The Bitcoin Lightning Network, as a second-layer solution for enhancing the scalability of Bitcoin transactions, facilitates transactions through payment channels between nodes. However, the rapid growth of the network and rising transaction volumes have exacerbated the challenge of managing payment channel imbalances. Payment channel imbalance, characterized by the concentration of liquidity in one direction, leads to a decrease in payment success rates, a reduction in the effective lifespan of payment channels, and a decline in the network’s overall efficiency and throughput. This study introduces a graph neural network-based recommendation strategy designed to enhance the Lightning Network’s autopilot system. The proposed approach proactively mitigates channel imbalances by optimizing channel recommendations, enabling dynamic and scalable liquidity management for network users. Simulations conducted using the CLoTH tool demonstrate a 45% increase in payment success rates, a 46% reduction in imbalanced channels, and a 14% increase in the lifespan of payment channels across the network compared to the existing autopilot recommendation strategies, and when compared with the commonly adopted circular rebalancing method, the proposed strategy achieves a 27% improvement in payment success rates. Additionally, we offer a comparative topological analysis between two snapshots of the LN, taken in November 2021 and August 2023, to facilitate unsupervised learning tasks. The results highlight an increase in network centralization alongside a decrease in the number of network size, emphasizing the growing need for decentralization strategies in the LN, such as the approach proposed in this study.

Applying artificial intelligence (AI) to data from Industrial Internet of Things (IIoT) devices is a novel direction in geological studies. However, privacy and security concerns hinder the sharing of data, thus affecting the performance of current AI-based approaches. In this article, we propose a novel data management style to address the privacy and security issues in joint hydrocarbon explorations. Federated learning can facilitate the analysis of multiple datasets without the need to share them, protecting private information of different companies in a virtual joint venture. We use the inference of petroleum reservoirs in karst stratigraphy as a case study. A federated learning-based enterprise data management framework is proposed to virtually integrate the information from different organizations. Our key contributions are summarized as follows. 1) A method for karst identification and inference is proposed, which uses neural networks to recognize the size of petroleum reservoirs in different karst areas. 2) A federated learning algorithm is applied to virtually aggregate data samples from different companies. 3) The performance of the new privacy-preserving integration model is compared with those of the individual/local deep learning models. Our results show that the proposed approach can substantially improve the accuracy of petroleum reservoir explorations.

Bitcoin uses blockchain technology to maintain transactions order and provides probabilistic guarantees to prevent double-spending, assuming that an attacker’s computational power does not exceed 50% of the network power. In this article, we design a novel bribery attack and show that this guarantee can be hugely undermined. Miners are assumed to be rational in this setup, and they are given incentives that are dynamically calculated. In this attack, the adversary misuses the Bitcoin protocol to bribe miners and maximize their gained advantage. We will reformulate the bribery attack to propose a general mathematical foundation upon which we build multiple strategies. We show that, unlike Whale Attack, these strategies are practical, especially in the future when halvings lower the mining rewards. In the so-called “guaranteed variable-rate bribing with commitment” strategy, through optimization by Differential Evolution (DE), we show how double-spending is possible in the Bitcoin ecosystem for any transaction whose value is above 218.9BTC, and this comes with 100% success rate. A slight reduction in the success probability, e.g., by 10%, brings the threshold down to 165BTC. If the rationality assumption holds, then this shows how vulnerable blockchain-based systems like Bitcoin are. We suggest a soft fork on Bitcoin to fix this issue at the end.

Vehicular Ad hoc Networks (VANET), as an inseparable part of Intelligent Transportation Systems (ITS), enable data communication between vehicles to promote road safety and traffic efficiency. But adversaries can also spread false information across these networks. Therefore, vehicles’ cognitive capabilities with respect to received data must be improved. This requires spatial intelligence and a mechanism to evaluate the trustworthiness of received data. But the dynamic nature of VANETs with intermittent connections, lack of infrastructure and real-time constraints make fulfilling this task very challenging. In this paper, we propose a trust-aware cognitive framework that exploits the redundancies in the exchanged DENM and CAM packets as well as spatial intelligence to filter malicious messages and prevent attackers from disrupting network operation. A novel supplementary mechanism is also presented that applies subjective logic on the pieces of information collected from all network vehicles to detect and isolate malicious entities. To assess the reliability of the proposed scheme, we made extensive comparisons between our model and two others. In the obtained results, our approach outperformed both of them and yielded an accuracy of over 90%, even when 50% of network participants were attackers. The supplementary malicious node detection mechanism of ours similarly yielded high accuracy and F1 scores in the simulations.

