DDoS Detection and Mitigation in Cloud Via FogFiter: A Defence Mechanism

2018 ◽  
Author(s):  
Bhumika Paharia ◽  
Kriti Bhushan
Keyword(s):  
Defence Mechanism ◽  
Ddos Detection

scholarly journals Entropy Based Features Distribution for Anti-DDoS Model in SDN

2021 ◽  
Vol 13 (3) ◽  
pp. 1522
Author(s):  
Raja Majid Ali Ujjan ◽  
Zeeshan Pervez ◽  
Keshav Dahal ◽  
Wajahat Ali Khan ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Network Security ◽  
False Positive ◽  
Denial Of Service ◽  
Network Services ◽  
Detection Accuracy ◽  
Data Sets ◽  
Traffic Patterns ◽  
Average Accuracy ◽  
Ddos Detection ◽  
Quantitative Results

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.

RNN-based DDoS Detection in IoT Scenario

2020 ◽  
Author(s):  
Chun-Yu Chen ◽  
Lo-An Chen ◽  
Yun-Zhan Cai ◽  
Meng-Hsun Tsai
Keyword(s):  
Ddos Detection

scholarly journals Brightly coloured tissues in limid bivalves chemically deter predators

2019 ◽  
Vol 6 (10) ◽  
pp. 191298
Author(s):  
Lindsey F. Dougherty ◽  
Alexandria K. Niebergall ◽  
Corey D. Broeckling ◽  
Kevin L. Schauer ◽  
Jingchun Li
Keyword(s):  
Mass Spectrometry ◽  
Chemical Defence ◽  
Chemical Compositions ◽  
Marine Bivalve ◽  
Metabolomic Analysis ◽  
Defence Mechanism ◽  
Defensive Compounds ◽  
Mantis Shrimp ◽  
Metabolite Profiles ◽  
Bivalve Tissues

Members of the marine bivalve family Limidae are known for their bright appearance. In this study, their colourful tissues were examined as a defence mechanism towards predators. We showed that when attacked by the peacock mantis shrimp ( Odontodactylus scyllarus ), the ‘disco’ clam, Ctenoides ales , opened wide to expose brightly coloured tissues to the predator. The predator also significantly preferred to consume the internal, non-colourful clam tissues than the external, colourful tissues. Mass spectrometry-based metabolomic analysis confirmed that colourful tissues had significantly different chemical compositions than the non-colourful ones. The internal, non-colourful tissues had metabolite profiles more similar to an outgroup bivalve than to the species' own colourful external tissues. A number of the compounds that differentiated the colourful tissues from the non-colourful tissues appeared to be peptide-like, which potentially serve as the underlying defensive compounds. This is the first study demonstrating that colourful bivalve tissues are used for chemical defence.

Collaborative 'many to many' DDoS detection in cloud

2016 ◽  
Vol 23 (3/4) ◽  
pp. 192
Author(s):  
Siqi Ma ◽  
David Lo ◽  
Ning Xi
Keyword(s):  
Ddos Detection

What is the Evolutionary Advantage of Migraine?

Cephalalgia ◽  
2002 ◽  
Vol 22 (8) ◽  
pp. 624-632 ◽  
Author(s):  
E Loder
Keyword(s):  
Nervous System ◽  
Natural Selection ◽  
Environmental Factors ◽  
Genetic Factors ◽  
Evolutionary Perspective ◽  
Defence Mechanism ◽  
Design Constraint ◽  
Trade Off ◽  
Evolutionary Advantage ◽  
Evolutionary Explanations

Susceptibility to migraine is determined by genetic factors and is therefore subject to the forces of natural selection. Migraine is a common and ancient disorder whose prevalence may be increasing, suggesting that a migraine-prone nervous system may be associated with reproductive or survival advantages. Five evolutionary explanations are reviewed that might account for the persistence of migraine: (i) migraine as a defence mechanism; (ii) migraine as a result of conflict with other organisms; (iii) migraine as result of novel environmental factors; (iv) migraine as a trade-off between genetic harms and benefits; and (v) migraine as a design constraint. An evolutionary perspective on migraine allows the generation of important hypotheses about the disorder and suggests rewarding possibilities for further research.

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