽  
Ying Liu ◽  
Huifen Feng
Keyword(s):  
Internet Of Things ◽  
Firefly Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Detection Scheme ◽  
Ddos Attack ◽  
Improved Firefly Algorithm ◽  
Iot Devices ◽  
Ddos Attack Detection

<abstract><p>Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.</p></abstract>

Traffic Weight Based CAT Distributed Detection Scheme Against DDoS Attacks

2012 ◽  
Vol 7 (1) ◽  
pp. 192-196
Author(s):  
Zaihong Zhou ◽  
Dongqing Xie ◽  
Jiawei Luo ◽  
Jian Zhou
Keyword(s):  
Distributed Detection ◽  
Ddos Attacks ◽  
Detection Scheme

Integrating Colloidal Quantum Dots with Porous Silicon for High Sensitivity Biosensing

2011 ◽  
Vol 1301 ◽  
Author(s):  
Girija Gaur ◽  
Dmitry Koktysh ◽  
Sharon M. Weiss
Keyword(s):  
Porous Silicon ◽  
Surface Area ◽  
High Surface ◽  
High Surface Area ◽  
High Sensitivity ◽  
Detection Sensitivity ◽  
Detection Scheme ◽  
Mode Detection ◽  
Cdte Qds ◽  
Refractive Index Increase

ABSTRACTWe aim to utilize the high surface area of a porous silicon (PSi) matrix coupled with semiconductor quantum dot (QD) amplifiers for ultrasensitive optical detection of small biomolecules using a dual-mode detection scheme. In our system, QDs attached to the target biomolecule serve as signal amplifiers by providing an additional refractive index increase beyond that of the smaller target molecules. The strong photoluminescence (PL) from the QDs serves as a secondary indication of target molecule attachment in the pores. A resulting increase in optical thickness of ∼190 nm and detection sensitivity of ∼700 nm/RIU have been demonstrated for attachment of glutathione capped CdTe QDs in the porous silicon matrix. Reflectance and PL measurements, combined with simulations, have been used to characterize the surface area coverage of the QDs within the porous framework, which is estimated at 10% for glutathione capped CdTe QDs.

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