Güneş Karabulut Kurt

Selen Gecgel Cetin, Gunes Karabulut Kurt, Angeles Vazquez-Castro

There is no doubt that the Moon has become the center of interest for commercial and international actors. Over the past decade, the number of planned long-term missions has increased dramatically. This makes the establishment of cislunar space networks (CSNs) crucial to orchestrate uninterrupted communications between the Moon and Earth. However, there are numerous challenges, unknowns, and uncertainties associated with cislunar communications that may pose various risks to lunar missions. In this study, we aim to address these challenges for cislunar communications by proposing a machine learning-based cislunar space domain awareness (SDA) capability that enables robust and secure communications. To this end, we first propose a detailed channel model for selected cislunar scenarios. Secondly, we propose two types of interference that could model anomalies that occur in cislunar space and are so far known only to a limited extent. Finally, we discuss our cislunar SDA to work in conjunction with the spacecraft communication system. Our proposed cislunar SDA, involving heuristic learning capabilities with machine learning algorithms, detects interference models with over 96% accuracy. The results demonstrate the promising performance of our cislunar SDA approach for secure and robust cislunar communication.

Anouar Boumeftah, Sarah McKenzie-Picot, Peter Klimas et al.

Modern satellite systems face increasing operational risks from jamming, cyberattacks, and electromagnetic disruptions in contested space environments. Traditional redundancy strategies often fall short against such dynamic and multi-vector threats. This paper introduces a resilience by design framework grounded in the PACE (Primary, Alternate, Contingency, Emergency) methodology, originally developed for tactical communications in military operations, and adapts it to satellite systems through a layered state transition model informed by threat scoring frameworks such as CVSS, DREAD, and NASA's risk matrix. We define a dynamic resilience index to quantify system adaptability and implement three PACE variants (static, adaptive, and epsilon-greedy reward optimized) to evaluate resilience under diverse disruption scenarios. Results show that lightweight, decision aware fallback mechanisms can substantially improve survivability and operational continuity for next generation space assets.

Rüdiger Machhamer, Lejla Begic Fazlic, Eray Guven et al.

An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation between different systems, which can be improved by the knowledge of experts. This paper presents an improved solution from Glacier Research that was published back in 1973. The results demonstrate the adaptability of this solution for various applications, including software calibration of sensors, implementation of expert-based adaptation, and paving the way for future advancements such as distributed learning methods. One idea here is to use the knowledge of experts for estimating an affine transformation between different systems. We evaluate our research with simulations and also with real measured data of a multi-sensor board with 8 identical sensors. Both data set and evaluation script are provided for download. The results show an improvement for both the simulation and the experiments with real data.

Kürşat Tekbıyık, Güneş Karabulut Kurt, Antoine Lesage-Landry

The increasing demand for data usage in wireless communications requires using wider bands in the spectrum, especially for backhaul links. Yet, allocations in the spectrum for non-communication systems inhibit merging bands to achieve wider bandwidth. To overcome this issue, spectrum-sharing or opportunistic spectrum utilization by secondary users stands out as a promising solution. However, both approaches must minimize interference to primary users. Therefore, spectrum sensing becomes vital for such opportunistic usage, ensuring the proper operation of the primary users. Although this problem has been investigated for 2D networks, unmanned aerial vehicle (UAV) networks need different points of view concerning 3D space, its challenges, and opportunities. For this purpose, we propose a federated learning (FL)-based method for spectrum sensing in UAV networks to account for their distributed nature and limited computational capacity. FL enables local training without sharing raw data while guaranteeing the privacy of local users,lowering communication overhead, and increasing data diversity. Furthermore, we develop a federated aggregation method, namely FedSNR, that considers the signal-to-noise ratio observed by UAVs to acquire a global model. The numerical results show that the proposed architecture and the aggregation method outperform traditional methods.

