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Prediction of DDoS Attacksusing Machine Learning and Deep Learning Algorithms
2019
International journal of recent technology and engineering
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The DDoS attack is the most significant network-based attack in the domain of computer security that disrupts the internet traffic of the target server. ...
This study mainly focuses to identify the advancements and research gaps in the development of efficient security algorithms addressing DDoS attacks in various ubiquitous network environments. ...
In the context of resolving the problems of the statistical models in detecting and predicting DDoS attacks, the researchers have focused on the deep and machine learning algorithms to develop context-aware ...
doi:10.35940/ijrte.d8162.118419
fatcat:yrug2c32cfge3go6fatnpp2y2a
Towards Situational Awareness of Botnet Activity in the Internet of Things
2018
2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA)
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On September 20 2016 the Mirai botnet was used to perform an unprecedented 620 Gbps DDoS attack on security journalist Brian Krebs website krebsonsecurity.com [9] . ...
leveraged to perform large scale DDoS attacks against targets on the Internet. ...
doi:10.1109/cybersa.2018.8551408
dblp:conf/cybersa/McDermottPM18
fatcat:ihsy2qmiwzfqxfleqguvctsofe
Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments
2021
IEEE Access
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Additionally, the paper discusses different DDoS defense systems based on ML techniques that make use of a virtualized environment, including cloud computing, software-defined network, and network functions ...
In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks. ...
[81] proposedan attack-aware security provisioning approach for DDoS attack mitigation in the context of the SDN/NFV combination with a 5G network. ...
doi:10.1109/access.2021.3062909
fatcat:xtj576lfsffrbpiqyk2kv5wuam
Features-Aware DDoS Detection in Heterogeneous Smart Environments based on Fog and Cloud Computing
2021
International Journal of Communication Networks and Information Security
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One of the existing problems of the SEs is the detection of Distributed Denial of Service (DDoS) attacks, due to the vulnerabilities of IoT devices. ...
The experiments performed, using real network traffic, suggest that the proposed system reaches 99% of accuracy, while reduces the volume of data exchanged and the detection time. ...
the network flows to detect DDoS attacks. ...
doi:10.54039/ijcnis.v13i3.5080
fatcat:uchepdwvhrgerntn3vsh4k6ns4
Analysis of Different Attacks on Software Defined Network and Approaches to Mitigate using Intelligent Techniques
2021
International Journal of Advanced Computer Science and Applications
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The detection of DDoS (Distributed Denial of Service) attacks is essential topic under network security. ...
DDoS attacks cause network services to become unavailable by repeatedly flooding servers with unwanted traffic. ...
DEEP LEARNING DDoS attacks are still the most common and lethal danger to current and next-generation network systems. ...
doi:10.14569/ijacsa.2021.0120938
fatcat:r5cm4po2rnashlmu54kz2bhpgu
Effective Attack Detection in Internet of Medical Things Smart Environment using a Deep Belief Neural Network
2020
IEEE Access
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In this work, we have proposed a deep learning-based method Deep Belief Network (DBN) algorithm model for the intrusion detection system. ...
Security and privacy are viewed as main problems in any technology's dependence on the IoT model. Privacy and security issues arise due to the different possible attacks caused by intruders. ...
PROPOSED ALGORITHM AND PROPOSED METHODOLOGY (DEEP BELIEF NETWORK DBNs are generative techniques. ...
doi:10.1109/access.2020.2986013
fatcat:e4svv3od3jfsjavrowmqgvsaii
Botnet Detection in the Internet of Things using Deep Learning Approaches
2018
2018 International Joint Conference on Neural Networks (IJCNN)
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On September 20 2016 the Mirai botnet was used to perform an unprecedented 620 Gbps DDoS attack on security journalist Brian Krebs website krebsonsecurity.com [8] . ...
An attacker (botmaster) then uses a command and control (C&C) server to remotely control the bots, forcing them to participate in DDoS attacks against targets on the Internet. ...
doi:10.1109/ijcnn.2018.8489489
dblp:conf/ijcnn/McDermottMP18
fatcat:tjl2u6unanf6dagt2gj43iabpe
An Exploratory Study of Cognitive Sciences Applied to Cybersecurity
2022
Electronics
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papers chosen in a systematic literature review that was carried out using PRISMA methodology. ...
