Development of Denial of Service (DoS) Mitigation for Internet of Things (IoT) Sensor Node

2019 ◽  
Vol 4 (2) ◽  
pp. 26-34
Author(s):  
Abdul Fuad Abdul Rahman ◽  
Azni Ab Halim ◽  
Nurul Syazwani ◽  
Maslina Daud ◽  
Madihah Zulfa Mohamad ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Sensor Node ◽  
Detection System ◽  
Denial Of Service ◽  
Sensor Nodes ◽  
Security Threat ◽  
Smart Water ◽  
Keywords Internet ◽  
Water Sensor ◽  
Using Data

ABSTRACT Objective - The objective of this paper is to propose a lightweight IDS algorithm to secure IoT Sensor Node. Methodology/Technique - The proposed IDS algorithm for IoT Sensor Node shall prevent the abnormal energy consumption by monitoring, calculating, and evaluating energy drop from each cluster nodes based on a few conditions. Findings - The DoS attack is considered as one of security threat that may affect the quality service of IoT network and also reduce the lifespan of IoT Sensor Nodes Novelty - The approach is using data from previous experiments and translated it to develop mitigation to secure IoT Sensor Node, thus increased the lifespan of IoT Sensor Nodes. Type of Paper: Other. Keywords: Internet of Things (IoT); Intrusion Detection System (IDS); Denial of Service (DoS); Smart Water; Sensor.

scholarly journals Anomaly Detection in Wireless Sensor Networks: A Proposed Framework

2020 ◽  
Vol 14 (10) ◽  
pp. 150
Author(s):  
Dina M. Ibrahim ◽  
Nada M. Alruhaily
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Research Problem ◽  
Sensor Nodes ◽  
Base Stations ◽  
Sensor Data ◽  
Wireless Sensor

With the rise of IOT devices and the systems connected to the internet, there was, accordingly, an ever-increasing number of network attacks (e.g. in DOS, DDOS attacks). A very significant research problem related to identifying Wireless Sensor Networks (WSN) attacks and the analysis of the sensor data is the detection of the relevant anomalies. In this paper, we propose a framework for intrusion detection system in WSN. The first two levels are located inside the WSN, one of them is between sensor nodes and the second is between the cluster heads. While the third level located on the cloud, and represented by the base stations. In the first level, which we called light mode, we simulated an intrusion traffic by generating data packets based on TCPDUMP data, which contain intrusion packets, our work, is done by using WSN technology. We used OPNET simulation for generating the traffic because it allows us to collect intrusion detection data in order to measure the network performance and efficiency of the simulated network scenarios. Finally, we report the experimental results by mimicking a Denial-of-Service (DOS) attack. <em> </em>

scholarly journals Cryptanalysis and Improvement of a Privacy-Preserving Three-Factor Authentication Protocol for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4625 ◽  
Author(s):  
Km Renuka ◽  
Sachin Kumar ◽  
Saru Kumari ◽  
Chien-Ming Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Denial Of Service ◽  
Authentication Protocol ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Strong Base ◽  
Gateway Node ◽  
Three Factor

Wireless sensor networks (WSNs) are of prominent use in unmanned surveillance applications. This peculiar trait of WSNs is actually the underlying technology of various applications of the Internet of Things (IoT) such as smart homes, smart cities, smart shopping complexes, smart traffic, smart health, and much more. Over time, WSNs have evolved as a strong base for laying the foundations of IoT infrastructure. In order to address the scenario in which a user wants to access the real-time data directly from the sensor node in wireless sensor networks (WSNs), Das recently proposed an anonymity-preserving three-factor authentication protocol. Das’s protocol is suitable for resource-constrained sensor nodes because it only uses lightweight cryptographic primitives such as hash functions and symmetric encryption schemes as building blocks. Das’s protocol is claimed to be secure against different known attacks by providing formal security proof and security verification using the Automated Validation of Internet Security Protocols and Applications tool. However, we find that Das’s protocol has the following security loopholes: (1) By using a captured sensor node, an adversary can impersonate a legal user to the gateway node, impersonate other sensor nodes to deceive the user, and the adversary can also decrypt all the cipher-texts of the user; (2) the gateway node has a heavy computational cost due to user anonymity and thus the protocol is vulnerable to denial of service (DoS) attacks. We overcome the shortcomings of Das’s protocol and propose an improved protocol. We also prove the security of the proposed protocol in the random oracle model. Compared with the other related protocols, the improved protocol enjoys better functionality without much enhancement in the computation and communication costs. Consequently, it is more suitable for applications in WSNs