Kürşat Tekbıyık, Güneş Karabulut Kurt, Antoine Lesage-Landry

Unmanned aerial vehicle (UAV) communications demand accurate yet interpretable air-to-ground (A2G) channel models that can adapt to nonstationary propagation environments. While deterministic models offer interpretability and deep learning (DL) models provide accuracy, both approaches suffer from either rigidity or a lack of explainability. To bridge this gap, we propose the Physics-Inspired Kolmogorov-Arnold Network (PIKAN) that embeds physical principles (e.g., free-space path loss, two-ray reflections) into the learning process. Unlike physics-informed neural networks (PINNs), PIKAN is more flexible for applying physical information because it introduces them as flexible inductive biases. Thus, it enables a more flexible training process. Experiments on UAV A2G measurement data show that PIKAN achieves comparable accuracy to DL models while providing symbolic and explainable expressions aligned with propagation laws. Remarkably, PIKAN achieves this performance with only 232 parameters, making it up to 37 times lighter than multilayer perceptron (MLP) baselines with thousands of parameters, without sacrificing correlation with measurements and also providing symbolic expressions. These results highlight PIKAN as an efficient, interpretable, and scalable solution for UAV channel modelling in beyond-5G and 6G networks.

Selen Gecgel Cetin, Tolga Ovatman, Gunes Karabulut Kurt

This letter addresses essential aspects of threat assessment by proposing intent-driven threat models that incorporate both capabilities and intents. We propose a holistic framework for cyber physical awareness (CPA) in space networks, pointing out that analyzing reliability and security separately can lead to overfitting on system-specific criteria. We structure our proposed framework in three main steps. First, we suggest an algorithm that extracts characteristic properties of the received signal to facilitate an intuitive understanding of potential threats. Second, we develop a multitask learning architecture where one task evaluates reliability-related capabilities while the other deciphers the underlying intentions of the signal. Finally, we propose an adaptable threat assessment that aligns with varying security and reliability requirements. The proposed framework enhances the robustness of threat detection and assessment, outperforming conventional sequential methods, and enables space networks with emerging intershell links to effectively address complex threat scenarios.

Kürşat Tekbıyık, Amir Hossein Fahim Raouf, İsmail Güvenç et al.

Low Altitude Economy (LAE) networks own transformative potential in urban mobility, emergency response, and aerial logistics. However, these networks face significant challenges in spectrum management, interference mitigation, and real-time coordination across dynamic and resource-constrained environments. After addressing these challenges, this study explores three core elements for enabling intelligent LAE networks as follows machine learning-based spectrum sensing and coexistence, artificial intelligence (AI)-optimized resource allocation and trajectory planning, and testbed-driven validation and standardization. We highlight how federated and reinforcement learning techniques support decentralized, adaptive decision-making under mobility and energy constraints. In addition, we discuss the role of real-world platforms such as AERPAW in bridging the gap between simulation and deployment and enabling iterative system refinement under realistic conditions. This study aims to provide a forward-looking roadmap toward developing efficient and interoperable AI-driven LAE ecosystems.

Ozan Alp Topal, Gunes Karabulut Kurt

Physical layer authentication (PLA) is the process of claiming identity of a node based on its physical layer characteristics such as channel fading or hardware imperfections. In this work, we propose a novel PLA method for the inter-satellite communication links (ISLs) of the LEO satellites. In the proposed PLA method, multiple receiving satellites validate the identity of the transmitter by comparing the Doppler frequency measurements with the reference mobility information of the legitimate transmitter and then fuse their decision considering the selected decision rule. Analytical expressions are obtained for the spoofing detection probability and false alarm probability of the fusion methods. Numerically obtained high authentication performance results pave the way to a novel and easily integrable authentication mechanism for the LEO satellite networks.

Yucel Aydin, Gunes K. Kurt, Enver Ozdemir et al.

Drones are begin used for various purposes such as border security, surveillance, cargo delivery, visual shows and it is not possible to overcome such intensive tasks with a single drone. In order to expedite performing such tasks, drone swarms are employed. The number of drones in a swarm can be high depending on the assigned duty. The current solution to authenticate a single drone using a 5G new radio (NR) network requires the execution of two steps. The first step covers the authentication between a drone and the 5G core network, and the second step is the authentication between the drone and the drone control station. It is not feasible to authenticate each drone in a swarm with the current solution without causing a significant latency. Authentication keys between a base station (BS) and a user equipment (UE) must be shared with the new BS while performing handover. The drone swarms are heavily mobile and require several handovers from BS to a new BS. Sharing authentication keys for each drone as explained in 5G NR is not scalable for the drone swarms. Also, the drones can be used as a UE or a radio access node on board unmanned aerial vehicle (UxNB). A UxNB may provide service to a drone swarm in a rural area or emergency. The number of handovers may increase and the process of sharing authentication keys between UxNB to new UxNB may be vulnerable to eavesdropping due to the wireless connectivity. In this work, we present a method where the time and the number of the communication for the authentication of a new drone joining the swarm are less than 5G NR. In addition, group-based handover solutions for the scenarios in which the base stations are terrestrial or mobile are proposed to overcome the scalability and latency issues in the 5G NR.

