Computational Overhead Research Articles

Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment

Recently, the Wireless Body Area Networks (WBAN) have become a promising and practical option in the tele-care medicine information system that aids for the better clinical monitoring and diagnosis. The trend of using Internet of Things (IoT) has propelled the WBAN technology to new dimension in terms of its network characteristics and efficient data transmission. However, these networks demand the strong authentication protocol to enhance the confidentiality, integrity, recoverability and dependability against the emerging cyber-physical attacks owing to the exposure of the IoT ecosystem and the confidentiality of biometric data. Hence this study proposes the Fog based WBAN infrastructure which incorporates the hybrid symmetric cryptography schemes with the chaotic maps and feed forward networks to achieve the physiological data info security without consuming the characteristics of power hungry WBAN devices. In the proposed model, scroll chaotic maps are iterated to produce the high dynamic keys streams for the real time applications and feed-forward layers are leveraged to align the complex input-output associations of cipher data for subsequent mathematical tasks. The feed forward layers are constructed which relies on the principle of Adaptive Extreme Learning Machines (AELM) thereby increasing randomness in the cipher keys thereby increasing its defensive nature against the different cyber-physical attacks and ensuring the high secured encrypted-decrypted data communication between the users and fog nodes. The real time analysis is conducted during live scenarios. BAN-IoT test beds interfaced with the heterogeneous healthcare sensors and various security metrics are analysed and compared with the various residing cryptographic algorithms. Results demonstrates that the recommended methodology has exhibited the high randomness characteristics and low computational overhead compared with the other traditional BAN oriented cryptography protocol schemes

Investigating the Impact of Random Field Element Size on Soil Slope Reliability Analysis

The determination of the optimal random field element (RFE) size is crucial in soil slope reliability analysis as it governs the trade-off between precision in failure probability calculations and computational efficiency. Given the substantial computational burden associated with smaller RFE sizes, studies on their impact on slope failure probability are scarce. This research examines the influence of RFE size on failure probability and safety factor, employing the Karhunen–Loève expansion to generate random fields and integrating the simplified Bishop method with particle swarm optimization (PSO) to assess slope stability. Through Monte Carlo Simulation (MCS), this study investigates the effects of the ratio of slope height to RFE size (H/De) on slope reliability metrics across two illustrative cases. Results reveal a notable influence of H/De on the distribution of safety factors (Fs) and failure probability (PF), with overestimation observed at smaller H/De ratios. When H/De exceeds 10 for Example 1 and 15 for Example 2, the Fs distribution patterns in both scenarios stabilize significantly, displaying minimal variability. The PF of Example 1 and Example 2 decreases with the increase of H/De and remains basically unchanged when H/De exceeds 10 and 15, respectively. Consequently, a recommended H/De ratio of 20 is proposed based on the analyzed cases, facilitating accurate calculations while mitigating computational overhead.

PQSF: post-quantum secure privacy-preserving federated learning

In federated learning, secret sharing is a key technology to maintain the privacy of participants’ local models. Moreover, with the rapid development of quantum computers, existing federated learning privacy protection schemes based on secret sharing will no longer be able to guarantee the data security of participants in the post-quantum era. In addition, existing privacy protection methods have the problem of high communication and computational overhead. Although the multi-stage secret sharing scheme proposed by Pilaram et al. is one of the effective solutions to the above problems, existing studies have proven the privacy leakage risk of this scheme. This paper firstly designs a new lattice-based multi-stage secret sharing scheme Improved-Pilaram to solve the security problem, which allows participants to use public vectors to reconstruct different secret values without changing the secret sharing. Based on Improved-Pilaram, this article proposes a post-quantum secure federated learning scheme PQSF. PQSF uses double masking technology to encrypt model parameters and achieves mask reconstruction through secret sharing. Since Improved-Pilaram is multi-stage, participants do not need to update their local secret shares frequently during training. Analysis and experimental results show that the PQSF proposed in this paper reduces the communication complexity between participants and reduces the computational overhead by about 20% compared with existing solutions.