A Reusable Device Driver Framework for the Sensor Nodes in Internet of Things

2012 ◽  
Vol 468-471 ◽  
pp. 60-63
Author(s):  
Xiao Fan Wu ◽  
Jia Jun Bu ◽  
Chun Chen
Keyword(s):  
Operating System ◽  
Internet Of Things ◽  
Embedded System ◽  
Sensor Node ◽  
Rapid Development ◽  
Source Code ◽  
Device Driver ◽  
Sensor Nodes ◽  
C Programming Language ◽  
C Programming

Due to the rapid development of Internet of Things (IoT), kinds of sensor nodes have been introduced to the different applications. Because of the variety of MCUs, sensors and radio modules, it’s challenging to reuse the device drivers between different sensor node platforms. To address this issue, a reusable device driver framework is proposed in this paper. Comparing with existed work, our framework is flexible, efficient, and easy to learn. The flexibility is achieved by layered encapsulation, which decouples the device driver with the sensor node operating system kernel. Our framework gives the reusability at the source code level, so it’s efficient. At the end, our framework is implemented in C programming language, which is the most common tool adopted by embedded system developing. This framework has applied to SenSpire OS, a micro-kernel real-time operating system for IoT sensor nodes.

scholarly journals Review on Malware and Malware Detection ‎Using Data Mining Techniques

2017 ◽  
Vol 25 (5) ◽  
pp. 1585-1601
Author(s):  
Wesam S Bhaya ◽  
Mustafa A Ali
Keyword(s):  
Data Mining ◽  
Computer Security ◽  
Computer System ◽  
Private Information ◽  
Detection System ◽  
Denial Of Service ◽  
Malware Detection ◽  
Malicious Software ◽  
Attack Data ◽  
Using Data

Malicious software is any type of software or codes which hooks some: private information, data from the computer system, computer operations or(and) merely just to do malicious goals of the author on the computer system, without permission of the computer users. (The short abbreviation of malicious software is Malware). However, the detection of malware has become one of biggest issues in the computer security field because of the current communication infrastructures are vulnerable to penetration from many types of malware infection strategies and attacks.  Moreover, malwares are variant and diverse in volume and types and that strictly explode the effectiveness of traditional defense methods like signature approach, which is unable to detect a new malware. However, this vulnerability will lead to a successful computer system penetration (and attack) as well as success of more advanced attacks like distributed denial of service (DDoS) attack. Data mining methods can be used to overcome limitation of signature-based techniques to detect the zero-day malware. This paper provides an overview of malware and malware detection system using modern techniques such as techniques of data mining approach to detect known and unknown malware samples.

scholarly journals Sistem Pendeteksi Ancaman Keamanan Rumah Dengan Menggunakan Telegram Berbasis Internet Of Things

2021 ◽  
Vol 20 (1) ◽  
pp. 127-132
Author(s):  
Fadilah Eka Prasetiyo ◽  
Didik Setiyadi Setiyadi
Keyword(s):  
Internet Of Things ◽  
Smart Home ◽  
Detection System ◽  
Security System ◽  
The Internet ◽  
Threat Detection ◽  
Security Threat ◽  
Home Owner ◽  
Long Time ◽  
The Internet Of Things

The comfort and safety of a house is the dream of any home owner, even a house that has a modern security system will be more in demand than a house with an ordinary security system. By utilizing existing technology, it is possible to create an excellent security system from theft and fire. In order to overcome these problems, a prototype of a security threat detection system was made using telegrams based on the internet of things. This can minimize the inconvenience of home owners when they are not at home in a long time, such as the owner of the house going out of town or abroad. The design of this smart home uses the NodeMCU ESP8266 Wifi Module as a controller, the telegram application as a notification when an unknown person opens a door or window, and when a fire occurs. The sensor used to detect the security of burglars is a Magnetic Door Switch, this sensor is placed on doors and windows. The sensor used to detect fire indications is the Flame Sensor which is placed on the ceiling of the house

scholarly journals A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 461 ◽  
Author(s):  
Amar Amouri ◽  
Vishwa T. Alaparthy ◽  
Salvatore D. Morgera
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Power Level ◽  
Denial Of Service ◽  
Mobile Internet ◽  
Malicious Nodes ◽  
Detection Rates ◽  
Super Node

Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.

scholarly journals A New Neural Network-Based Intrusion Detection System for Detecting Malicious Nodes in WSNs

2020 ◽  
Vol 1 (3) ◽  
pp. 1-8
Author(s):  
Narmatha C ◽  
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Key Management ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Fuzzy Rule ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Malicious Nodes