Sueda Guzey, Gunes Karabulut Kurt, Enver Ozdemir

Group authentication is a method of confirmation that a set of users belong to a group and of distributing a common key among them. Unlike the standard authentication schemes where one central authority authenticates users one by one, group authentication can handle the authentication process at once for all members of the group. The recently presented group authentication algorithms mainly exploit Lagrange's polynomial interpolation along with elliptic curve groups over finite fields. As a fresh approach, this work suggests use of linear spaces for group authentication and key establishment for a group of any size. The approach with linear spaces introduces a reduced computation and communication load to establish a common shared key among the group members. The advantages of using vector spaces make the proposed method applicable to energy and resource constrained devices. In addition to providing lightweight authentication and key agreement, this proposal allows any user in a group to make a non-member to be a member, which is expected to be useful for autonomous systems in the future. The scheme is designed in a way that the sponsors of such members can easily be recognized by anyone in the group. Unlike the other group authentication schemes based on Lagrange's polynomial interpolation, the proposed scheme doesn't provide a tool for adversaries to compromise the whole group secrets by using only a few members' shares as well as it allows to recognize a non-member easily, which prevents service interruption attacks.

Kürşat Tekbıyık, Okan Yurduseven, Güneş Karabulut Kurt

In this paper, we present a single-pixel compressive direction of arrival (DoA) estimation technique leveraging a graph attention network (GAT)-based deep-learning framework. The physical layer compression is achieved using a coded-aperture technique, probing the spectrum of far-field sources that are incident on the aperture using a set of spatio-temporally incoherent modes. This information is then encoded and compressed into the channel of the coded-aperture. The coded-aperture is based on a metasurface antenna design and it works as a receiver, exhibiting a single-channel and replacing the conventional multichannel raster scan-based solutions for DoA estimation. The GAT network enables the compressive DoA estimation framework to learn the DoA information directly from the measurements acquired using the coded-aperture. This step eliminates the need for an additional reconstruction step and significantly simplifies the processing layer to achieve DoA estimation. We show that the presented GAT integrated single-pixel radar framework can retrieve high fidelity DoA information even under relatively low signal-to-noise ratio (SNR) levels.

Selen Gecgel, Gunes Karabulut Kurt

Towards sixth-generation networks (6G), satellite communication systems, especially based on Low Earth Orbit (LEO) networks, become promising due to their unique and comprehensive capabilities. These advantages are accompanied by a variety of challenges such as security vulnerabilities, management of hybrid systems, and high mobility. In this paper, firstly, a security deficiency in the physical layer is addressed with a conceptual framework, considering the cyber-physical nature of the satellite systems, highlighting the potential attacks. Secondly, a learning-driven detection scheme is proposed, and the lightweight convolutional neural network (CNN) is designed. The performance of the designed CNN architecture is compared with a prevalent machine learning algorithm, support vector machine (SVM). The results show that deficiency attacks against the satellite systems can be detected by employing the proposed scheme.

Caner Goztepe, Saliha Buyukcorak, Gunes Karabulut Kurt et al.

The impact of localization systems in our daily lives is increasing. As next-generation networks will introduce hyperconnectivity with the emerging applications, this impact will undoubtedly further increase, proliferating the importance of the location information's reliability. As society becomes more dependent on this information in terms of the products and services, security solutions will have to be enriched to provide countermeasures sufficiently advanced to ever-evolving threats, forcing the joint design of communication and localization systems. This paper envisions integrated communication and localization systems by focusing on localization security. Also, conventional and next-generation attacks on localization are discussed along with an efficient attack detection method and test-bed-based demonstration, highlighting the need for effective countermeasures.

