scholarly journals 3S-IoT an Algorithm to make the Network Secured and Smart

2020 ◽  
Author(s):  
Maneesh Pant ◽  
Brij Mohan Singh ◽  
Dharam Vir Gupta
Keyword(s):  
Denial Of Service ◽  
Chaotic Map ◽  
High Energy ◽  
Key Generation ◽  
Multicast Communication ◽  
Multicast Traffic ◽  
Proposed Model ◽  
Man In The Middle ◽  
Multicast Communications ◽  
Actuator Control

Abstract Internet of Things (IoT) evolving and widespread presence has made the lives of all comfortable and handy, while on the other hand posing various challenges, i.e. less efficiency, less security, and high energy drain, threatening smart IoT-based applications. Compared to unicast communication, multicast communication is considered more powerful in group-oriented systems, because transmission takes place using less resources. This is why many of the IoT applications rely on multicast in their transmission. This multicast traffic needs to be handled explicitly for sensitive applications requiring actuator control. Securing multicast traffic by itself is cumbersome as it requires an efficient and flexible Group Key Establishment (GKE) protocol. We propose a three-tier model that can, not only be used to control the IoT, but also to control multicast communications. The architecture is built with a 256-bit keyless encryption technique to protect the authentication to create the network link. Machine learning-based chaotic map key generation is used to protect GKE. Finally, using MD5, the system key is authenticated. The algorithm is checked for energy used, bandwidth, and time taken. The proposed model is applied and evaluated against numerous benchmark attacks such as Distributed Denial of Service (DDoS), Man in the Middle and Fishing.

scholarly journals 3S-IoT an Algorithm to make the Network Secure and Smart

2021 ◽  
Author(s):  
maneesh pant ◽  
Brijmohan Singh ◽  
Dharam Vir Gupta
Keyword(s):  
Denial Of Service ◽  
Chaotic Map ◽  
High Energy ◽  
Key Generation ◽  
Multicast Traffic ◽  
Proposed Model ◽  
Man In The Middle ◽  
Multicast Communications ◽  
Actuator Control ◽  
And Control

Abstract The growing and widespread presence of Internet of Things (IoT) has made the lives of all comfortable and handy, but poses various challenges, like efficiency, security, and high energy drain, threatening smart IoT-based applications. Small applications rely on Unicast communication. In a group-oriented communication, multicast is better as transmission takes place using fewer resources. Therefore, many IoT applications rely on multicast transmission. To handle sensitive applications, the multicast traffic requires an actuator control. Securing multicast traffic by itself is cumbersome, as it expects an efficient and flexible Group Key Establishment (GKE) protocol. The paper proposes a three-tier model that can control the IoT and control multicast communications. The first authentication is at network linking where we used a 256-bit keyless encryption technique. Machine learning-based chaotic map key generation authenticates the GKE. Finally, MD5 establishes the system key. 3S-IoT is smart to detect any tempering with the devices. It stores signatures of the connected devices. The algorithm reports any attempt to change or temper a device. 3S-IoT can thwart attacks such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MiTM), phishing, and more. We calculated energy consumed, bandwidth, and the time taken to check the robustness of the proposed model. The results establish that 3S-IoT can efficiently deal with the attacks. The paper compares 3S-IoT with Benchmark algorithms.

scholarly journals An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
James Dzisi Gadze ◽  
Akua Acheampomaa Bamfo-Asante ◽  
Justice Owusu Agyemang ◽  
Henry Nunoo-Mensah ◽  
Kwasi Adu-Boahen Opare
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Single Point ◽  
Denial Of Service ◽  
Specific Work ◽  
Learning Models ◽  
Ddos Attack ◽  
Deep Learning Algorithm ◽  
Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

Proposed Back Propagation Deep Neural Network for Intrusion Detection in Internet of Things Fog Computing

2021 ◽  
Vol 9 (4) ◽  
pp. 464-469
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Fog Computing ◽  
Denial Of Service ◽  
Back Propagation ◽  
Security And Privacy ◽  
Brute Force ◽  
Iot Platform ◽  
Proposed Model

Internet of things (IoT) is an emerging concept which aims to connect billions of devices with each other anytime regardless of their location. Sadly, these IoT devices do not have enough computing resources to process huge amount of data. Therefore, Cloud computing is relied on to provide these resources. However, cloud computing based architecture fails in applications that demand very low and predictable latency, therefore the need for fog computing which is a new paradigm that is regarded as an extension of cloud computing to provide services between end users and the cloud user. Unfortunately, Fog-IoT is confronted with various security and privacy risks and prone to several cyberattacks which is a serious challenge. The purpose of this work is to present security and privacy threats towards Fog-IoT platform and discuss the security and privacy requirements in fog computing. We then proceed to propose an Intrusion Detection System (IDS) model using Standard Deep Neural Network's Back Propagation algorithm (BPDNN) to mitigate intrusions that attack Fog-IoT platform. The experimental Dataset for the proposed model is obtained from the Canadian Institute for Cybersecurity 2017 Dataset. Each instance of the attack in the dataset is separated into separate files, which are DoS (Denial of Service), DDoS (Distributed Denial of Service), Web Attack, Brute Force FTP, Brute Force SSH, Heartbleed, Infiltration and Botnet (Bot Network) Attack. The proposed model is trained using a 3-layer BP-DNN

