Adaptive Detection of Hello Flood Attack in Wireless Sensor Networks

Khosravi, H.; Azmi, R.; Sharghi, M.

doi:10.18178/ijfcc.2016.5.2.452

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…as increase in the time for all packets to reach their destination and in the number of lost packets). In addition, the delay is less in the proposed study, although there are more packets sent compared with that of H. Khosravi et al [22]. This study estimating better results can be used for overcoming HF attacks.…”

Section: Figure 8 Energy Consumption Of Non-malicious Nodes Figure 9mentioning

confidence: 67%

RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning

2020

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Cyberattacks targeting Internet of Things (IoT), have increased significantly, over the past decade, with the spread of internet-connected smart devices and applications. Routing Protocol for Low-Power and Lossy Network (RPL) enables messages to be routed between nodes for the Wireless Sensor Network in the network layer. RPL protocol, which is sensitive and difficult to protect, is exposed to various attacks. These attacks negatively affect data transmission and cause great destruction to the topology by consuming the resources. Hello Flooding (HF) attacks against RPL cause consumption of constrained resources (memory, processing and energy) in nodes. Therefore, in this study, a Gated Recurrent Unit network model based deep learning has been proposed to predict and prevent HF attacks on RPL protocol in IoT networks. The proposed model has been compared with Support Vector Machine and Logistic Regression methods, and different power states and total energy consumptions of the nodes have been taken into consideration and experimented with. The results confirm the promised and expected performance from the model in terms of source efficiency and IoT security. In addition, attack detection has been carried out with a much lower error rate than literature studies for HF attacks from RPL flood attacks.

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Section: Figure 8 Energy Consumption Of Non-malicious Nodes Figure 9mentioning

confidence: 67%

RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning

2020

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“…Is the Ad-hoc On-demand Distance Routing beside Hello flood Detection with Prevention which is proposed for detection of the node that generates a hello flood intrusion. Neighborhood based IDS [88] The detection of the malicious node is done based on a principle that the sensor nodes which are neighboring to each other spatially are tended to have similar behavior. If a node proves a vital variation in its behavior in contrast with other nodes in the region, it is considered as a malicious node.…”

Section: Detects the Intermediate Spoofing Attacks By Analyzing Abnormal Variations In The Receiver Signal Adaptive Intrusion Detection Smentioning

confidence: 99%

A survey of IoT security threats and defenses

Ahmed¹,

Nasr²,

Abdel-Mageid³

et al. 2019

IJACR

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The IoT points to an ever-increasing network containing the things which are not only conventional computers or mobile objects, but also the physical things similar to temperature sensors, wearable devices, watches, and other smart objects. Academics, industries, and governments are interested in studying how to connect all things in the world to the internet, called Internet of Things. Its applications contain large numbers of devices (perceptions), which are difficult to implement security methods such as encryption because of the restrictions on time, memory, processing, and energy constraints [1]. Recently, the smart devices have increased with the increased availability of distributed networks. From Figure 1. We should know that the number of devices connected to it is increasing through a positive relationship with the time. Also, the market of IoT is increasing with timeIoT is attracting for many organizations, one of them is Cisco internet business solutions group (IBSG), which reported that IoT is advantageous when the number of "objects/things" connected to the Internet is greater than the human"s connection [3]. IoT enables several applications and objects for connecting with each other through the internet to a certain scale, facilitating communication and access to information, including business as-well-as technologies related challenges to realize business benefits. These applications and objects impose a strict challenge to the security of the IoT environment and systems. For example, privacy, confidentiality, integrity, authentication, and authorization of IoT system. Moreover, the IoT environment should provide solutions for other challenges such as reliability, performance, availability, mobility, management, interoperability, scalability, and big data. IoT security is a major area of concern, it is the most impacted challenges for IoT [4].IoT architecture is divided into three layers: physical (aka perception/sensing) layer, network layer, and

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Characterization of Destructive Nodes and Analysing their Impact in Wireless Networks

Srinivas Aluvala,

V. Srikanth

2024

IJCESEN

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Mobile Ad hoc Networks (MANETs) are being used to meet new requirements for efficiency and coordination in a variety of new public and residential contexts. Certain essential functions, including as resource management among network nodes, trust-based routing, and security for network maintenance, are not performed as well as they should because of the dynamic nature of wireless networks. Ad-hoc networks can also be attacked from different tiers of a network stack, and they are susceptible to secure communications. Destructive nodes have the ability to alter or reject routing parameters. They may also provide bogus routes in an attempt to intercept source data packets and pass them through. To handle the complexity arising from secure data exchange, some protocols have been developed. However, not all attack types can be detected and eliminated by a secure protocol in every scenario. Since security is not a feature that is built into MANETs, new secure wireless protocols need to concentrate on these issues. Thus, the analysis of destructive nodes' characteristics and effects on wireless networks in this research paper examined the behaviour of multiple attacks, their activities through neighbour selection, the establishment of paths from sources to destinations, and the dissemination of attack presence detection information to regular devices during path discovery and data transmission mechanisms. In order to categorize as legitimate, nodes must be constructed with safe transmission knowledge to provide trustworthy communication, validation, honesty, and privacy.

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Adaptive Detection of Hello Flood Attack in Wireless Sensor Networks

Cited by 3 publications

References 9 publications

RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning

RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning

A survey of IoT security threats and defenses

Characterization of Destructive Nodes and Analysing their Impact in Wireless Networks

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