After Bitcoin’s emergence, blockchain found its way to many industries, including Fintech, energy, and manufacturing. Blockchains consensus algorithms, like Nakamoto’s, are mechanisms to probabilistically guarantee that a transaction is not undone after confirmation. This mechanism requires that no one’s computational power exceeds 50% of the network power. However, recent attacks on blockchains have raised serious questions about their security and whether they can be trusted to be employed in critical infrastructure and Industry 4.0. In this article, we introduce a new category of blockchain attacks which we call “misleading-attacks.” In this type of attack, a fraction of network power is misled so that the attacker reaches her goal. The technique is most effective when miners are rational and algorithm-oriented, similar to machines/agents in future Industry 4.0 or industrial Internet of Things. Moreover, this technique has the potential to be used in inventing new attacks, or can be used in combination with other known attacks. We first analyze a case in which the attacker uses misleading techniques to prevent her newly mined block from becoming orphaned. We show that the proposed technique can push the attack success probability up by 16.42%. In a case study, we demonstrate how the technique promotes the success rate from 29.02% to 45.44%. Initiating the attack will be profitable if the attacker’s power is more than 24% of the network power. By combining this novel technique with bribery attack, we show how the cost ofguaranteed variable-rate bribing with commitmentstrategy can be drastically reduced.

With the development of 5G technology and Internet of Things, all kinds of real life data are collected and recorded by a large number of sensors. It is of great significance to mine and analyze the hidden information in the data for applications like future prediction. However, due to interferences or instability of collection equipment, collected sensory data are often incomplete, and this incompleteness hinders the in-depth analysis of data in the cloud. Therefore, processing around missing values is significant. Relying on cloud machine learning methods is not enough to deal with the problem of missing data in the Artificial Intelligence of Things (AIoT) environment, however, edge computing provides a promising solution. In this article, gated recurrent units filling is employed at the edge nodes. A mobile edge node can not only find the historical information of the current missing data node but also acquire the data of the nodes adjacent to the missing data node. These ensure that the missing data are restored to the maximum extent at the source. The experimental results show that the missing value filling based on edge computing not only outperforms other filling methods in quality but also greatly reduces the energy consumption in AIoT.

The Internet of Things (IoT) growth is extremely fast and it now has found its way to healthcare applications too. Many smart health gadgets and devices are helping practitioners in collecting medical information and monitoring patients. In this distributed system, information or service is sometimes shared and used by other devices. Considering the importance of health-related information and the decisions made based on it, there should be some sort of assurance on the security and quality of the services or information provided. Trust management is an efficient means of promoting application security and reliability in these cases. However, due to some limitations that are specific to IoT, traditional trust evaluation algorithms cannot be employed or do not yield satisfactory results. In this paper, evidence theory is exploited to design a decentralized service-oriented trust management model for healthcare IoT. A measure of evidence distance is used to reward well-behaving healthcare service/information providers as well as referrers and punish malicious entities. In this context-aware model, trust is estimated based on direct experiences and indirect feedbacks of recommenders. The process runs in two contexts; trust to healthcare service and trust to recommendation. When personal direct experience does not exist, trust to a source or service is estimated by applying the combinatorial laws of evidence theory and integrating indirect trust values. The proposed model is secure against bad-mouthing, good-mouthing, and on-off attacks due to its dynamic parameters and using the concept of evidence distance. Our results confirm the robustness and efficiency of this scheme.

Edge computing, as a relatively recent evolution of cloud computing architecture, is the newest way for enterprises to distribute computational power and lower repetitive referrals to central authorities. In the edge computing environment, Generative Models (GMs) have been found to be valuable and useful in machine learning tasks such as data augmentation and data pre-processing. Federated learning and distributed learning refer to training machine learning models in the edge computing network. However, federated learning and distributed learning also bring additional risks to GMs since all peers in the network have access to the model under training. In this article, we study the vulnerabilities of federated GMs to data-poisoning-based backdoor attacks via gradient uploading. We additionally enhance the attack to reduce the required poisonous data samples and cope with dynamic network environments. Last but not least, the attacks are formally proven to be stealthy and effective toward federated GMs. According to the experiments, neural backdoors can be successfully embedded by including merely 5\% poisonous samples in the local training dataset of an attacker.

