Malicious Anchors Research Articles

Malicious anchor node extraction using geodesic search for survivable underwater wireless sensor network

Localization in underwater wireless sensor network (UWSN) faces an imminent threat when the triangulating anchor node starts to malfunction. Traditional geometric approaches are insufficient to cope with the survivability of UWSN topology. To address these issues, this paper presents a symplectic geometry for identification of the malicious anchor node. Consequently, a geodesic search algorithm (GSA) based Target localization is proposed which reduces the positioning error by exploiting the phase-space constancy of the underwater acoustic sensor network topology to effectively triangulate the target node despite its mobility. First, a malicious anchor node model is presented. The node movement is expressed in the form of “ripple region”. GSA is then proposed which effectively frees the node metastasis from anchor node geometry, thereby making the underwater system more survivable and resilient. Simulation results evaluate the survivability of the geodesic formalism in terms of the reduced penalty incurred by node movement, as well as the reduced impact of anchor node malfunction. An improvement of 13.46% and 9.26% reveals the utility of the geodesic technique in aquamarine sensor deployments, which would be beneficial in underwater resource exploration and defense planning.

Robustness Enhanced Sensor Assisted Monte Carlo Localization for Wireless Sensor Networks and the Internet of Things

With distributed sensor systems commonly found in Wireless Sensor Networks or the Internet of Things, knowing the location sensor data was acquired from is very important, especially in scenarios with mobile sensors. Range-free Monte Carlo Localization based approaches are very energy efficient and do not require additional hardware beyond a radio, which is found on sensor nodes anyways. However, the use of motion sensor data based dead reckoning greatly improves the accuracy of location estimates and increases robustness against faulty or malicious actors within the network. In this work, we propose Robustness Enhanced Sensor Assisted Monte Carlo Localization (RESA-MCL). We show RESA-MCL’s effectiveness with respect to both general localization accuracy and robustness against malicious attacks or malfunctioning nodes. To evaluate and compare our scheme against existing approaches, we introduce three attack models based on malicious anchor nodes. The performance of RESA-MCL is evaluated under these attack models and our approach outperforms existing schemes in both very low and higher anchor node density environments, achieving a localization error of 0.5 with an anchor density of 0.33. Overall, RESA-MCL outperforms comparable approaches at lower anchor densities with up to 48% lower localization error and demonstrates strongly increased robustness against attacks with minimal computational overhead.

To Hide Private Position Information in Localization for Internet of Underwater Things

Privacy-preserving localization for Internet of Underwater Things (IoUT) plays a fundamental role in the sensing, communication and control of ocean environments. However, the unique characteristics of underwater environment make it much more difficult to achieve such a task. In this article, we are concerned with a privacy-preserving localization issue for IoUT, subjected to asynchronous clock, stratification effect and forging attack in cyber channels. In order to eliminate the influence of asynchronous clock and hide the private position information, we develop a privacy-preserving asynchronous transmission protocol, where a received signal strength (RSS)-based detection strategy is given to detect the malicious anchor nodes. Based on this, a least squares estimator is designed to estimate the position information of target. Particularly, a ray compensation strategy is incorporated into the localization estimator, such that the localization bias from assuming the straight-line transmission can be avoided. It is worth mentioning that, the proposed localization solution in this article can not only hide the private position information, but also eliminate the influences of asynchronous clock, stratification effect and forging attack. Finally, simulation and experiment results are conducted to reveal that the proposed localization solution outperforms the other existing works in terms of localization accuracy and effectiveness.

Evaluation of Node-Metastasis in Sparse Underwater Acoustic Sensor Networks for Localization Under Acoustically Stratified Malicious Node Conditions

Malicious anchor node is a serious issue in underwater sensor networks. This problem gets compounded under sparse deployment scenario. To overcome this issue, the authors present a compressed sensing theorem (abbreviated as CS-MC Theorem) which would be applicable to underwater localization. To counter malicious node behavior, CS-MC Theorem is justified through three lemmas. For underwater acoustic propagation, a specific L1-norm minimization method is derived. It describes the condition for unique localization of target by discretizing the target search space. Malicious node is identified by inserting the anchor nodes into the discretized grid. An insertion vector concept is introduced to determine the extent of node metastasis. Derivations are performed to prove the mathematical basis of the proposed algorithm under acoustically stratified and unstratified scenarios. Numerical results establish the feasibility of CS-MC algorithm for sparse underwater acoustic sensor network.

RSS-Based Localization in the Presence of Malicious Nodes in Sensor Networks

The ability of a sensor node to determine its location in a sensor network is important in many applications. We address the scenario, where malicious anchor node(s) attempt to disrupt, in an uncoordinated or coordinated manner, the localization process of a target node. We propose four localization techniques: weighted least square (WLS), secure WLS (SWLS), and $\ell _{1}$ -norm-based techniques LN-1 and LN-1E. WLS and SWLS techniques are shown to offer a significant advantage in uncoordinated attacks over existing techniques by detecting the malicious nodes and eliminating their measurements from the localization process and assigning larger weights to the measurements corresponding to anchor nodes that are closer to the target node. In a coordinated attack, the localization problem can be posed as a plane fitting problem, where the measurements from nonmalicious and malicious anchor nodes lie on two different planes. The LN-1E technique estimates the two planes and prevents disruption of the localization process. LN-1E can also determine the location where the malicious anchor nodes intend to make the target node appear to be located. The Cramer–Rao lower bound for the position estimate is also derived. We present simulation and experimental results, which demonstrate that the proposed techniques provide better localization accuracy than existing algorithms.

