Wireless ad-hoc networks operate independently of existing infrastructure, using devices like access points to connect end-user computing devices. Current methods face issues such as low detection accuracy, structural deviation, and extended processing times. This paper proposes a cross-layer approach that leverages knowledge from the physical and Medium Access Control (MAC) layers, which is then shared with higher layers to effectively mitigate wormhole and blackhole attacks. A wormhole attack disrupts communication through tunneling, while a blackhole attack manipulates network traffic by impersonating the source. The proposed cross-layer framework integrates the network, MAC, and physical layers, and is independent of specific network protocols. The physical layer handles channel interference, the network layer manages process handling, and the MAC layer oversees bandwidth information and tracks failed transmissions. Performance metrics are measured in seconds. The Enhanced Support Vector Machine (E-SVM) algorithm, implemented using NS3 software, demonstrates superior performance compared to traditional SVM techniques across multiple metrics, including average energy consumption, average remaining energy, packets received, packet delivery ratio, delay, jitter, throughput, normalized overhead, dropping ratio, and goodput. Simulation results show that E-SVM achieves a 12.5% dropping ratio, 98.459% energy consumption, and an 89.2879% packet delivery ratio, outperforming existing SVM techniques across various network sizes.
The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhole and greyhole attacks pose significant risks to the availability and integrity of CPSs. The current detection and mitigation approaches often struggle to accurately differentiate between legitimate and malicious behavior, leading to ineffective protection. This paper introduces Gini-index and blockchain-based Blackhole/Greyhole RPL (GBG-RPL), a novel technique designed for efficient detection and mitigation of blackhole and greyhole attacks in smart health monitoring CPSs. GBG-RPL leverages the analytical prowess of the Gini index and the security advantages of blockchain technology to protect these systems against sophisticated threats. This research not only focuses on identifying anomalous activities but also proposes a resilient framework that ensures the integrity and reliability of the monitored data. GBG-RPL achieves notable improvements as compared to another state-of-the-art technique referred to as BCPS-RPL, including a 7.18% reduction in packet loss ratio, an 11.97% enhancement in residual energy utilization, and a 19.27% decrease in energy consumption. Its security features are also very effective, boasting a 10.65% improvement in attack-detection rate and an 18.88% faster average attack-detection time. GBG-RPL optimizes network management by exhibiting a 21.65% reduction in message overhead and a 28.34% decrease in end-to-end delay, thus showing its potential for enhanced reliability, efficiency, and security.
The rapid integration of Internet of Things (IoT) technologies into smart city infrastructures has enabled advanced urban services but also introduced significant challenges related to scalability, reliability, and security. Traditional routing protocols often lack robust mechanisms to ensure data integrity, defend against cyber threats, and maintain high performance in large-scale deployments. This paper presents a blockchain-enabled secure IoT routing framework designed to strengthen trust, enhance routing efficiency, and mitigate network attacks in smart city environments. The proposed framework integrates Hyperledger Fabric with Practical Byzantine Fault Tolerance (PBFT) consensus into the routing decision process, enabling immutable identity verification, consensus-approved route validation, and anomaly detection. Using NS-3 simulations, its performance is compared against three conventional protocols 6TiSCH, SDN-based routing, and ROLL across various network sizes and traffic scenarios. Evaluation metrics include throughput, end-to-end latency, packet delivery ratio (PDR), and resilience against blackhole and spoofing attacks. The results indicate that the blockchain-enabled framework can improve throughput by approximately 30%, reduce latency by up to 40%, increase PDR by up to 8% points, and significantly reduce successful security breaches compared to traditional approaches. These findings suggest that blockchain can provide secure, scalable, and high-performance IoT networking solutions for future smart cities.
The growth of Wireless Sensor Networks (WSNs) in essential fields such as health care, defense, and environmental monitoring poses severe cybersecurity threats, specifically to the network layer. Network layer attacks such as Blackhole, Flooding, and Selective Forwarding are common and tend to have a serious impact on the data integrity, the reliability, and the lifetime of the network. To counter these vulnerabilities, this study proposes the development of a hybrid deep learning (DL) model combining the Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) structures for the detection of multi-class WSNs attacks. The proposed model combines the temporal learning capability of GRU with the long-term dependency learning strength of LSTM to accurately detect malicious attacks. Using WSNBFSF dataset, different types of experiments have been conducted to evaluate the performance of the developed model in accuracy, precision, recall, and F1-score metric measures. Experimental results indicate that the GRU+LSTM hybrid model offers improved classification performance when compared to traditional DL models, i.e., Deep Neural Network (DNN), Recurrent Neural Networks (RNN), Artificial Neural Network (ANN), Attention, LSTM, and GRU models for multi-class classification tasks. The model achieved an overall accuracy of 97.41% (without SMOTE) and 98.74% using a Synthetic Minority Oversampling Technique (SMOTE) technique and k-fold validation along with macro F1 and weighted F1 of 0.9874 across multiple attack classes. These results indicate that the proposed architecture effectively captures complex sequential attack patterns and provides a reliable solution for intelligent intrusion detection in WSN environments.