Industry 4.0 will make manufacturing processes smarter but this smartness requires more environmental awareness, which in case of Industrial Internet of Things, is realized by the help of sensors. This article is about industrial pharmaceutical systems and more specifically, water purification systems. Purified water which has certain conductivity is an important ingredient in many pharmaceutical products. Almost every pharmaceutical company has a water purifying unit as a part of its interdependent systems. Early detection of faults right at the edge can significantly decrease maintenance costs and improve safety and output quality, and as a result, lead to the production of better medicines. In this article, with the help of a few sensors and data mining approaches, an anomaly detection system is built for CHRIST Osmotron water purifier. This is a practical research with real-world data collected from SinaDarou Labs Co. Data collection was done by using six sensors over two-week intervals before and after system overhaul. This gave us normal and faulty operation samples. Given the data, we propose two anomaly detection approaches to build up our edge fault detection system. The first approach is based on supervised learning and data mining, e.g., by support vector machines. However, since we cannot collect all possible faults data, an anomaly detection approach is proposed based on normal system identification which models the system components by artificial neural networks. Extensive experiments are conducted with the data set generated in this study to show the accuracy of the data-driven and model-based anomaly detection methods.

Due to memory and processing limitations, Internet-of-Things (IoT) devices require external fog servers to perform some of their tasks. However, this offloading of tasks comes at the cost of more interactions whose security cannot be guaranteed without the authentication and key management scheme. Traditional prescriptions, such as those used for securing the Web, require referring to central agents, such as certificate authorities (CA) or online certificate status protocol (OCSP) responders, that sit in the cloud. This poses many challenges, including additional communication costs and repetitive delays which work against the low latency and energy efficiency goals of edge networking. In this article, we propose a novel semidecentralized public-key management scheme for smart IoT systems in which devices intelligently decide whether to look for the keying material locally at the edge or refer to the cloud for this purpose. The result is a security architecture that links IoT devices, fog servers, and cloud, but with minimal dependency on the latter. In the proposed solution, devices work collaboratively to deliver revocation lists and digital certificates of fog servers to each other. The decision to go for edge nodes or cloud CA/OCSP responders is made intelligently by each node upon learning its neighborhood and network statistics. The core idea is based on the Web of trust, but unlike that, whenever a material is not found locally, cloud servers are queried. Experiments show that through this intelligent approach, the cost of key management operations, e.g., delay, can be reduced by up to 50%.

6G will be an enabler for the massive Internet of Things (IoT) in which millions of devices communicate at high data rates and low latencies. One key area among 6G applications is advanced sensing. However, higher speed implies moving to higher frequencies, which generally require more transmission power. In remote sensing, this causes problems, since either we have to increase the number of sensors and lower their communications ranges or increase their ranges and accept faster battery depletion. To cut the cost, even localization modules are not usually included in sensors. However, in many applications, IoT sensors must know their locations. Recent advances in the field of drones have led to promising solutions for localization. In this article, we propose a novel approach called semidynamic mobile anchor guiding (SEDMAG) for drones which aims at energy-conservative trajectory planning and localization of massive IoT devices. In this approach, the drone tracks the shortest path over a connected graph. This path determines the visiting order of devices. But we show that the complexity of this approach is high, thus, a graph reduction approach is proposed. It reduces the complexity and decreases the drones' energy consumption and positioning delay. The drone then follows a weighted search algorithm (WSA) to dynamically visit the devices. Simulation results are used to verify the superiority of the proposed approach.

A principled approach to modeling sociocognitive networks is fundamental to understanding the network interrelations which in turn can be used in many applications such as human behavior analysis or team performance assessment. More specifically, in the opinion domain, learning the cognitive links and making a proper model for causal relationships between individuals is necessary for both analysis and control purposes. There are several mathematical models for opinion dynamics. However, few of them have been tested to be consistent with real-world data. In this article, a new hybrid model for opinion dynamics is proposed and is put to test with subjective experiments. It is imperative that a realistic model considers two cognitive facts: 1) a person tends to stick to his/her previously shaped opinion and 2) the opinion of a person is affected by others (either reinforced in a positive way or undermined negatively). This article presents a novel mathematical formulation of the proposed opinion dynamics model and proves its stability too. The new model is also extended to support multiple dimensions. In the multidimensional approach, opinions about two or more subjects are considered separately. The rationale behind this is to describe the evolution of agents’ opinions on several topics. To study how the model performs in reality, some real-world experiments are conducted and the influence matrix is learned in each case. In addition, a method is introduced to extract the parameters of the model from the experimental data. It is shown that the new model predictions, after it is trained, chase the real behaviors of participants very well and result in less error compared with the previous models.