Secure and Efficient Data Hiding in the Cloud with Quantum-Resistant Encryption

Traditional steganographic methods in multi-cloud environments face significant challenges in cloud security, including complex key management, high computational overhead, and vulnerabilities to various attacks regaring secure data concealment. This paper presents a novel steganographic mechanism tailored for single cloud environments that address these issues. The proposed mechanism leverages a combination of homomorphic encryption, elliptic curve cryptography (ECC), and lattice-based cryptographic techniques to ensure robust security against classical and quantum attacks. Confining operations to a single cloud simplifies credential and key management, reducing operational complexity and enhancing system efficiency. The proposed approach mitigates the risks associated with multi-cloud environments and offers strong resistance to brute-force, differential, and steganalysis attacks. The implementation and validation of the mechanism demonstrate its efficacy in maintaining data integrity, confidentiality, and availability while minimizing computational resources. The results indicate that this approach significantly improves the security and performance of steganographic operations in cloud storage environments, setting a new standard for secure data concealment in cloud computing.

Full Iso-Recursive Types

There are two well-known formulations of recursive types: iso-recursive and equi-recursive types. Abadi and Fiore [LICS 1996] have shown that iso- and equi-recursive types have the same expressive power. However, their encoding of equi-recursive types in terms of iso-recursive types requires explicit coercions. These coercions come with significant additional computational overhead, and complicate reasoning about the equivalence of the two formulations of recursive types. This paper proposes a generalization of iso-recursive types called full iso-recursive types. Full iso-recursive types allow encoding all programs with equi-recursive types without computational overhead. Instead of explicit term coercions, all type transformations are captured by computationally irrelevant casts, which can be erased at runtime without affecting the semantics of the program. Consequently, reasoning about the equivalence between the two approaches can be greatly simplified. We present a calculus called λ Fi µ , which extends the simply typed lambda calculus (STLC) with full iso-recursive types. The λ Fi µ calculus is proved to be type sound, and shown to have the same expressive power as a calculus with equi-recursive types. We also extend our results to subtyping, and show that equi-recursive subtyping can be expressed in terms of iso-recursive subtyping with cast operators.

Edge-assisted U-shaped split federated learning with privacy-preserving for Internet of Things

In the realm of the Internet of Things (IoT), deploying deep learning models to process data generated or collected by IoT devices is a critical challenge. However, direct data transmission can cause network congestion and inefficient execution, given that IoT devices typically lack computation and communication capabilities. Centralized data processing in data centers is also no longer feasible due to concerns over data privacy and security. To address these challenges, we present an innovative Edge-assisted U-Shaped Split Federated Learning (EUSFL) framework, which harnesses the high-performance capabilities of edge servers to assist IoT devices in model training and optimization process. In this framework, we leverage Federated Learning (FL) to enable data holders to collaboratively train models without sharing their data, thereby enhancing data privacy protection by transmitting only model parameters. Additionally, inspired by Split Learning (SL), we split the neural network into three parts using U-shaped splitting for local training on IoT devices. By exploiting the greater computation capability of edge servers, our framework effectively reduces overall training time and allows IoT devices with varying capabilities to perform training tasks efficiently. Furthermore, we proposed a novel noise mechanism called LabelDP to ensure that data features and labels can securely resist reconstruction attacks, eliminating the risk of privacy leakage. Our theoretical analysis and experimental results demonstrate that EUSFL can be integrated with various aggregation algorithms, maintaining good performance across different computing capabilities of IoT devices, and significantly reducing training time and local computation overhead.

Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning

Understanding structural flexibility of metal-organic frameworks (MOFs) via molecular dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and quantum-chemistry methods provide highly accurate molecular dynamics, but the computational overheads limit their use in long time-dependent simulations. In contrast, classical force fields struggle with the description of coordination bonds. Here we develop a DFT-accurate machine-learning spectral neighbor analysis potentials for two representative MOFs. Their structural and vibrational properties are then studied and tightly compared with available experimental data. Most importantly, we demonstrate an active-learning algorithm, based on mapping the relevant internal coordinates, which drastically reduces the number of training data to be computed at the DFT level. Thus, the workflow presented here appears as an efficient strategy for the study of flexible MOFs with DFT accuracy, but at a fraction of the DFT computational cost.