Denial-of-service (DoS) attacks pose a major threat to various kinds of computer networks. There are several kinds of networks that are victims of DoS attacks, one of them being the wireless sensor network (WSN). The main objective of this work is to detect such attacks in wireless sensor networks. These networks are susceptible to intrusion attacks because of their fragile defense mechanisms in unattended environments. Thus, a suitable intrusion detection system must be created to optimally detect DoS attacks and prevent them. This work proposes a hybrid technique called Grasshopper Optimization Algorithm-Genetic Algorithm (GOA-GA), which combines the advantages of two metaheuristic algorithms, namely, the Grasshopper Optimization Algorithm and the Genetic Algorithm, to optimize feature selection based on the given WSN dataset. After optimal feature selection and training, the machine learning classification algorithms classify whether the traffic is normal or benign in the form of four types of DoS attacks, namely, Blackhole, Scheduling, Flooding, and Grayhole attacks. The proposed model and algorithms used are further validated and compared based on standard performance metrics. The experiments conducted during the research show that the GOA-GA method, when combined with the KNN classifier, achieves an accuracy of 95.51% and a recall of 95.51%, exhibiting competitive performance relative to recent state-of-the-art approaches while reducing feature dimensionality and computational overhead. These results indicate that the proposed hybrid optimization strategy offers a robust and efficient solution for DoS attack detection in WSNs, contributing to ongoing research in information security.
A mobile ad hoc network (MANET) is a self-configurable network connected by wireless links. This type of network is only suitable for provisional communication links as it is infrastructure-less and there is no centralized control. Wireless Sensor Networks (WSNs) and Mobile Ad Hoc Networks (MANETs) are increasingly employed in mission-critical applications due to the flexibility and scalability. However, the open and dynamic nature of these networks makes it highly susceptible to various security threats, including zero-day attacks, and denial-of-service (DoS), leading to degraded network performance. Mobile Ad Hoc Networks (MANETs) are decentralized and highly dynamic environments that face significant challenges in secure communication and routing efficiency due to node mobility and vulnerability to attacks such as blackhole, Sybil, and DoS. To address these challenges, this framework proposes a novel trust-based secure communication framework that integrates adaptive clustering, optimized routing, and intelligent intrusion detection. The model begins with Density-Aware Possibilistic C-Means (DAPCM) for robust and flexible cluster formation, followed by Enhanced Crisscross Moss Growth Optimization (ECCMGO) for optimal Cluster Head (CH) selection based on energy, trust, and link stability. Each node’s trustworthiness is evaluated through a Multi-Attribute Trust Model (MATM) using behavioral parameters such as packet forwarding ratio, delay, and energy level. These trust scores are embedded into the Modified Pine Cone Optimization Algorithm (MPCOA) to establish secure and efficient routing paths by avoiding low-trust or malicious nodes. To ensure proactive threat detection, the system incorporates an Attention-based Spatio-Temporal Relational Architecture (ASTRA), which captures both spatial interactions and temporal behavioral patterns across nodes. ASTRA enables accurate, real-time detection of attacks by learning complex trust dynamics and network anomalies. Simulation outcomes show significant enhancement in energy efficiency, packet delivery ratio, and intrusion detection accuracy, confirming the model’s suitability for real-time, mission-critical MANET applications.
MS imaging studies have demonstrated an ependymal "surface-in" distribution of white matter damage. The origin of this effect and how it impacts myelinated axons in people with MS (PwMS) is poorly understood. To investigate (1) whether there is clear evidence of tract-wise, periventricular damage, based on fractional anisotropy, mean diffusivity, axonal water fraction (AWF) and myelin volume fraction (MVF), in PwMS, (2) if such tissue damage is associated with T1-"black-hole" (T1-BH) and/or non-BH lesions, and (3) if periventricular quantitative MRI abnormalities are correlated with lateral ventricle (LV) and choroid plexus (CP) enlargement. We applied structural, MT-weighted, and multi-shell diffusion MRI in healthy controls and PwMS to quantify periventricular gradient damage to axons and myelin. We also evaluated the correlation between such gradient damage and both non-BH and T1-BH lesion loads, and brain volume changes. Clear periventricular gradients exist in both AWF and MVF measures in the Association and Projection Tracts. They are strongly correlated with T1-BH lesion load, and with LV and CP enlargement in PwMS with EDSS > 2. Tract-specific, periventricular gradients in PwMS exist prominently in AWF and MVF, are strongly associated with T1-BH lesion loads, and correlate with LV and CP enlargement.