Internet of Things is making objects smarter and more autonomous. At the other side, online education is gaining momentum and many universities are now offering online degrees. Content preparation for such programs usually involves recording the classes. In this article, we intend to introduce a deep learning-based camera management system as a substitute for the academic filming crew. The solution mainly consists of two cameras and a wearable gadget for the instructor. The fixed camera is used for the instructor's position and pose detection and the pan-tilt-zoom (PTZ) camera does the filming. In the proposed solution, image processing and deep learning techniques are merged together. Face recognition and skeleton detection algorithms are used to detect the position of instructor. But the main contribution lies in the application of deep learning for instructor's skeleton detection and postprocessing of the deep network output for correction of the pose detection results using a Bayesian Maximum A Posteriori (MAP) estimator. This estimator is defined on a Markov state machine. The pose detection result along with the position info is then used by the PTZ camera controller for filming purposes. The proposed solution is implemented by using OpenPose which is a convolutional neural network for detection of body parts. Feeding a neural network pose classifier with 12 features extracted from the output of the deep network yields an accuracy of 89%. However, as we show, the accuracy can be improved by the Markov model and MAP estimator to reach as high as 95.5%.

Opinion dynamics modeling has long been interesting to scientists because of its applications in the marketing industry as well as elections. Agreement, e.g., on a special product, has an affective influence on its manufacturing process. However, most of the existing opinion dynamics models are linear time invariant. In this article, eight non-linear time variant models for opinion dynamics are proposed and put to test on subjective synthetic networks. Despite all the efforts, the issues of stability and convergence in time-varying networks, which can be found in industrial contexts, have not been resolved yet and only sufficient conditions have been derived. In the proposed models, unlike in the classical ones such as the French-DeGroot model, we have taken into account each member's susceptibility to persuasion by others that is a cognitive parametric function of individuals' opinions. In addition, we have introduced a stubbornness matrix which has large values initially but shrinks to zero as the time grows. This implies that agents' susceptibilities to others, at the beginning of any experiment, are smaller than what they are at the end. We present a novel mathematical formulation for these new opinion dynamics models and prove their stability too. For evaluation, we simulated a scenario in which some people connected via a graph, voted for a product. They initially had their own stands but gradually came to a point found through interaction with the others. The results confirm the convergence and stability of the proposed models.

With the benefits of Internet of Vehicles (IoV) paradigm, come along unprecedented security challenges. Among many applications of inter-connected systems, vehicular networks and smart cars are examples that are already rolled out. Smart vehicles not only have networks connecting their internal components e.g. via Controller Area Network (CAN) bus, but also are connected to the outside world through road side units and other vehicles. In some cases, the internal and external network packets pass through the same hardware and are merely isolated by software defined rules. Any misconfiguration opens a window for the hackers to intrude into vehicles' internal components e.g. central lock system, Engine Control Unit (ECU), Anti-lock Braking System (ABS) or Adaptive Cruise Control (ACC) system. Compromise of any of these can lead to disastrous outcomes. In this paper, we study the security of smart vehicles' adaptive cruise control systems in the presence of covert attacks. We define two covert/stealth attacks in the context of cruise control and propose a novel intrusion detection and compensation method to disclose and respond to such attacks. More precisely, we focus on the covert cyber attacks that compromise the integrity of cruise controller and employ a neural network identifier in the IDS engine to estimate the system output dynamically and compare it against the ACC output. If any anomaly is detected, an embedded substitute controller kicks in and takes over the control. We conducted extensive experiments in MATLAB to evaluate the effectiveness of the proposed scheme in a simulated environment.