Ozan Alp Topal, Gunes Karabulut Kurt, Halim Yanikomeroglu

In this work, we propose a secret key generation procedure specifically designed for the inter-spacecraft communication links. As a novel secrecy source, the spacecrafts utilize Doppler frequency shift based measurements. In this way, the mobilities of the communication devices are exploited to generate secret keys, where this resource can be utilized in the environments that the channel fading based key generation methods are not available. The mobility of a spacecraft is modeled as the superposition of a pre-determined component and a dynamic component. We derive the maximum achievable secret key generation rate from the Doppler frequency shift. The proposed secret key generation procedure extracts the Doppler frequency shift in the form of nominal power spectral density samples (NPSDS). We propose a maximum-likelihood (ML) estimation for the NPSDS at the spacecrafts, then a uniform quantizer is utilized to obtain secret key bits. The key disagreement rate (KDR) is analytically obtained for the proposed key generation procedure. Through numerical studies, the tightness of the provided approximations is shown. Both the theoretical and numerical results demonstrate the validity and the practicality of the presented physical layer key generation procedure considering the security of the communication links of spacecrafts.

Kürşat Tekbıyık, Güneş Karabulut Kurt, Ali Rıza Ekti et al.

Direct-to-satellite (DtS) communication has gained importance recently to support globally connected Internet of things (IoT) networks. However, relatively long distances of densely deployed satellite networks around the Earth cause a high path loss. In addition, since high complexity operations such as beamforming, tracking and equalization have to be performed in IoT devices partially, both the hardware complexity and the need for high-capacity batteries of IoT devices increase. The reconfigurable intelligent surfaces (RISs) have the potential to increase the energy-efficiency and to perform complex signal processing over the transmission environment instead of IoT devices. But, RISs need the information of the cascaded channel in order to change the phase of the incident signal. This study evaluates the pilot signal as a graph and incorporates this information into the graph attention networks (GATs) to track the phase relation through pilot signaling. The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods. Moreover, bit error rate performance is investigated for RIS designs with discrete and non-uniform phase shifts under channel estimation based on the proposed method. One of the findings in this study is that the channel models of the operating environment and the performance of the channel estimation method must be considered during RIS design to exploit performance improvement as far as possible.

Selen Gecgel, Caner Goztepe, Gunes Karabulut Kurt et al.

Communications are realized as a result of successive decisions at the physical layer, from modulation selection to multi-antenna strategy, and each decision affects the performance of the communication systems. Future communication systems must include extensive capabilities as they will encompass a wide variety of devices and applications. Conventional physical layer decision mechanisms may not meet these requirements, as they are often based on impractical and oversimplifying assumptions that result in a trade-off between complexity and efficiency. By leveraging past experiences, learning-driven designs are promising solutions to present a resilient decision mechanism and enable rapid response even under exceptional circumstances. The corresponding design solutions should evolve following the lines of learning-driven paradigms that offer more autonomy and robustness. This evolution must take place by considering the facts of real-world systems and without restraining assumptions. In this paper, the common assumptions in the physical layer are presented to highlight their discrepancies with practical systems. As a solution, learning algorithms are examined by considering the implementation steps and challenges. Furthermore, these issues are discussed through a real-time case study using software-defined radio nodes to demonstrate the potential performance improvement. A cyber-physical framework is presented to incorporate future remedies.

Yucel Aydin, Gunes Karabulut Kurt, Enver Ozdemir et al.

The widespread use of new technologies such as the Internet of things (IoT) and machine type communication(MTC) forces an increase on the number of user equipments(UEs) and MTC devices that are connecting to mobile networks. Inherently, as the number of UEs inside a base station's (BS) coverage area surges, the quality of service (QoS) tends to decline. The use of drone-mounted BS (UxNB) is a solution in places where UEs are densely populated, such as stadiums. UxNB emerges as a promising technology that can be used for capacity injection purposes in the future due to its fast deployment. However, this emerging technology introduces a new security issue. Mutual authentication, creating a communication channel between terrestrial BS and UxNB, and fast handover operations may cause security issues in the use of UxNB for capacity injection. This new protocol also suggests performing UE handover from terrestrial to UxNB as a group. To the best of the authors' knowledge, there is no authentication solution between BSs according to LTE and 5G standards. The proposed scheme provides a solution for the authentication of UxNB by the terrestrial BS. Additionally, a credential sharing phase for each UE in handover is not required in the proposed method. The absence of a credential sharing step saves resources by reducing the number of communications between BSs. Moreover, many UE handover operations are completed in concise time within the proposed group handover method.

Kürşat Tekbıyık, Güneş Karabulut Kurt, Ali Rıza Ekti et al.