The bell-like ringdown of the gravitational field in the final stage of massive black-hole mergers is now routinely detected on Earth by the latest generation of gravitational-wave detectors. Its spectrum is interpreted as a sum of damped sinusoidal vibrations of spacetime in the vicinity of the black hole. These so-called quasinormal modes are the subject of extensive current studies, yet their physical nature remains elusive. Here, we emulate in the laboratory genuine four-dimensional (3+1)D black-hole metrics using an effective (2+1)D optical metric defined on a two-dimensional curved surface that preserves the features of light-like geodesics. We analytically compute the quasinormal modes of the optical cavity and show that, in addition to conventional whispering-gallery modes (WGMs) supported near the cavity boundary, a new family of modes is confined around the photon sphere, the unstable region where spacetime curvature traps light in circular orbits. By 3D-printing non-Euclidean dye-doped microcavities, we demonstrate lasing in both WGMs and photon-sphere modes, with the latter exhibiting spatial profiles in close agreement with analytical predictions. These results place our system within the broader framework of analogue-gravity experiments, providing a complementary photonic platform to investigate black-hole photon-sphere physics under tabletop conditions and inspiring new approaches to microcavity photonics.
Mobile Ad hoc Networks (MANETs) represent a decentralized and self-tuning network paradigm that relies on routing protocols to transmit data from source to destination. However, the absence of a fixed infrastructure makes MANETs vulnerable to various security threats, including blackhole and gray hole attacks. Addressing these vulnerabilities is critical to ensuring the reliability and security of MANETs. The paper proposes an agent-based approach for effectively identifying and preventing such attacks within the MANET environment. Unlike existing static or centralized models, agent-based approach deploys dedicated agent nodes in each cluster for real-time monitoring and classification of malicious behaviour. Furthermore, the paper introduces an energy-efficient optimum clustering method, leveraging ensemble clustering-based optimization techniques, to select cluster heads responsible for data aggregation. The combination of optimal clustering and agent-based attack detection enhances the overall security and performance of the MANET. This also significantly improving energy efficiency and data aggregation reliability. Each cluster in the proposed model is equipped with an attack detection agent node, which plays a critical role in identifying suspicious, blackhole, wormhole, and normal nodes within the incoming traffic. This proactive detection mechanism ensures timely response and mitigation of potential security threats. The development of an ensemble-based clustering optimization technique to enhance energy efficiency and improve data aggregation. In addition to the detection mechanism, the study performs a comprehensive comparison of multiple machine learning algorithms. This comparison aims to determine the most effective models for accurate attack identification and trust score generation for network nodes. This determines the most effective algorithms based on accuracy and computational cost by enabling more accurate threat identification and trust-based routing decision. By combining agent-based attack detection, energy-efficient clustering, and intelligent machine learning models, this research work offers a comprehensive and robust solution to enhance the security and reliability of MANETs. Experimental results on simulated MANET environments demonstrate that the proposed approach significantly improves detection accuracy and enhances network lifetime and throughput compared to existing methods. Specifically, proposed approach achieved a throughput of 93 Kbps. This shows approx. 8% improvement over existing approach. The results demonstrate the effectiveness of the proposed approach in providing valuable insights for future research in securing MANETs.
We revisit the black-hole information problem from the viewpoint of a population-coherence decomposition of density-matrix purity. Building on a previously developed formalism for n-dimensional density matrices, we characterize each state by a normalized global purity index and two complementary indices, which quantify the contributions of level populations and coherences. This yields a simple quadratic relation and a geometric representation in a "population-coherence plane", where different routes to purity can be distinguished. In the two-level case, we construct explicit families of states with identical spectra and global purity but opposite internal structure, realizing population-dominated and coherence-dominated routes. We then apply this framework to a standard Page-type evaporation model without an explicit Hamiltonian, in which a black hole and its Hawking radiation form a bipartite pure state with varying Hilbert-space dimensions. Using known results for typical reduced states in large dimensions, we analyze the behavior of population and coherence components of purity along the evaporation process. Under the physically motivated requirement that, in this energy-free setting, the radiation populations remain nearly uniform in the chosen basis, we show that the late-time recovery of purity must be coherence-dominated: the global purity of the radiation approaches unity while the population index stays small and the coherence index carries essentially all the purity.