Connected and Automated Vehicles (CAVs), owing to their characteristics such as seamless and real-time transfer of data, are imperative infrastructural advancements to realize the emerging smart world. The sensor-generated data are, however, vulnerable to anomalies caused due to faults, errors, and/or cyberattacks, which may cause accidents resulting in fatal casualties. To help in avoiding such situations by timely detecting anomalies, this study proposes an anomaly detection method that incorporates a combination of a multi-stage attention mechanism with a Long Short-Term Memory (LSTM)-based Convolutional Neural Network (CNN), namely, MSALSTM-CNN. The data streams, in the proposed method, are converted into vectors and then processed for anomaly detection. We also designed a method, namely, weight-adjusted fine-tuned ensemble: WAVED, which works on the principle of average predicted probability of multiple classifiers to detect anomalies in CAVs and benchmark the performance of the MSALSTM-CNN method. The MSALSTM-CNN method effectively enhances the anomaly detection rate in both low and high magnitude cases of anomalous instances in the dataset with the gain of up to 2.54% in F-score for detecting different single anomaly types. The method achieves the gain of up to 3.24% in F-score in the case of detecting mixed anomaly types. The experiment results show that the MSALSTM-CNN method achieves promising performance gain for both single and mixed multi-source anomaly types as compared to the state-of-the-art and benchmark methods.

Vehicular ad hoc networks (VANETs) have received considerable attention in recent years. Like any other network, privacy and anonymity are a requirement in VANETs. Classic anonymous routing protocols such as onion routing algorithm are fragile in mobile networks due to frequent link breaks. In this article, we propose a novel onion-based anonymous routing protocol for highly mobile vehicular networks. It introduces the concept of location-based dynamic relay groups. In this concept, vehicles dynamically form groups around specific locations to act as cryptographic onion relays. The proposed protocol satisfies source anonymity, destination anonymity, and route anonymity features and is very scalable. Employment of pseudo-IDs further helps in keeping the real identity of vehicles anonymous. The proposed protocol can modify the chain structure on the fly and make it longer to maintain source/destination location anonymity or cut it short to maintain performance. The extensive simulations conducted based on the SUMO traffic simulator showed that the proposed protocol significantly outperforms the naïve onion routing protocol in terms of delivery ratio, delay and number of retransmissions.

This article proposes a hybrid intelligent-classic control approach for reconstruction and compensation of cyber attacks launched on inputs of nonlinear cyber-physical systems (CPS) and industrial Internet of Things systems, which work through shared communication networks. In this article, a class of n-order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel. An intelligent-classic control system is developed to compensate cyber-attacks. Neural network (NN) is designed as an intelligent estimator for attack estimation and a classic nonlinear control system based on the variable structure control method is designed to compensate the effect of attacks and control the system performance in tracking applications. In the proposed strategy, nonlinear control theory is applied to guarantee the stability of the system when attacks happen. In this strategy, a Gaussian radial basis function NN is used for online estimation and reconstruction of cyber-attacks launched on the networked system. An adaptation law of the intelligent estimator is derived from a Lyapunov function. Simulation results demonstrate the validity and feasibility of the proposed strategy in car cruise control application as the testbed.

Sensor networks are a branch of distributed ad hoc networks with a broad range of applications in surveillance and environment monitoring. In these networks, message exchanges are carried out in a multi-hop manner. Due to resource constraints, security professionals often use lightweight protocols, which do not provide adequate security. Even in the absence of constraints, designing a foolproof set of protocols and codes is almost impossible. This leaves the door open to the worms that take advantage of the vulnerabilities to propagate via exploiting the multi-hop message exchange mechanism. This issue has drawn the attention of security researchers recently. In this paper, we investigate the propagation pattern of information in wireless sensor networks based on an extended theory of epidemiology. We develop a geographical susceptible-infective model for this purpose and analytically derive the dynamics of information propagation. Compared with the previous models, ours is more realistic and is distinguished by two key factors that had been neglected before: 1) the proposed model does not purely rely on epidemic theory but rather binds it with geometrical and spatial constraints of real-world sensor networks and 2) it extends to also model the spread dynamics of conflicting information (e.g., a worm and its patch). We do extensive simulations to show the accuracy of our model and compare it with the previous ones. The findings show the common intuition that the infection source is the best location to start patching from, which is not necessarily right. We show that this depends on many factors, including the time it takes for the patch to be developed, worm/patch characteristics as well as the shape of the network.

( t , n ) threshold secret sharing is a cryptographic mechanism to divide and disseminate information among n participants in a way that at least t ( t ≤ n ) of them should be present for the original data to be retrieved. This has practical applications in the protection of secure information against loss, destruction and theft. In this study, the authors propose a new multi‐secret sharing scheme which is based on Hermite interpolation polynomials. Using the properties of discrete logarithm over elliptic curves and bilinear maps, they have created a verifiable scheme in which there is no need for a secure channel and every participant chooses their own share. This feature does not let the dealer cheat. The proposed method is dynamic to the changes in the number and value of the secrets as well as the threshold. In addition, it has the multi‐use property which reduces the cost of secret distribution in multiple rounds of operation. The public values used in the proposed scheme are less than those of schemes providing similar features and the computations are also less complex. At the end of this study, they have compared the author's scheme with the similar ones against a comprehensive set of key features used in secret sharing.

