scholarly journals Energy Adaptive Intrusion Detection System for Energy Harvesting IoT

2019 ◽  
Vol 8 (4S2) ◽  
pp. 629-635
Keyword(s):  
Energy Harvesting ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Life Time ◽  
Sensor Data ◽  
Energy Scavenging ◽  
Security Challenges ◽  
Iot Devices ◽  
The Right

Internet of Things (IoT) is an emerging technology that makes network of physical objects which can identify, communicate and share information through Internet. The edge of IoT network are mostly simple sensors. The success of the IoT application depends on the quality of sensor data at the right time, this leads to the requirement of IoT devices be long term, self-sustaining and have the ability to harvest their required energy from deployed environment. Such devices incur additional security challenges because of prolonged life time and change in the life cycle of devices. A novel intrusion detection system is designed for energy harvesting 6LoWPAN based IoT network considering the energy scavenging characteristics of devices in addition to conventional IoT. The simulation results confirm that the proposed intrusion detection system is efficient and accurate in detecting the attacks.

scholarly journals A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1210 ◽  
Author(s):  
Khraisat ◽  
Gondal ◽  
Vamplew ◽  
Kamruzzaman ◽  
Alazab
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Support Vector ◽  
Detection Accuracy ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Iot Devices

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

A Self Organizing Map Intrusion Detection System for RPL Protocol Attacks

2019 ◽  
Vol 11 (1) ◽  
pp. 30-43 ◽  
Author(s):  
Elie Kfoury ◽  
Julien Saab ◽  
Paul Younes ◽  
Roger Achkar
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Early Stage ◽  
Sensor Data ◽  
Self Organizing Map ◽  
Lossy Networks ◽  
Mesh Topology ◽  
Som Neural Network ◽  
Self Organizing

Routing over low power and lossy networks (RPL) is a standardized routing protocol for constrained Wireless Sensor Network (WSN) environments. The main node's constraints include processing capability, power, memory, and energy. RPL protocol describes how WSN nodes create a mesh topology, enabling them to route sensor data. Unfortunately, various attacks exist on the RPL protocol that can disrupt the topology and consume nodes' energy. In this article, the authors propose an intrusion detection system (IDS) based on self-organizing map (SOM) neural network to cluster the WSN routing attacks, and hence notify the system administrator at an early stage, reducing the risk of interrupting the network and consuming nodes' power. Results showed that the proposed SOM architecture is able to cluster routing packets into three different types of attacks, as well as clean data.

scholarly journals Effect of Mutation and Crossover Probabilities on Genetic Algorithm and Signature Based Intrusion Detection System

2018 ◽  
Vol 7 (4.19) ◽  
pp. 1011
Author(s):  
Mr. Prakash N Kalavadekar ◽  
Dr. Shirish S. Sane
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Features Selection ◽  
Access Management ◽  
Intrusion Prevention ◽  
Detection Systems ◽  
Management Schemes ◽  
The Right

Conventional methods of intrusion prevention like firewalls, cryptography techniques or access management schemes, have not provided complete protection to computer systems and networks from refined malwares and attacks. Intrusion Detection Systems (IDS) are giving the right solution to the current issues and became an important part of any security management system to detect these threats and will not generate widespread harm. The basic goal of IDS is to detect attacks and their nature that may harm the computer system. Several different approaches for intrusion detection have been reported in the literature. The signature based concept using genetic algorithm as features selection and, J48 as classifier to detect attack is proposed in this paper. The system was evaluated on KDD Cup 99, NSL-KDD and Kyoto 2006+ datasets. 

scholarly journals A Supervised Intrusion Detection System for Smart Home IoT Devices

2019 ◽  
Vol 6 (5) ◽  
pp. 9042-9053 ◽  
Author(s):  
Eirini Anthi ◽  
Lowri Williams ◽  
Malgorzata Slowinska ◽  
George Theodorakopoulos ◽  
Pete Burnap
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Smart Home ◽  
Detection System ◽  
Iot Devices

scholarly journals Preventing MQTT Vulnerabilities Using IoT-Enabled Intrusion Detection System

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 567
Author(s):  
Muhammad Husnain ◽  
Khizar Hayat ◽  
Enrico Cambiaso ◽  
Ubaid U. Fayyaz ◽  
Maurizio Mongelli ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Communication Protocol ◽  
Detection System ◽  
Communication Technologies ◽  
Cyber Attacks ◽  
Iot Security ◽  
Iot Applications ◽  
Iot Devices ◽  
Main Component

The advancement in the domain of IoT accelerated the development of new communication technologies such as the Message Queuing Telemetry Transport (MQTT) protocol. Although MQTT servers/brokers are considered the main component of all MQTT-based IoT applications, their openness makes them vulnerable to potential cyber-attacks such as DoS, DDoS, or buffer overflow. As a result of this, an efficient intrusion detection system for MQTT-based applications is still a missing piece of the IoT security context. Unfortunately, existing IDSs do not provide IoT communication protocol support such as MQTT or CoAP to validate crafted or malformed packets for protecting the protocol implementation vulnerabilities of IoT devices. In this paper, we have designed and developed an MQTT parsing engine that can be integrated with network-based IDS as an initial layer for extensive checking against IoT protocol vulnerabilities and improper usage through a rigorous validation of packet fields during the packet-parsing stage. In addition, we evaluate the performance of the proposed solution across different reported vulnerabilities. The experimental results demonstrate the effectiveness of the proposed solution for detecting and preventing the exploitation of vulnerabilities on IoT protocols.

scholarly journals Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Lixin Zheng ◽  
Shangguang Wang ◽  
Chao Huang ◽  
...  
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fog Computing ◽  
Full Dataset ◽  
Security Challenges ◽  
Protection Technology ◽  
Decision Tree Method

Fog computing, as the supplement of cloud computing, can provide low-latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system (IDS) is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naïve Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty-two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.

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