scholarly journals ForChaos: Real Time Application DDoS Detection Using Forecasting and Chaos Theory in Smart Home IoT Network

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Andria Procopiou ◽  
Nikos Komninos ◽  
Christos Douligeris
Keyword(s):  
Chaos Theory ◽  
Smart Home ◽  
Detection Algorithm ◽  
Home Networks ◽  
Ddos Attacks ◽  
Dos Attacks ◽  
Network Systems ◽  
Ddos Detection ◽  
Series Of Experiments ◽  
Iot Devices

Recently, D/DoS attacks have been launched by zombie IoT devices in smart home networks. They pose a great threat to network systems with Application Layer DDoS attacks being especially hard to detect due to their stealth and seemingly legitimacy. In this paper, we propose ForChaos, a lightweight detection algorithm for IoT devices, which is based on forecasting and chaos theory to identify flooding and DDoS attacks. For every time-series behaviour collected, a forecasting-technique prediction is generated, based on a number of features, and the error between the two values is calculated. In order to assess the error of the forecasting from the actual value, the Lyapunov exponent is used to detect potential malicious behaviour. In NS-3 we evaluate our detection algorithm through a series of experiments in flooding and slow-rate DDoS attacks. The results are presented and discussed in detail and compared with related studies, demonstrating its effectiveness and robustness.

scholarly journals DDoS Detection on Internet of Things using Unsupervised Algorithms

2021 ◽  
Vol 297 ◽  
pp. 01005
Author(s):  
Hailyie Tekleselassie
Keyword(s):  
Learning Algorithms ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Accuracy Score ◽  
Ddos Attacks ◽  
Knowledge Based ◽  
Ddos Detection ◽  
Iot Devices ◽  
Unsupervised Algorithms

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

Multi-Aspect DDOS Detection System for Securing Cloud Network

2019 ◽  
pp. 1952-1983
Author(s):  
Pourya Shamsolmoali ◽  
Masoumeh Zareapoor ◽  
M.Afshar Alam
Keyword(s):  
Cloud Computing ◽  
Detection System ◽  
Denial Of Service ◽  
Complex Form ◽  
Internet Security ◽  
Detection Methods ◽  
Ddos Attacks ◽  
Dos Attacks ◽  
Ddos Detection ◽  
Cloud Network

Distributed Denial of Service (DDoS) attacks have become a serious attack for internet security and Cloud Computing environment. This kind of attacks is the most complex form of DoS (Denial of Service) attacks. This type of attack can simply duplicate its source address, such as spoofing attack, which defending methods do not able to disguises the real location of the attack. Therefore, DDoS attack is the most significant challenge for network. In this chapter we present different aspect of security in Cloud Computing, mostly we concentrated on DDOS Attacks. The Authors illustrated all types of Dos Attacks and discussed the most effective detection methods.

scholarly journals AntibIoTic: Protecting IoT Devices Against DDoS Attacks

2017 ◽  
Author(s):  
Michele De Donno ◽  
Nicola Dragoni ◽  
Alberto Giaretta ◽  
Manuel Mazzara
Keyword(s):  
Denial Of Service ◽  
Main Idea ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Dos Attacks ◽  
The World ◽  
Iot Devices

The 2016 is remembered as the year that showed to the world how dangerous distributed Denial of Service attacks can be. Gauge of the disruptiveness of DDoS attacks is the number of bots involved: the bigger the botnet, the more powerful the attack. This character, along with the increasing availability of connected and insecure IoT devices, makes DDoS and IoT the perfect pair for the malware industry. In this paper we present the main idea behind AntibIoTic, a palliative solution to prevent DoS attacks perpetrated through IoT devices.

scholarly journals A Secure Internet of Things Smart Home Network: Design and Configuration

2021 ◽  
Vol 11 (14) ◽  
pp. 6280
Author(s):  
Jinsuk Baek ◽  
Munene W. Kanampiu ◽  
Cheonshik Kim
Keyword(s):  
Network Design ◽  
Smart Home ◽  
Home Network ◽  
Secure Communications ◽  
Home Networks ◽  
Ip Address ◽  
Home Gateway ◽  
Extended Access ◽  
Significant Performance ◽  
Iot Devices

Many home IoT devices are joining IoT networks by gaining access to some home gateway that configures smart, multimedia, and home networks. To enable secure IoT-based home networking services, (1) an IoT network should be effectively designed and configured with a IoT server, (2) a messaging protocol is required to exchange information between the IoT server and IoT devices, and (3) the home gateway should monitor all safety aspects in both inbound and outbound traffic of the home network. However, not all home network users put in consideration the need for an adequate security posture. Instead, many users still rely on the minimum home network security by setting an easiest-to-guess password to restrict unauthorized access to their home gateway. In this paper, we propose a network design and configuration that enables secure IoT services with MQTT messaging protocol for home networks. With the proposed network design, a home network is interconnected to external networks through a home gateway. To separate the IoT-subnet from other parts of home network, the home gateway subdivides a home network into an inside-subnet and an IoT-subnet with a private IP address using subnet masking. The IoT server, located in the IoT-subnet can be implemented with either a general HTTP server or a security server that acts as an MQTT broker. The secure communications among network entities are governed by a home gateway operating a well-configured extended access control. The effectiveness of the proposed design and configuration is verified through a simulation by showing that it does not impose any significant performance degradation for reinforced security. We expect the proposed configuration to help facilitate interconnection among heterogeneous network entities.

Multi-Aspect DDOS Detection System for Securing Cloud Network

2017 ◽  
pp. 222-252
Author(s):  
Pourya Shamsolmoali ◽  
Masoumeh Zareapoor ◽  
M.Afshar Alam
Keyword(s):  
Cloud Computing ◽  
Detection System ◽  
Denial Of Service ◽  
Complex Form ◽  
Internet Security ◽  
Detection Methods ◽  
Ddos Attacks ◽  
Dos Attacks ◽  
Ddos Detection ◽  
Cloud Network

Distributed Denial of Service (DDoS) attacks have become a serious attack for internet security and Cloud Computing environment. This kind of attacks is the most complex form of DoS (Denial of Service) attacks. This type of attack can simply duplicate its source address, such as spoofing attack, which defending methods do not able to disguises the real location of the attack. Therefore, DDoS attack is the most significant challenge for network. In this chapter we present different aspect of security in Cloud Computing, mostly we concentrated on DDOS Attacks. The Authors illustrated all types of Dos Attacks and discussed the most effective detection methods.

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

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