In many network applications, the nature of traffic is of burst type. Often, the transient response of network to such traffics is the result of a series of interdependant events whose occurrence prediction is not a trivial task. The previous efforts in IEEE 802.15.4 networks often followed top-down approaches to model those sequences of events, i.e., through making top-view models of the whole network, they tried to track the transient response of network to burst packet arrivals. The problem with such approaches was that they were unable to give station-level views of network response and were usually complex. In this paper, we propose a non-stationary analytical model for the IEEE 802.15.4 slotted CSMA/CA medium access control (MAC) protocol under burst traffic arrival assumption and without the optional acknowledgements. We develop a station-level stochastic time-domain method from which the network-level metrics are extracted. Our bottom-up approach makes finding station-level details such as delay, collision and failure distributions possible. Moreover, network-level metrics like the average packet loss or transmission success rate can be extracted from the model. Compared to the previous models, our model is proven to be of lower memory and computational complexity order and also supports contention window sizes of greater than one. We have carried out extensive and comparative simulations to show the high accuracy of our model.

Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people’s behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people’s preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.

Some safety applications in vehicular ad-hoc networks (VANETs) require the dissemination of safety information to all nearby vehicles in a broadcast fashion. Each vehicle should periodically broadcast its state information up to a safety range around itself to avoid likely collisions. This causes a congested channel in dense areas especially in multi-lane roads and leads to significant performance reduction. In this paper, using a Markov model, we analytically derive the percentage of channel utilization as well as the packet transmission rate based on the contention window size, carrier sense range, density of vehicles and packet generation rate. Unlike the previous models, the devised Markov model enables us to derive the probability of packet obsolescence before broadcasting from the probability mass function of service delay. Also, we can evaluate the performance of tracking applications for large and small contention window sizes. The extensive simulations carried out confirm the accuracy of our model.

Instances of batch traffic can be seen in many applications. A set of sensors around the source of a sudden event which try to report it to the cluster head, and energy conserving MAC protocols in hierarchical networks with sleep-awake schedules which lead to burst traffics toward a single destination are good examples. The transient non-stationary nature of network reaction to batch arrivals has led to the development of complex models in order to estimate the network quantities of interest. In this paper, we propose a non-stationary Markov model to capture the transient characteristics of IEEE 802.15.4-based networks response to batch traffic arrivals of one- shot type. Mainly, the slotted CSMA/CA MAC protocol of the IEEE 802.15.4 standard is analyzed and the packet loss metric value is extracted from the proposed model. Extensive simulations were conducted to evaluate the accuracy of the proposed model and to compare its performance with that of the previous models.

Broadcasting a request or challenge is a classic method of collecting local information in distributed wireless networks. Neighbor discovery is known to be a fundamental element in ad hoc and sensor networks topology formation, which takes advantage of such methods. Most of the current neighbor discovery protocols rely on a challenge or request broadcast by the discovering node called “Hello.” Hello flooding attack was specifically designed to exploit the broadcasting nature of these protocols in order to convince a large group of nodes that the sender is their neighbor by using very high transmission power. Several studies have been done to mitigate the effectiveness of the flooding threats but little effort has been made in modeling and analyzing this problem. Arguing that random channel access protocols must be inevitably employed in neighbor discovery, we propose an analytical approach for stochastic modeling of the challenge-broadcasting scenarios in networks using slotted carrier sense multiple access with collision avoidance (CSMA/CA) protocols. We model the nonstationary channel right after issuance of the request by a recursive method and then put forward an approach to find the broadcaster's approximate payoff. The model also supports the cases where the broadcaster is a malicious node with an abnormally high transmission and reception range, which is found in severe flooding attacks. We investigate the applications of the model in finding the optimal attack range for the flooding adversaries and deriving a flood-resilient medium access control (MAC) protocol design framework to increase the security of challenge-response protocols. The latter one is especially relevant to mobile networks as it provides a low-cost solution. This paper describes the detailed analysis of the proposed theoretical framework as well as the comprehensive evaluations that have been carried out via simulations.