Enhancing Graph Neural Networks via Memorized Global Information

Graph neural networks (GNNs) have gained significant attention for their impressive results on different graph-based tasks. The essential mechanism of GNNs is the message-passing framework, whereby node representations are aggregated from local neighborhoods. Recently, Transformer-based GNNs have been introduced to learn the long-range dependencies, enhancing performance. However, their quadratic computational complexity, due to the attention computation, has constrained their applicability on large-scale graphs. To address this issue, we propose MGIGNN ( M emorized G lobal I nformation G raph N eural N etwork), an innovative approach that leverages memorized global information to enhance existing GNNs in both transductive and inductive scenarios. Specifically, MGIGNN captures long-range dependencies by identifying and incorporating global similar nodes, which are defined as nodes exhibiting similar features, structural patterns and label information within a graph. To alleviate the computational overhead associated with computing embeddings for all nodes, we introduce an external memory module to facilitate the retrieval of embeddings and optimize performance on large graphs. To enhance the memory-efficiency, MGIGNN selectively retrieves global similar nodes from a small set of candidate nodes. These candidate nodes are selected from the training nodes based on a sparse node selection distribution with a Dirichlet prior. This selecting approach not only reduces the memory size required but also ensures efficient utilization of computational resources. Through comprehensive experiments conducted on ten widely-used and real-world datasets, including seven homogeneous datasets and three heterogeneous datasets, we demonstrate that our MGIGNN can generally improve the performance of existing GNNs on node classification tasks under both inductive and transductive settings.

Efficient Maliciously Secure Oblivious Exponentiations

Oblivious Pseudorandom Functions (OPRFs) allow a client to evaluate a pseudorandom function (PRF) on her secret input based on a key that is held by a server. In the process, the client only learns the PRF output but not the key, while the server neither learns the input nor the output of the client. The arguably most popular OPRF is due to Naor, Pinkas and Reingold (Eurocrypt 2009). It is based on an Oblivious Exponentiation by the server, with passive security under the Decisional Diffie-Hellman assumption. In this work, we strengthen the security guarantees of the NPR OPRF by protecting it against active attacks of the server. We have implemented our solution and report on the performance. Our main result is a new batch OPRF protocol which is secure against maliciously corrupted servers, but is essentially as efficient as the semi-honest solution. More precisely, the computation (and communication) overhead is a multiplicative factor o ( 1 ) as the batch size increases. The obvious solution using zero-knowledge proofs would have a constant factor overhead at best, which can be too expensive for certain deployments. Our protocol relies on a novel version of the DDH problem, which we call the Oblivious Exponentiation Problem (OEP), and we give evidence for its hardness in the Generic Group model. We also present a variant of our maliciously secure protocol that does not rely on the OEP but nevertheless only has overhead o ( 1 ) over the known semi-honest protocol. Moreover, we show that our techniques can also be used to efficiently protect threshold blind BLS signing and threshold ElGamal decryption against malicious attackers.

Real-time Blood Pressure Prediction on Wearable Devices using Edge Based Deep Neural Networks: A Hardware-software Co-design Approach

This paper presents the hardware realization of a real-time blood pressure (BP) prediction model for wearable devices, utilizing long short-term memory (LSTM) deep neural networks (DNNs). The proposed system uses both electrocardiogram (ECG) and photoplethysmogram (PPG) signal values for BP prediction. It aims to address the limitations of traditional BP measurement methods, providing a low error, minimal computational overhead, more accurate and convenient alternative system for individuals with hypertension or at risk for cardiovascular diseases. The utilization of split matrix approach leads to a reduction in hardware complexity across the entire system. This technique involves breaking down the larger weight matrices used in the computations of DNNs into smaller matrices. This fragmentation results in a decrease in the complexity of the hardware responsible for performing matrix vector multiplications (MVMs) within LSTMs. The resultant architecture of the predictive model gains several advantages, including a lowered level of complexity in terms of the space occupied by individual cells, decreased processing delay, and reduced power consumption. Furthermore, this approach enables the achievement of a notably improved minimum achievable clock period of 2.972 ns. This prediction model can operate locally on wearable devices, reducing the reliance on cloud computing and improving privacy and security. The performance evaluations are carried out using both analytical and implementation results. The results indicate that the proposed model can be practically applied to real-world problems and can potentially enhance the accuracy of various machine-learning tasks.

