AEF : Adaptive en-route filtering to extend network lifetime in wireless sensor networks

Funding Information: This work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2017R1A2B2012337). This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (Ministry of Science and ICT) NRF-2017R1A2B2012337). Publisher Copyright: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.Peer reviewe

Gafor : Genetic algorithm based fuzzy optimized re-clustering in wireless sensor networks

Acknowledgments: The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing. Funding: This research was funded by King Saud University in 2020.Peer reviewedPublisher PD

A false injection-resilient scheme to monitor time-variant phenomenon in wireless sensor networks

Being a promising technology which is envisioned to pervade numerous aspects of human life, wireless sensor networks are attracting remarkable attention in research community. The typical wireless sensors are small low-power, resource-constrained devices subject to functional failures which could be due to power loss or even malicious attacks on the devices. As the projected applications for wireless sensor networks range from smart applications such as traffic monitoring to critical military applications such as measuring levels of gas concentration in battle fields, security in sensor networks becomes a prime concern. In sensitive applications, it becomes imperative to continuously monitor the transient state of the system rather than steady state observations and take requisite preventive and corrective actions, if necessary. Also, the network is prone to attack by adversaries who intend to disrupt the functioning of the system by compromising the sensor nodes and injecting false data into the network. So it is important to shield the sensor network from false data injection attacks. Through this work, we prove that in the presence of adversaries, it would be difficult to correctly observe the transient phenomenon if sensors report just their readings. We develop a novel robust statistical framework to monitor correctly the transient phenomenon while limiting the impact of false data injection. In this framework, each sensor does a lightweight computation and reports a statistical digest in addition to the current sensed reading. Through a series of carefully-designed inter-sensor statistical tests on both the readings and digests, we are able to achieve our goal of preserving the transient phenomenon. We show a concrete realization of our statistical framework by developing a secure statistical scheme, called SSTF, to effectively monitor the transient phenomenon while being immune to false data injection attacks. SSTF is a two-tier system and the kernel of SSTF is our statistical framework, which is employed atop an enhanced version of the IHHAS security scheme. We present detailed theoretical analysis and in-depth simulation results to demonstrate the effectiveness of SSTF

PCAD: Power control attack detection in wireless sensor networks

Security in wireless sensor networks is critical due to its way of open communication. In this paper we have provided a solution to detect malicious nodes which perform radio transmission power control attack and sinkhole attack in wireless sensor networks. In the proposed approach, data transmission is divided into multiple rounds of equal time duration. Each node chooses the parent node in the beginning of the round for forwarding the packet towards sink. Each node adds its identity in the packet as a routing path marker and encrypts before forwarding to parent. Child node observes the parent, handles acknowledgement from 2-hop distance node and decides the trust on parent based on successful and unsuccessful transactions. Each node sends a trust value report via multiple paths to Sink at the end of the round. Sink identifies the malicious node by comparing trust value report received from each node with number of data packets received. Simulated the algorithm in NS-3 and performance analysis compared with other recently proposed approach. Simulation results show that proposed method detect the malicious nodes efficiently and early. © 2016 IEEE

Intrusion-Resilient Integrity in Data-Centric Unattended WSNs

Unattended Wireless Sensor Networks (UWSNs) operate in autonomous or disconnected mode: sensed data is collected periodically by an itinerant sink. Between successive sink visits, sensor-collected data is subject to some unique vulnerabilities. In particular, while the network is unattended, a mobile adversary (capable of subverting up to a fraction of sensors at a time) can migrate between compromised sets of sensors and inject fraudulent data. In this paper, we provide two collaborative authentication techniques that allow an UWSN to maintain integrity and authenticity of sensor data-in the presence of a mobile adversary-until the next sink visit. Proposed schemes use simple, standard, and inexpensive symmetric cryptographic primitives, coupled with key evolution and few message exchanges. We study their security and effectiveness, both analytically and via simulations. We also assess their robustness and show how to achieve the desired trade-off between performance and security

IMNTV-Identifying Malicious Nodes using Trust Value in Wireless Sensor Networks

Security is the major area of concern in communication channel. Security is very crucial in wireless sensor networks which are deployed in remote environments. Adversary can disrupt the communication within multi hop sensor networks by launching the attack. The common attacks which disrupt the communication of nodes are packet dropping, packet modification, packet fake routing, badmouthing attack and Sybil attack. In this paper we considered these attacks and presented a solution to identify the attacks. Many approaches have been proposed to diminish these attacks, but very few methods can detect these attacks effectively. In this simple scheme, every node selects a parent node to forward the packet towards base station or sink. Each node append its unique identity and trust to the parent as a path marker. It encrypts the bytes using a secret key generated and shared among the sink. The encrypted packet is then forwarded to the parent node. Base station can identify the malicious nodes by using these unique identity and trust value