A type of energy-efficient secure localization algorithm FM based in dynamic sensor networks

Since dynamic wireless sensor networks are widely used in the military field, the location information is the basis of various applications, because each node in the dynamic sensor network is moving continuously, and it is quite possible to be attacked by various forms of information. Aiming at the problem of secure localization in dynamic sensor networks, a new secure localization algorithm—frequency modulation secure localization (FMSL)—for dynamic sensor networks is proposed with the support of FM signal and Monte Carlo method. The algorithm uses FM signals which are widely covered to locate randomly distributed nodes loaded with FM signal receiving module, filters some malicious attack anchors in the network, and uses the improved Monte Carlo algorithm to locate the nodes, so as to improve the positioning accuracy. Meanwhile, according to the moving path and the localization time, energy consumption of the network could be estimated and also give the sleep scheduling strategy for the node to be localized. The simulation results show that the FMSL algorithm can significantly improve the security performance and positioning accuracy of mobile sensor networks compared with the existing security positioning algorithms such as Monte Carlo and convex programming. In the process of motion, the positioning accuracy can reach less than 10%.

Robust and Cooperative Localization for Underwater Sensor Networks in the Existence of Malicious Anchors.

Precise and robust localization in three-dimensional underwater sensor networks is still an important research problem. This problem is particularly challenging if there are some malicious anchors among ordinary anchor nodes that will broadcast their locations falsely and deliberately. In this paper, we study how to self-localize large teams of underwater sensor nodes under the condition that some malicious anchor nodes mixed with ordinary anchors. Due to malicious characteristic of some deliberate anchor nodes, an iterative and cooperative 3D-localization algorithm for underwater sensor networks in the existence of malicious anchors is proposed in this paper. The proposed robust localization algorithm takes advantage of distributed reputation voting method within 1-Hop neighboring reference nodes to detect and eliminate malicious anchor nodes. Moreover, one kind of Minimum Mean Squared Error estimation based iterative localization method is applied to determine accurate location information. Additionally, we analyze and prove that our localization algorithm would have a bounded error when the number of malicious anchors is smaller than a certain threshold. Extensive simulation results are provided to demonstrate performance improvements comparing to traditional Minimum Mean Squared Error and Attack Resistant Minimum Mean Squared Error based localization methods in terms of localization accuracy and coverage ratio.

A Range-Based Secure Localization Algorithm for Wireless Sensor Networks

The accurate localization of nodes in wireless sensor networks can be jeopardized in certain applications of fields, such as those in security and military. In order to detect the nodes that are captured as malicious anchors, a malicious node detection algorithm named MNDC and its improved version named EMDC are proposed in this paper. The algorithms utilize the density-based spatial clustering to acquire the abnormal clusters, which are further examined via a sequential probability ratio test. The malicious nodes that jeopardize networks are then determined. The conducted simulation results and corresponding analysis demonstrate that the proposed algorithms outperform other state-of-art schemes in terms of detection accuracy and effectiveness.

Sparsity‐incorporated secure localisation for wireless sensor networks

To enable efficient and accurate secure localisation in wireless sensor networks, a sparsity-incorporated localisation algorithm is proposed. Unlike most of the existing schemes, the localisation problem is solved in an L1 minimisation framework which is based on the attack sparsity and the linearisation of a distance measurement model including the bogus data from malicious anchors. The localisation errors, speed and detection rates of the scheme are analysed in the simulations. Results demonstrate that the method can simultaneously achieve target localisation and malicious anchor identification in an efficient and accurate way.

Sparse recovery formulation for secure distance-based localization in the presence of cheating anchors

Secure localization in the presence of cheating anchors is a critical issue in wireless sensor networks, where compromised anchors attempt to falsify the measured distances in the location references. In this study, such misbehaviors of unfaithful anchors are modeled as unknown perturbations to the measured distances. By exploiting the sparsity of malicious anchors, the secure localization problem is formulated as a sparse signal recovery problem whose objective is to concurrently locate the sensors and identify the malicious anchors. Under the above formulation, the upper bound for the number of tolerable malicious anchors is obtained and the localization error bound is also provided. A gradient projection algorithm with variable step size is proposed to solve the sparse recovery problem. To further improve the localization accuracy, another modified gradient projection algorithm is presented. Simulation results show that the proposed algorithms can identify the unfaithful anchors with high probability and achieve good localization accuracy.