For achieving this, we developed exploratory research based on two steps: (1) a text mining process to identify main interest areas of research in the cybersecurity field and (2) a valuable review of the ...
thank the Universidad de las Fuerzas Armadas ESPE of Sangolquí, Ecuador, for the resources granted for the development of the research project entitled: "Detection and Mitigation of Social Engineering attacks ...
doi:10.3390/electronics11111692
fatcat:w64k5p6rc5at5ak6d4xfpuol5m
Comparison of Classification Model for the Detection of Cyber-attack using Ensemble Learning Models
2022
EAI Endorsed Transactions on Scalable Information Systems
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Machine learning algorithms were trained to predict cyber-attack scores using data from prior cyber-attacks on an open source website. ...
It is possible to predict the future by using machine learning. A cyber-attack detection system is depicted in this study using machine learning models. ...
Author hope author's framework will aid in the development of context-aware IoT security solutions for sensitive use cases, such as healthcare. ...
doi:10.4108/eai.1-2-2022.173293
fatcat:gcw7ujjdfjccxhb7s4feovpwye
Machine Learning in IoT Security: Current Solutions and Future Challenges
[article]
2019
arXiv
pre-print
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The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. ...
The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. ...
As mentioned before, MQTT and CoAP are two of the most widely used telemetry protocols in IoT. Pacheco et al. [115] evaluated the effect of DDoS attack on generic IoT network that uses CoAP. ...
arXiv:1904.05735v1
fatcat:k5v6zad7lfhdrjngjmxgroafz4
Intelligent Traffic Management in Next-Generation Networks
2022
Future Internet
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These methods can model and learn network traffic behavior using training data/environments. ...
The recent development of smart devices has lead to an explosion in data generation and heterogeneity. ...
As a result, several ML models have been used for DDoS attack detection in the SDN environment. In this context, Ahmad et al. ...
doi:10.3390/fi14020044
fatcat:dx6leecgtfegdlao5aq6p6ouci
DEA: Anomaly Detection in Smart Environments using Artificial Intelligence
2019
Latin American Network Operations and Management Symposium
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Within this context, this paper presents the DEA project, which aims to develop a system based on AI to monitor network traffic and to detect anomalies in smart environments, generating a profile of the ...
network and detecting out of order behaviors (different from the behavior pattern). ...
The final result of DEA project is an context-aware adaptive anomaly detection system based on AI. ...
dblp:conf/lanoms/MoreiraMCGSN19
fatcat:ua5fi63whjflvixfkkkmol2heq
Towards the transversal detection of DDoS network attacks in 5G multi-tenant overlay networks
2018
Computers & security
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A novel approach which significantly extends the capabilities of a commonly used IDS, to accurately identify attacking nodes in a 5G network, regardless of multiple network traffic encapsulations, has ...
This paper focuses on addressing a transversal detection system to be able to protect at the same time, infrastructures, tenants and 5G users in both edge and core network segments of the 5G multitenant ...
His current research focus is User Profiling, Ontology, Knowledge Management, and Context-aware Computing. ...
doi:10.1016/j.cose.2018.07.017
fatcat:rehm5346bfaxdfc2jhtuetvyfy
A Flow Based Anomaly Detection Approach with Feature Selection Method Against DDoS Attacks in SDNs
2022
IEEE Transactions on Cognitive Communications and Networking
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JES ÚS et al. [42] applied six ML algorithms, REP Tree, SVM, MLP, RF, J48 and Random Tree for DDoS attack detection under the SDN context. ...
attacks without the awareness of the device's rightful owner. ...
doi:10.1109/tccn.2022.3186331
fatcat:f6bssjotwbexnjqngtq7hyo624
CoAP-DoS: An IoT Network Intrusion Dataset
[article]
2022
arXiv
pre-print
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We develop a new data set by collecting network traffic from real CoAP denial of service attacks and compare the data on multiple different machine learning classifiers. ...
There are many network traffic data sets but very few that focus on IoT network traffic. Within the IoT network data sets there is a lack of CoAP denial of service data. ...
A model for classifying malicious attacks on patient enabled IoT context aware devices used in a devices medical situation and a CoAP/MQTT data set were developed in [13] . ...
arXiv:2206.14341v1
fatcat:rgaft3c22zgs3cfh5a7r6u7usa
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