Secret key generation based on private pilot under man-in-the-middle attack

2017 ◽  
Vol 60 (10) ◽  
Author(s):  
Yu Huang ◽  
Liang Jin ◽  
Na Li ◽  
Zhou Zhong ◽  
Xiaoming Xu
Keyword(s):  
Key Generation ◽  
Secret Key ◽  
Secret Key Generation ◽  
Man In The Middle

Multi-Criteria Decision Support System for Green Commercial Space Design

2018 ◽  
Vol 43 (4) ◽  
pp. 5-15
Author(s):  
Hao-Cheng Huang ◽  
Yeng-Horng Perng
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Energy Saving ◽  
Support System ◽  
Human Life ◽  
Evaluation Model ◽  
High Energy ◽  
Commercial Space ◽  
Proposed Model ◽  
Commercial Spaces

Commercial space features essential characteristics of attracting clients and creating profits; thus, the exterior and interior designs of conventional commercial space were often made to look grandiose and overdecorated. Over time, according to commercial attributes, operator preferences, and style of the designer, commercial spaces have constantly undergone renovation into varied styles. However, the physical renovation processhas caused multiple and composite types of environmental pollution, such as waste and noise pollution caused by breaking of walls or partitions, anddecorative paint pollution, as well as the use of high-energy-consuming lighting equipment, air-conditioning systems, and decorative materials. Global pollution has caused climate change, endangering living organismsand human life. Furthermore, no effective method exists to control the problem of high greenhouse gas emissions. Therefore, this study used energy-saving design concerns of a garden-type commercial space to propose an energy-saving evaluation model. The study combined three methodologies, the Delphi method, analytic hierarchy process, and fuzzy logic theory, to construct a multi-criteria decision support system for designing green commercial spaces, and used the green spatial design of a garden café as an example to illustrate the high objectivity and adaptability of the proposed model in practical application. The study also used an international award-winning case to validate that the proposed model had practical value as a reference to support key design decisions.

Malware Detection Approaches and Analysis for the Internet of Medical Things Enabled Healthcare Systems

2021 ◽  
Vol 10 (6) ◽  
pp. 3117-3121
Keyword(s):  
Denial Of Service ◽  
Malware Detection ◽  
Healthcare Systems ◽  
Information And Communications Technology ◽  
The Internet ◽  
Smart Healthcare ◽  
Man In The Middle ◽  
Internet Of Medical Things ◽  
Detection Schemes ◽  
The Internet Of Things

The advancement of information and communications technology has changed an IoMT-enabled healthcare system. The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT) that focuses on smart healthcare (medical) device connectivity. While the Internet of Medical Things (IoMT) communication environment facilitates and supports our daily health activities, it also has drawbacks such as password guessing, replay, impersonation, remote hijacking, privileged insider, denial of service (DoS), and man-in-the-middle attacks, as well as malware attacks. Malware botnets cause assaults on the system's data and other resources, compromising its authenticity, availability, confidentiality and, integrity. In the event of such an attack, crucial IoMT communication data may be exposed, altered, or even unavailable to authorised users. As a result, malware protection for the IoMT environment becomes critical. In this paper, we provide several forms of malware attacks and their consequences. We also go through security, privacy, and different IoMT malware detection schemes

Analysis of Issues in SDN Security and Solutions

2018 ◽  
pp. 217-239
Author(s):  
Ankur Dumka ◽  
Hardwari Lal Mandoria ◽  
Anushree Sah
Keyword(s):  
Network Topology ◽  
Network Architecture ◽  
Denial Of Service ◽  
Network Services ◽  
Software Defined Network ◽  
Denial Of Service Attack ◽  
Man In The Middle ◽  
Service Attack ◽  
Topology Information ◽  
The Given

The chapter surveys the analysis of all the security aspects of software-defined network and determines the areas that are prone to security attacks in the given software-defined network architecture. If the fundamental network topology information is poisoned, all the dependent network services will become immediately affected, causing catastrophic problems like host location hijacking attack, link fabrication attack, denial of service attack, man in the middle attack. These attacks affect the following features of SDN: availability, performance, integrity, and security. The flexibility in the programmability of control plane has both acted as a bane as well as a boon to SDN. Like the ARP poisoning in the legacy networks, there are several other vulnerabilities in the SDN architecture as well.

Congestion Avoidance and Delay Minimization in Energy Aware Routing of Dynamic ieee 802.11s WMN

2016 ◽  
Vol 7 (1) ◽  
pp. 85-112 ◽  
Author(s):  
S.P. Shiva Prakash ◽  
T.N. Nagabhushan ◽  
Kirill Krinkin
Keyword(s):  
Mesh Networks ◽  
Cluster Head ◽  
High Energy ◽  
Base Station ◽  
Base Stations ◽  
Cluster Head Selection ◽  
Slot Allocation ◽  
Proposed Model ◽  
Slot Scheduling ◽  
Link Duration

Minimization of delay in collecting the data at any base stations is one of the major concerns in cluster based Wireless Mesh Networks. several researches have proposed algorithms to control congestion considering static nature of a node. Mobility of a node results in high congestion due to frequent link breakages and high energy consumption due to re-establishment of route during routing process. Hence, the authors consider dynamic nodes with single hop inside the static cluster. The proposed model includes four modules namely, Cluster head selection, slot allocation, slot scheduling and data collection process. the cluster head selection is based on the maximum energy, number of links and link duration. Slot allocation is based on the available energy () and the required energy (). Slot scheduling is carried out based on the link duration. Data at the base station will be collected as they are scheduled. Model is tested using Network Simulator-3 (NS3) and results indicate that the proposed model achieves least delay besides reducing the congestion compared to the existing methods.

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