Area-law entropy appears in local quantum ground states, low-temperature Gibbs states, and gravitational physics, whereas classical thermodynamics is formulated with volume-extensive entropy. We propose a coarse-grained information-theoretic framework, based on an effective free-energy functional combining Fisher information, a potential term, and Shannon entropy, that organises these different scalings within a single thermodynamic picture. Comparing localisation costs, external stabilisation, and gravitational self-interaction at the level of scaling reveals three regimes. At microscopic scales, locality and low-temperature coherence enforce area-type entropy scaling. At intermediate scales, volume-law entropy emerges as an effective regime sustained by non-gravitational confinement or external support; in the absence of such support, volume-extensive entropy does not by itself define an intrinsically stable equilibrium. At large scales dominated by gravitational self-interaction, a reduced scaling analysis identifies area-type behaviour as the distinguished infrared scaling, consistent with black-hole thermodynamics and with macroscopic universality requirements. The framework clarifies the limited domain of classical extensivity and offers a unified coarse-grained perspective on the recurrence of area-law scaling across quantum and gravitational settings.
Recent discoveries of faint active galactic nuclei (AGN) at the redshift frontier have revealed a plethora of broad Hα emitters with optically red continua, named little red dots (LRDs)1, which comprise 15-30% of the high-redshift broad-line AGN population2. Owing to their peculiar properties3-6, modelling LRDs with standard AGN scenarios has proven challenging. In particular, the validity of single-epoch virial mass estimates in determining the black-hole masses of LRDs has been called into question, with some models claiming that masses might be overestimated by up to two orders of magnitude7-10. Here we report a direct, dynamical black-hole mass measurement in a strongly lensed LRD at a redshift of 7.04. The combination of lensing with deep spectroscopic data reveals a rotation curve that is inconsistent with a nuclear star cluster, yet can be well explained by Keplerian rotation around a point mass of 50 million solar masses, consistent with virial black-hole mass estimates. The Keplerian rotation leaves little room for any stellar component in a host galaxy, as we conservatively infer MBH/M⁎ > 2 (where MBH is the black-hole mass and M⁎ is the stellar mass). Such a 'naked' black hole, together with its near-pristine environment11, indicates that this LRD is a massive black-hole seed caught in its earliest accretion phase.
Rapidly rotating black-hole spacetimes outside general relativity are key to many tests of Einstein's theory. We here develop an efficient spectral method to represent such spacetimes analytically, in closed-form, and to high accuracy, in a large class of effective-field-theory extensions of general relativity. We exemplify this method by constructing, for the first time, closed-form and analytic representations of spinning black holes in scalar-Gauss-Bonnet, dynamical Chern-Simons, and axidilaton gravity to an accuracy better than 10^{-8} for all dimensionless spins below 0.99.
We construct a novel effective field theory for a compact body coupled to gravity, whose key feature is that the dynamics of gravitational perturbations is explicitly determined by known solutions in black hole perturbation theory in four dimensions. In this way, the physics of gravitational perturbations in curved space are already encoded in the effective field theory, thus bypassing the need for the higher-order calculations that constitute a major hurdle in standard approaches. Concretely, we model the compact body as a spherical shell, whose finite size regulates short-distance divergences in four dimensions and whose tidal responses are described by higher-dimensional operators. As an application, we consider scalar perturbations and derive new results for scalar Love numbers through O(G^{9}) for Schwarzschild black holes and for generic compact bodies. Finally, our analysis reveals an intriguing structure of the scalar black-hole Love numbers in terms of the Riemann zeta function, which we conjecture to hold to all orders.
Via constructing an explicit Lagrangian for which the perturbation equations are analogs of a scalar field propagating in a planar black-hole space-time, it is found that all planar black holes conformal to a Painlevé-Gullstrand-type line element can be realized as analog metrics. We also introduce the concept of holographic entanglement entropy for planar black-hole space-times. This is valid for an arbitrary choice of conformal and blackening factor, thereby vastly extending the number of known examples of explicitly known analog metrics.
