Improving reputation systems for wireless sensor networks using genetic algorithms

Banković, Zorana; Fraga, David; Vallejo, Juan Carlos; Moya, José Manuel Huidobro

doi:10.1145/2001576.2001798

Cited by 4 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The idea is to analyze temporal and spatial inconsistencies in routing paths using unsupervised genetic algorithm [37]. If the estimated inconsistency is higher than a certain threshold then it is assumed that there is attack and attack's location is determined.…”

Section: (Vii) Unsupervised Genetic Algorithm Based Methodmentioning

confidence: 99%

“…(i) Greyhole/selective forwarding [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] (ii) Wormhole [35][36][37][38][39][40][41][42][43] (iii) Sinkhole [26,[42][43][44][45][46][47][48][49][50] (iv) Sybil attacks [51][52][53][54][55][56][57][58][59] Since we did not find a significant number of articles for other attacks, we overview articles only for the abovementioned attacks.…”

Section: Proposed Trust Mechanisms To Secure Routingmentioning

confidence: 99%

“…(iii) HELLO Packet Received Time-Based Method. In [37] similar idea based mechanism to the above-mentioned scheme is proposed, which considers the neighboring node's HELLO packet received time. If packet is received in time, then neighbor node is considered to be legitimate.…”

Section: Trust Mechanisms Tomentioning

confidence: 99%

“…(i) Packet forwarding behavior and estimated sign of wormhole attack [35] (ii) Bandwidth requirement of the particular route and data rate of the transceiver at node [36] (iii) Previous reputation value and level of inconsistencies in routing paths [37] (iv) Spatial and temporal changes in routing path [38] (v) Packet forwarding time [39] (vi) Packet forwarding behavior and HELLO packet received time [40] (vii) Packet forwarding behavior and route length [41] (viii) Packet forwarding behavior and packet integrity [42] Although trust estimation equation is central to proposed scheme some of the schemes do not consider it [35,38,42]. Proposed trust estimation equations are diverse.…”

Section: Trust Estimation Componentmentioning

confidence: 99%

“…Energy considerations are skipped in this work too. In [37,43] routing algorithm is not considered. In [38] authors refer to use of cluster based routing, but they do not provide any details about it.…”

Section: Routing Component Generally Routing Algorithms Avoid Wormhomentioning

confidence: 99%

See 4 more Smart Citations

Trust Mechanisms to Secure Routing in Wireless Sensor Networks: Current State of the Research and Open Research Issues

Ishmanov

Zikria

2017

Journal of Sensors

View full text Add to dashboard Cite

Routing is one of the most important operations in wireless sensor networks (WSNs) as it deals with data delivery to base stations. Routing attacks can cripple it easily and degrade the operation of WSNs significantly. Traditional security mechanisms such as cryptography and authentication alone cannot cope with some of the routing attacks as they come from compromised nodes mostly. Recently, trust mechanism is introduced to enhance security and improve cooperation among nodes. In routing, trust mechanism avoids/includes nodes in routing operation based on the estimated trust value. Many trust-based routing protocols are proposed to secure routing, in which they consider different routing attacks. In this research work, our goal is to explore the current research state and identify open research issues by surveying proposed schemes. To achieve our goal we extensively analyze and discuss proposed schemes based on the proposed framework. Moreover, we evaluate proposed schemes based on two important factors, which are energy consumption and attack resiliency. We discuss and present open research issues in the proposed schemes and research field.

show abstract

Section: (Vii) Unsupervised Genetic Algorithm Based Methodmentioning

confidence: 99%

Section: Proposed Trust Mechanisms To Secure Routingmentioning

confidence: 99%

Section: Trust Mechanisms Tomentioning

confidence: 99%

Section: Trust Estimation Componentmentioning

confidence: 99%

Section: Routing Component Generally Routing Algorithms Avoid Wormhomentioning

confidence: 99%

See 3 more Smart Citations

Trust Mechanisms to Secure Routing in Wireless Sensor Networks: Current State of the Research and Open Research Issues

Ishmanov

Zikria

2017

Journal of Sensors

View full text Add to dashboard Cite

show abstract

A Time-Series Self-Supervised Learning Approach to Detection of Cyber-physical Attacks in Water Distribution Systems

Mahmoud

Gaber

2022

Energies

View full text Add to dashboard Cite

Water Distribution System (WDS) threats have significantly grown following the Maroochy shire incident, as evidenced by proofed attacks on water premises. As a result, in addition to traditional solutions (e.g., data encryption and authentication), attack detection is being proposed in WDS to reduce disruption cases. The attack detection system must meet two critical requirements: high accuracy and near real-time detection. This drives us to propose a two-stage detection system that uses self-supervised and unsupervised algorithms to detect Cyber-Physical (CP) attacks. Stage 1 uses heuristic adaptive self-supervised algorithms to achieve near real-time decision-making and detection sensitivity of 66% utilizing Boss. Stage 2 attempts to validate the detection of attacks using an unsupervised algorithm to maintain a detection accuracy of 94% utilizing Isolation Forest. Both stages are examined against time granularity and are empirically analyzed against a variety of performance evaluation indicators. Our findings demonstrate that the algorithms in stage 1 are less favored than those in the literature, but their existence enables near real-time decision-making and detection reliability. In stage 2, the isolation Forest algorithm, in contrast, gives excellent accuracy. As a result, both stages can collaborate to maximize accuracy in a near real-time attack detection system.

show abstract

Detecting Unknown Attacks in Wireless Sensor Networks That Contain Mobile Nodes

Banković

Fraga

Moya

et al. 2012

Sensors

Self Cite

View full text Add to dashboard Cite

As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

show abstract

Improving reputation systems for wireless sensor networks using genetic algorithms

Cited by 4 publications

References 9 publications

Trust Mechanisms to Secure Routing in Wireless Sensor Networks: Current State of the Research and Open Research Issues

Trust Mechanisms to Secure Routing in Wireless Sensor Networks: Current State of the Research and Open Research Issues

A Time-Series Self-Supervised Learning Approach to Detection of Cyber-physical Attacks in Water Distribution Systems

Detecting Unknown Attacks in Wireless Sensor Networks That Contain Mobile Nodes

Contact Info

Product

Resources

About