Next-generation communication technology will be made possible by cooperation between terrestrial networks with non-terrestrial networks (NTN) comprised of high-altitude platform stations and satellites. Further, as humanity embarks on the long road to establish new habitats on other planets, cooperation between NTN and deep-space networks (DSN) will be necessary. In this regard, we propose the use of reconfigurable intelligent surfaces (RIS) to improve coordination between these networks given that RIS perfectly match the size, weight, and power restrictions of operating in space. A comprehensive framework of RIS-assisted non-terrestrial and interplanetary communications is presented that pinpoints challenges, use cases, and open issues. Furthermore, the performance of RIS-assisted NTN under environmental effects such as solar scintillation and satellite drag is discussed in light of simulation results.

Kürşat Tekbıyık, Güneş Karabulut Kurt, Chongwen Huang et al.

In this paper, graph attention network (GAT) is firstly utilized for the channel estimation. In accordance with the 6G expectations, we consider a high-altitude platform station (HAPS) mounted reconfigurable intelligent surface-assisted two-way communications and obtain a low overhead and a high normalized mean square error performance. The performance of the proposed method is investigated on the two-way backhauling link over the RIS-integrated HAPS. The simulation results denote that the GAT estimator overperforms the least square in full-duplex channel estimation. Contrary to the previously introduced methods, GAT at one of the nodes can separately estimate the cascaded channel coefficients. Thus, there is no need to use time-division duplex mode during pilot signaling in full-duplex communication. Moreover, it is shown that the GAT estimator is robust to hardware imperfections and changes in small-scale fading characteristics even if the training data do not include all these variations.

Gunes Karabulut Kurt, Enver Ozdemir, Neslihan Aysen Ozkirisci et al.

In this work, we propose a novel key reconciliation protocol for the quantum key distribution (QKD). Based on Newton's polynomial interpolation, the proposed protocol aims to correct all erroneous bits at the receiver without revealing information to the eavesdropper. We provide the exact frame error rate (FER) expression of the proposed protocol. The inherent nature of the proposed algorithm ensures correcting all erroneous bits if the algorithm succeeds. We present an information-theoretical proof that the revealed information during the key reconciliation process is equal to zero. We also provide a numerical comparison of our algorithm with the asymptotic performance of the error-correcting codes and two exemplary low-density-parity-check (LDPC) codes. The results highlight that our algorithm provides superior performance when compared to the LDPC codes, regardless of the distance between Alice and Bob. Furthermore, the proposed key reconciliation protocol is usable for the longer quantum link distances than the state-of-the-art protocols.

Kürşat Tekbıyık, Özkan Akbunar, Ali Rıza Ekti et al.

Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and signal identification. The proposed method classifies wireless signals without a priori information and it is implemented in two different settings entitled CASE1 and CASE2. In CASE1, signals are jointly sensed and classified. In CASE2, sensing and classification are conducted in a sequential manner. In contrary to the classical spectrum sensing techniques, the proposed CNN method does not require a statistical decision process and does not need to know the distinct features of signals beforehand. Implementation of the method on the measured overthe-air real-world signals in cellular bands indicates important performance gains when compared to the signal classifying deep learning networks available in the literature and against classical sensing methods. Even though the implementation herein is over cellular signals, the proposed approach can be extended to the detection and classification of any signal that exhibits cyclostationary features. Finally, the measurement-based dataset which is utilized to validate the method is shared for the purposes of reproduction of the results and further research and development.

Ufuk Altun, Semiha T. Basaran, Gunes K. Kurt et al.

In this paper, we consider the problem of secret key generation for multiple parties. Multi-user networks usually require a trusted party to efficiently distribute keys to the legitimate users and this process is a weakness against eavesdroppers. With the help of the physical layer security techniques, users can securely decide on a secret key without a trusted party by exploiting the unique properties of the channel. In this context, we develop a physical layer group key generation scheme that is also based on the ideas of the analog function computation studies. We firstly consider the key generation as a function to be computed over the wireless channel and propose two novel methods depending on the users transmission capability (i.e. half-duplex and full-duplex transmissions). Secondly, we exploit the uniqueness of the prime integers in order to enable the simultaneous transmission of the users for key generation. As a result, our approach contributes to the scalability of the existing physical layer key generation algorithms since all users transmit simultaneously rather than using pairwise communications. We prove that our half-duplex network model reduces the required number of communications for group key generation down to a linear scale. Furthermore, the full-duplex network model reduces to a constant scale.