The binary black hole signal GW250114, the loudest gravitational wave detected to date, offers a unique opportunity to test Einstein's general relativity (GR) in the high-velocity, strong-gravity regime and probe whether the remnant conforms to the Kerr metric. Upon perturbation, black holes emit a spectrum of damped sinusoids with specific, complex frequencies. Our analysis of the postmerger signal shows that at least two quasinormal modes are required to explain the data, with the most damped remaining statistically significant for about one cycle. We probe the remnant's Kerr nature by constraining the spectroscopic pattern of the dominant quadrupolar (ℓ=m=2) mode and its first overtone to match the Kerr prediction to tens of percent at multiple postpeak times. The measured mode amplitudes and phases agree with a numerical-relativity simulation having parameters close to GW250114. By fitting a parametrized waveform that incorporates the full inspiral-merger-ringdown sequence, we constrain the fundamental (ℓ=m=4) mode to tens of percent and bound the quadrupolar frequency to within a few percent of the GR prediction. We perform a suite of tests-spanning inspiral, merger, and ringdown-finding constraints that are comparable to, and in some cases 2-3 times more stringent than those obtained by combining dozens of events in the fourth Gravitational-Wave Transient Catalog. These results constitute the most stringent single-event verification of GR and the Kerr nature of black holes to date, and outline the power of black-hole spectroscopy for future gravitational-wave observations.
We present an observational confirmation of Hawking's black-hole area theorem using the newly released gravitational-wave data from the GWTC-4.0. We analyze the high signal-to-noise ratio binary black hole merger GW230814 and measure the (total) horizon area of the black holes before and after the merger. For preferred (and reasonable) choices of the post-truncation start time, the horizon area of the remnant black hole is found to be greater than the total horizon area of the two pre-merger black holes at a high possibility (at least ≳99.5%). Importantly, our analysis accounts for sky-location uncertainty. These results provide a stringent observational confirmation of the black-hole area law, further bolstering the validity of classical general relativity in the dynamical, strong-field regime.
We propose a time crystal based on a quantum black-hole laser, where the genuinely spontaneous character of the symmetry breaking stems from the self-amplification of spontaneous Hawking radiation. The resulting Hawking time crystal (HTC) is characterized by the periodic dependence of the out-of-time density-density correlation function, while equal-time observables are time independent because they embody averages over different realizations with a random oscillation phase. The HTC provides a nonlinear periodic analog of the Andreev-Hawking effect, exhibiting anticorrelation bands resulting from the spontaneous, quantum emission of pairs of dispersive waves and solitons into the upstream and downstream regions. Remarkably, we prove that any parametric amplifier has associated a time operator, which leads to a unique characterization of the time-crystal formation in terms of two time operators: one associated with the initial black-hole laser and another associated with the final spontaneous Floquet state.
Stellar theory predicts a forbidden range of black-hole masses between approximately 50 M⊙ and 130 M⊙ owing to pair-instability supernovae1-7, but evidence for such a gap in the mass distribution from gravitational-wave astronomy has proved elusive. Early hints of a cut-off in black-hole masses at about 45 M⊙ disappeared with the subsequent discovery of more massive binary black holes8,9. Here we report evidence of the pair-instability gap in LIGO-Virgo-KAGRA's fourth Gravitational-Wave Transient Catalog (GWTC-4), with a lower boundary of 4 4 - 4 + 5 M ⊙ (90% credibility). Although the gap is not present in the distribution of primary masses m1 (the bigger of the two black holes in a binary system), it appears unambiguously in the distribution of secondary masses m2, in which m2 ≤ m1. The location of the gap lines up well with a previously identified transition in the binary black-hole spin distribution; binaries with primary components in the gap tend to spin more rapidly than those below the gap. We interpret these findings as evidence for a subpopulation of hierarchical mergers: binaries in which the primary component is the product of a previous black-hole merger and thus populates the gap. Our measurement of the location of the pair-instability gap constrains the S-factor for 12C(α, γ)16O at 300 keV to 26 0 - 108 + 190 keV barns .
Intrusion detection in Wireless Sensor Networks (WSNs) is an emerging area of research, given their extensive use in sensitive fields like military surveillance, healthcare, environmental monitoring, and smart cities. However, WSNs face several security challenges due to their limited computational capabilities and energy constraints. Their deployment in open, unattended environments makes them especially vulnerable to threats like eavesdropping, interference, and jamming. To address this problem, Random Forest (RF) is a popular machine learning model. The RF model can be tweaked because of its multiple hyperparameters. Tuning these parameters manually is tedious, as the combinations will be exponential. This work presents an enhanced intrusion detection approach by integrating Tabu Search (TS) optimization with a RF classifier. As a result, TS will help RF automatically search optimal hyperparameters and improve the generalization ability. This work integrates the pros of TS with RF. The model was tested on three different datasets, i.e., (a) the WSN-DS dataset, (b) CICIDS 2017, and (c) the CIC-IoT 2023 dataset, which shows better results on different metrics like precision, recall, F1-score, Cohen's Kappa, and ROC AUC. Detection of Blackhole and Gray Hole attacks also improved, demonstrating the effectiveness of combining metaheuristic optimization with ensemble learning for stronger WSN security.