A Secure Localization Approach Using Mutual Authentication and Insider Node Validation in Wireless Sensor Networks

Localization is a concerning issue in the applications of wireless sensor networks. Along with the accuracy of the location estimation of the sensor nodes, the security of the estimation is another priority. Wireless sensor networks often face various attacks where the attackers try to manipulate the estimated location or try to provide false beacons. In this paper, we have proposed a methodology that will address this problem of security aspects in localization of the sensor nodes. Moreover, we have considered the network environment with random node deployment and mobility as these two conditions are less addressed in previous research works. Further, our proposed algorithm provides low overhead due to the usage of less control messages in a limited transmission range. In addition, we have also proposed an algorithm to detect the malicious anchor nodes inside the network. The simulated results show that our proposed algorithm is efficient in terms of time consumption, localization accuracy, and localization ratio in the presence of malicious nodes.

Reexamining DNS From a Global Recursive Resolver Perspective

The performance and operational characteristics of the Domain Name System (DNS) protocol are of deep interest to the research and network operations community. In this paper, we present measurement results from a unique dataset containing more than 26 billion DNS query-response pairs collected from more than 600 globally distributed recursive DNS resolvers. We use this dataset to reaffirm findings in published work and notice some significant differences that could be attributed both to the evolving nature of DNS traffic and to our differing perspective. For example, we find that although characteristics of DNS traffic vary greatly across networks, the resolvers within an organization tend to exhibit similar behavior. We further find that more than 50% of DNS queries issued to root servers do not return successful answers, and that the primary cause of lookup failures at root servers is malformed queries with invalid top-level domains (TLDs). Furthermore, we propose a novel approach that detects malicious domain groups using temporal correlation in DNS queries. Our approach requires no comprehensive labeled training set, which can be difficult to build in practice. Instead, it uses a known malicious domain as anchor and identifies the set of previously unknown malicious domains that are related to the anchor domain. Experimental results illustrate the viability of this approach, i.e., we attain a true positive rate of more than 96%, and each malicious anchor domain results in a malware domain group with more than 53 previously unknown malicious domains on average.

Collaborative Localization and Location Verification in WSNs.

Localization is one of the most important technologies in wireless sensor networks. A lightweight distributed node localization scheme is proposed by considering the limited computational capacity of WSNs. The proposed scheme introduces the virtual force model to determine the location by incremental refinement. Aiming at solving the drifting problem and malicious anchor problem, a location verification algorithm based on the virtual force mode is presented. In addition, an anchor promotion algorithm using the localization reliability model is proposed to re-locate the drifted nodes. Extended simulation experiments indicate that the localization algorithm has relatively high precision and the location verification algorithm has relatively high accuracy. The communication overhead of these algorithms is relative low, and the whole set of reliable localization methods is practical as well as comprehensive.

An empirical reexamination of global DNS behavior

The performance and operational characteristics of the DNS protocol are of deep interest to the research and network operations community. In this paper, we present measurement results from a unique dataset containing more than 26 billion DNS query-response pairs collected from more than 600 globally distributed recursive DNS resolvers. We use this dataset to reaffirm findings in published work and notice some significant differences that could be attributed both to the evolving nature of DNS traffic and to our differing perspective. For example, we find that although characteristics of DNS traffic vary greatly across networks, the resolvers within an organization tend to exhibit similar behavior. We further find that more than 50% of DNS queries issued to root servers do not return successful answers, and that the primary cause of lookup failures at root servers is malformed queries with invalid TLDs. Furthermore, we propose a novel approach that detects malicious domain groups using temporal correlation in DNS queries. Our approach requires no comprehensive labeled training set, which can be difficult to build in practice. Instead, it uses a known malicious domain as anchor, and identifies the set of previously unknown malicious domains that are related to the anchor domain. Experimental results illustrate the viability of this approach, i.e. , we attain a true positive rate of more than 96%, and each malicious anchor domain results in a malware domain group with more than 53 previously unknown malicious domains on average.

Secure and Robust Localization in a Wireless Ad Hoc Environment

We study the problem of accurate localization of static or mobile nodes in a wireless ad hoc network using the distance estimates of a group of untrusted anchors within the communication range of the nodes. Some of the anchors may be malicious and may independently lie about the distance estimate. The malicious anchors may also collude to lie about the distance estimates. In both cases, accurate node localization may be seriously undermined. We propose a scheme that performs accurate localization of the nodes in the network despite the presence of such malicious anchors. We also show how to identify most of these malicious anchors. In the case where measurements are error free, we derive a critical threshold <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> for the number of malicious anchors that can be tolerated in the localization process without undermining accuracy. We also show how to correctly localize a node and identify all the malicious anchors in this setting. In the presence of measurement errors, we propose a convex optimization-based localization scheme that can accurately localize a node, as long as the number of malicious anchors in its communication range is no more than <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> . Simulation results show that our schemes are very effective. When the measurements are error prone and the number of malicious anchors is no more than <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> , our scheme localizes a node with an error less than 8% and is also able to identify a significant number of malicious anchors. Our schemes guarantee that a true anchor is not identified as malicious.