The Wireless Sensor Networks (WSNs) are vulnerable to numerous security hazards that could affect the entire network performance, which could lead to catastrophic problems such as a denial of service attacks (DoS). The WSNs cannot protect these types of attacks by key management protocols, authentication protocols, and protected routing. A solution to this issue is the intrusion detection system (IDS). It evaluates the network with adequate data obtained and detects the sensor node(s) abnormal behavior. For this work, it is proposed to use the intrusion detection system (IDS), which recognizes automated attacks by WSNs. This IDS uses an improved LEACH protocol cluster-based architecture designed to reduce the energy consumption of the sensor nodes. In combination with the Multilayer Perceptron Neural Network, which includes the Feed Forward Neutral Network (FFNN) and the Backpropagation Neural Network (BPNN), IDS is based on fuzzy rule-set anomaly and abuse detection based learning methods based on the fugitive logic sensor to monitor hello, wormhole and SYBIL attacks.

scholarly journals Hybrid Intrusion Detection System for Internet of Things (IoT)

2020 ◽  
Vol 2 (4) ◽  
pp. 190-199 ◽  
Author(s):  
Dr. S. Smys ◽  
Dr. Abul Basar ◽  
Dr. Haoxiang Wang
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Research Work ◽  
Experimental Result ◽  
Security Issues ◽  
Wide Range ◽  
Conventional Machine

Internet of things (IoT) is a promising solution to connect and access every device through internet. Every day the device count increases with large diversity in shape, size, usage and complexity. Since IoT drive the world and changes people lives with its wide range of services and applications. However, IoT provides numerous services through applications, it faces severe security issues and vulnerable to attacks such as sinkhole attack, eaves dropping, denial of service attacks, etc., Intrusion detection system is used to detect such attacks when the network security is breached. This research work proposed an intrusion detection system for IoT network and detect different types of attacks based on hybrid convolutional neural network model. Proposed model is suitable for wide range of IoT applications. Proposed research work is validated and compared with conventional machine learning and deep learning model. Experimental result demonstrate that proposed hybrid model is more sensitive to attacks in the IoT network.

scholarly journals Efficient Intrusion Detection System for SDN Orchestrated Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Esubalew M. Zeleke ◽  
Henock M. Melaku ◽  
Fikreselam G. Mengistu
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Service Availability ◽  
Dos Attacks ◽  
Network Systems ◽  
Security Challenge ◽  
Accuracy Result

Internet of Things (IoT) can simply be defined as an extension of the current Internet system. It extends the human to human interconnection and intercommunication scenario of the Internet by including things, to bring anytime, anywhere, and anything communication. A discipline in networking evolving in parallel with IoT is Software Defined Networking (SDN). It is an important technology that is aimed to solve the different problems existing in the traditional network systems. It provides a new convenient home to address the different challenges existing in different network-based systems including IoT. One important security challenge prevailing in such SDN-based IoT (SDIoT) systems is guarantying service availability. The ever-increasing denial of service (DoS) attacks are responsible for such service denials. A centralized signature-based intrusion detection system (IDS) is proposed and developed in this work. Random Forest (RF) classifier is used for training the model. A very popular and recent benchmark dataset, CICIDS2017, has been used for training and validating the machine learning (ML) models. An accuracy result of 99.968% has been achieved by using only 12 features on Wednesday’s release of the dataset. This result is higher than the achieved accuracy results of related works considering the original CICIDS2017 dataset. A maximum cross-validated accuracy result of 99.713% has been achieved on the same release of the dataset. These developed models meet the basic requirement of a supervised IDS system developed for smart environments and can effectively be used in different IoT service scenarios.

scholarly journals Multiple intrusion detection in RPL based networks

2020 ◽  
Vol 10 (1) ◽  
pp. 467 ◽  
Author(s):  
Manjula C. Belavagi ◽  
Balachandra Muniyal
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Network Size ◽  
Sensor Nodes ◽  
Machine Learning Techniques ◽  
Lossy Networks ◽  
Malicious Nodes ◽  
Denial Of Service Attack

<span lang="EN-US">Routing Protocol for Low Power and Lossy Networks based networks consists of large number of tiny sensor nodes with limited resources. These nodes are directly connected to the Internet through the border router. Hence these nodes are susceptible to different types of attacks. The possible attacks are rank attack, selective forwarding, worm hole and Denial of service attack. These attacks can be effectively identified by intrusion detection system model. The paper focuses on identification of multiple intrusions by considering the network size as 10, 40 and 100 nodes and adding 10%, 20% and 30% of malicious nodes to the considered network. Experiments are simulated using Cooja simulator on Contiki operating system. Behavior of the network is observed based on the percentage of inconsistency achieved, energy consumption, accuracy and false positive rate. Experimental results show that multiple intrusions can be detected effectively by machine learning techniques.</span>

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