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Special Issue "Advanced Intrusion Detection & Mitigation Systems in Wireless Sensor Networks"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editor

Prof. Dr. Salvatore Domenic Morgera
Website
Guest Editor
Department of Electrical Engineering, University of South Florida, Tampa, FL, USA
Interests: cybersecurity; 5G; cloud–fog networking; Interrnet of Things; wireless networks; military networks; neurological networks; medical devices
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless sensor network cybersecurity is a process of continuous improvement. As the number of wireless sensor networks grows, and as these networks increasingly connect to the Internet, they will become both more powerful data gathering agents and more vulnerable to malicious attacks. Encryption does not provide a sufficient level of protection from malicious attacks and, eventually, intrusion detection and mitigation (IDM) systems will be de-facto standard components of wireless sensor networks.

The complement of papers in this Special Issue will confirm the fact that designing an effective IDM system is both an engineering/computer science accomplishment and an art and requires a deep understanding of networking, combined with a realistic simulation and, if possible, field experience. Our use of the word “effective” implies that the reader will learn of advanced IDM systems that are robust and exhibit low false alarm behavior. 

Advanced IDM systems having these characteristics generally focus on the intention of an attack, rather than just a specific type of attack or methodology. These systems generally run multiple intelligent algorithms or statistical anomaly-based detectors and use sophisticated cross-layer approaches. Instead of only looking for a specific attack signature, these advanced IDM systems check cross-layer data and establish a relationship between the information in that data and its potential impact on the wireless sensor network from the security viewpoint.

In this framework, we are very pleased to edit this Special Issue on “Advanced Intrusion Detection and Mitigation Systems in Wireless Sensor Networks”. The Special Issue is dedicated to presenting advanced IDM system designs that demonstrate superior performance in wireless sensor networks. IDM system designs for other networks, such as MANETs, military networks, and high value legacy networks, are also especially welcome, as are IDM system designs for specific wireless network scenarios that employ active and passive sensing from satellite, aerial or drone platforms, methods that secure biomedical networks and medical device networks from intrusion, and intrusion detection and mitigation methods based on those found in the human immune system.

Prof. Dr. Salvatore Domenic Morgera
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Wireless sensor networks
  • Mobile ad hoc networks
  • Biomedical networks
  • Cybersecurity
  • Anomaly intrusion detection
  • Distributed intrusion detection
  • Hybrid intrusion detection
  • Cross-layer techniques
  • Machine learning
  • Deep learning
  • Human immune system

Published Papers (4 papers)

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Research

Open AccessArticle
A Machine Learning Based Intrusion Detection System for Mobile Internet of Things
Sensors 2020, 20(2), 461; https://doi.org/10.3390/s20020461 - 14 Jan 2020
Cited by 4
Abstract
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 [...] Read more.
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. Full article
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Open AccessArticle
An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
Sensors 2019, 19(20), 4579; https://doi.org/10.3390/s19204579 - 21 Oct 2019
Cited by 8
Abstract
Cluster-based hierarchical routing protocols play an essential role in decreasing the energy consumption of wireless sensor networks (WSNs). A low-energy adaptive clustering hierarchy (LEACH) has been proposed as an application-specific protocol architecture for WSNs. However, without considering the distribution of the cluster heads [...] Read more.
Cluster-based hierarchical routing protocols play an essential role in decreasing the energy consumption of wireless sensor networks (WSNs). A low-energy adaptive clustering hierarchy (LEACH) has been proposed as an application-specific protocol architecture for WSNs. However, without considering the distribution of the cluster heads (CHs) in the rotation basis, the LEACH protocol will increase the energy consumption of the network. To improve the energy efficiency of the WSN, we propose a novel modified routing protocol in this paper. The newly proposed improved energy-efficient LEACH (IEE-LEACH) protocol considers the residual node energy and the average energy of the networks. To achieve satisfactory performance in terms of reducing the sensor energy consumption, the proposed IEE-LEACH accounts for the numbers of the optimal CHs and prohibits the nodes that are closer to the base station (BS) to join in the cluster formation. Furthermore, the proposed IEE-LEACH uses a new threshold for electing CHs among the sensor nodes, and employs single hop, multi-hop, and hybrid communications to further improve the energy efficiency of the networks. The simulation results demonstrate that, compared with some existing routing protocols, the proposed protocol substantially reduces the energy consumption of WSNs. Full article
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Open AccessArticle
A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks
Sensors 2019, 19(20), 4383; https://doi.org/10.3390/s19204383 - 10 Oct 2019
Cited by 6
Abstract
An Intrusion detection system is an essential security tool for protecting services and infrastructures of wireless sensor networks from unseen and unpredictable attacks. Few works of machine learning have been proposed for intrusion detection in wireless sensor networks and that have achieved reasonable [...] Read more.
An Intrusion detection system is an essential security tool for protecting services and infrastructures of wireless sensor networks from unseen and unpredictable attacks. Few works of machine learning have been proposed for intrusion detection in wireless sensor networks and that have achieved reasonable results. However, these works still need to be more accurate and efficient against imbalanced data problems in network traffic. In this paper, we proposed a new model to detect intrusion attacks based on a genetic algorithm and an extreme gradient boosting (XGBoot) classifier, called GXGBoost model. The latter is a gradient boosting model designed for improving the performance of traditional models to detect minority classes of attacks in the highly imbalanced data traffic of wireless sensor networks. A set of experiments were conducted on wireless sensor network-detection system (WSN-DS) dataset using holdout and 10 fold cross validation techniques. The results of 10 fold cross validation tests revealed that the proposed approach outperformed the state-of-the-art approaches and other ensemble learning classifiers with high detection rates of 98.2%, 92.9%, 98.9%, and 99.5% for flooding, scheduling, grayhole, and blackhole attacks, respectively, in addition to 99.9% for normal traffic. Full article
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Open AccessArticle
Automated Vulnerability Discovery and Exploitation in the Internet of Things
Sensors 2019, 19(15), 3362; https://doi.org/10.3390/s19153362 - 31 Jul 2019
Abstract
Recently, automated software vulnerability detection and exploitation in Internet of Things (IoT) has attracted more and more attention, due to IoT’s fast adoption and high social impact. However, the task is challenging and the solutions are non-trivial: the existing methods have limited effectiveness [...] Read more.
Recently, automated software vulnerability detection and exploitation in Internet of Things (IoT) has attracted more and more attention, due to IoT’s fast adoption and high social impact. However, the task is challenging and the solutions are non-trivial: the existing methods have limited effectiveness at discovering vulnerabilities capable of compromising IoT systems. To address this, we propose an Automated Vulnerability Discovery and Exploitation framework with a Scheduling strategy, AutoDES that aims to improve the efficiency and effectiveness of vulnerability discovery and exploitation. In the vulnerability discovery stage, we use our Anti-Driller technique to mitigate the “path explosion” problem. This approach first generates a specific input proceeding from symbolic execution based on a Control Flow Graph (CFG). It then leverages a mutation-based fuzzer to find vulnerabilities while avoiding invalid mutations. In the vulnerability exploitation stage, we analyze the characteristics of vulnerabilities and then propose to generate exploits, via the use of several proposed attack techniques that can produce a shell based on the detected vulnerabilities. We also propose a genetic algorithm (GA)-based scheduling strategy (AutoS) that helps with assigning the computing resources dynamically and efficiently. The extensive experimental results on the RHG 2018 challenge dataset and the BCTF-RHG 2019 challenge dataset clearly demonstrate the effectiveness and efficiency of the proposed framework. Full article
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