Kürşat Tekbıyık, Ali Rıza Ekti, Ali Görçin et al.

Automatic modulation classification (AMC) has been studied for more than a quarter of a century; however, it has been difficult to design a classifier that operates successfully under changing multipath fading conditions and other impairments. Recently, deep learning (DL)-based methods are adopted by AMC systems and major improvements are reported. In this paper, a novel convolutional neural network (CNN) classifier model is proposed to classify modulation classes in terms of their families, i.e., types. The proposed classifier is robust against realistic wireless channel impairments and in relation to that when the data sets that are utilized for testing and evaluating the proposed methods are considered, it is seen that RadioML2016.10a is the main dataset utilized for testing and evaluation of the proposed methods. However, the channel effects incorporated in this dataset and some others may lack the appropriate modeling of the real-world conditions since it only considers two distributions for channel models for a single tap configuration. Therefore, in this paper, a more comprehensive dataset, named as HisarMod2019.1, is also introduced, considering real-life applicability. HisarMod2019.1 includes 26 modulation classes passing through the channels with 5 different fading types and several numbers of taps for classification. It is shown that the proposed model performs better than the existing models in terms of both accuracy and training time under more realistic conditions. Even more, surpassed their performance when the RadioML2016.10a dataset is utilized.

Yucel Aydin, Gunes Karabulut Kurt, Enver Özdemir et al.

Internet of Things (IoT) networks are becoming a part of our daily lives, as the number of IoT devices around us are surging. The authentication of millions of connected things and the distribution and management of secret keys between these devices pose challenging research problems. Current one-to-one authentication schemes do not take the resource limitations of IoT devices into consideration. Nor do they address the scalability problem of massive machine type communication (mMTC) networks. Group authentication schemes (GAS), on the other hand, have emerged as novel approaches for many-to-many authentication problems. They can be used to simultaneously authenticate numerous resource-constrained devices. However, existing GAS are not energy efficient, and they do not provide enough security for widespread use. In this paper, we propose a lightweight GAS that significantly reduces energy consumption on devices, providing almost 80% energy savings when compared to the state-of-the-art solutions. Our approach is also resistant to the replay and man-in-the-middle attacks. The proposed approach also includes a solution for key agreement and key distribution problems in mMTC environments. Moreover, this approach can be used in both centralized and decentralized group authentication scenarios. The proposed approach has the potential to address the fast authentication requirements of the envisioned agile 6G networks, supported through aerial networking nodes.

Yucel Aydin, Gunes Karabulut Kurt, Enver Ozdemır

Current advancements in mobility of devices and also Internet of Things (IoT) have replaced the central networks by distributed infrastructure. The more a network is distributed, the more the security of infrastructure and the communication is getting complex. The members in a distributed network create different groups according to their coverage area or their requirements. Mobility nature of the members brings a problem called hand-over of members between groups. Current authentication methods are not applicable due to the lack of resources in the devices.A lightweight authentication method and an easy and fast hand-over process are the current need for the distributed networks. Shamir Secret Sharing algorithm is used for the authentication process in the studies before, but still secure group authentication algorithm and hand-over process are challenges in the group authentication. In this study, a new method is proposed to provide a secure group authentication and hand-over process between groups based on Lagrange's Interpolation.

Yucel Aydin, Gunes Karabulut Kurt, Enver Ozdemır

Shamir or Blakley secret sharing schemes are used for the authentication process in the studies before, but still secure group authentication and hand-over process remain as challenges in group authentication approaches. In this study, a novel method is proposed to provide a secure group authentication. The proposed approach also enables a hand-over process between groups by using Lagrange's polynomial interpolation and Weil pairing in elliptic curve groups for wireless networks with mobility support. One of the advantages of our proposed scheme is that the computational load for a member in the group is lower than the other schemes in the state-of-the-art. It is also possible to authorize many users at the same time, not one-to-one as in the group authentication methods in current cellular networks including Long Term Evolution (LTE). Another advantage that is not covered in other secret sharing methods is that the proposed approach constitutes a practical solution for the hand-over of members between different groups. We have also proposed a solution for replay and man-in-the-middle attacks in secret exchange.