Fish feeding behavior quantification and automatic acoustic event detection: algorithmic optimization with deep learning

In recent years, the integration of sensor processing technologies with intelligent feeding control systems has become crucial for enhancing recirculating aquaculture systems (RAS). Intelligent feeding control significantly improves profitability and animal welfare in aquaculture by accurately monitoring feed intake and reducing waste. Acoustic methods have proven effective in automating feed monitoring, particularly in assessing feeding intensity. Automatic Audio Event Detection (AED) holds promise for identifying sound events associated with feeding activities, enabling precise monitoring of feeding behavior and estimation of feed consumption through frequency-based feeding signatures. Advancements in sensor technologies and computational capabilities enable real-time collection, processing, and analysis of acoustic data. However, handling high volumes of data with minimal latency and high accuracy poses challenges. To address these challenges, we propose a systematic approach: (1) Data preprocessing using wavelet transforms enhances efficiency and reduces computational overhead. (2) Data pruning focuses on informative features and reduces input data dimensionality, improving classification accuracy. (3) We adopt a weakly-labeled semi-supervised learning model with Convolutional Neural Networks (CNNs) to detect feeding events, improving classification performance. (4) Ensemble learning combines multiple classifiers to mitigate overfitting and enhance model generalization. Our methodology shows promise in accurately quantifying fish feeding behavior using acoustic signals.

Leveraging TenSEAL

In our interconnected world, the use of Internet of Things (IoT) devices generates vast amounts of sensitive data. To secure this data while extracting insights, privacy-preserving machine learning (PPML) tools like Fully Homomorphic Encryption (FHE) are crucial. This study compares two FHE schemes, BFV and CKKS, for protecting IoT data during PPML analysis. Logistic regression models are implemented on encrypted datasets using the TenSEAL library, and the accuracy and efficiency are compared with unencrypted data. The study focuses on privacy preservation, computational overhead, and model accuracy. Using the capabilities of TenSEAL, the study finds that both BFV and CKKS effectively preserve IoT data privacy during ML tasks. BFV demonstrates superior computational efficiency, enabling faster analysis and training, while CKKS achieves higher accuracy in predicting IoT data. This study aids researchers in selecting the most suitable homomorphic encryption scheme for their needs.

Low-complexity recursive constrained maximum Versoria criterion adaptive filtering algorithm

Linearly-constrained adaptive filtering algorithms have emerged as promising candidates for system estimation. The existing methods such as the constrained least mean square algorithm rely on mean square error based learning, which delivers suboptimal performance under non-Gaussian noise environments. Therefore, the recursive constrained maximum Versoria criterion (RCMVC) algorithm has been derived and is robust against impulsive distortions. Nonetheless, RCMVC suffers from a notable computational overhead stemming from matrix inversion operations. To circumvent this issue, utilizing the weighting method and the dichotomous coordinate descent (DCD) iteration method, this paper derives a low-complexity version of the RCMVC algorithm called DCD-RCMVC, which alleviates the requirement of matrix inversion and enhances the estimation accuracy and robustness against non-Gaussian interference. Furthermore, we also present a comprehensive theoretical analysis of the DCD-RCMVC algorithm, encompassing discussions on its equivalence, convergence properties, and computational complexity. Simulations performed for system identification problems indicate that the DCD-RCMVC algorithm outperforms the existing state-of-art approaches.

Secure and Efficient Authentication using Linkage for permissionless Bitcoin network

The cryptocurrency’s permissionless and large-scale broadcasting requirements prohibit the traditional authentication implementation on the blockchain’s underlying peer-to-peer (P2P) networking. Thus, blockchain networking implementations remain vulnerable to networking integrity threats such as spoofing or hijacking. We design Secure and Efficient Authentication using Linkage (SEAL) to build connection security for permissionless Bitcoin networking. SEAL uses the linkage between the packets for a symmetric operation, in contrast to the traditional authentication approach relying on identity-credential-based trust. To make it appropriate for cryptocurrency networking, SEAL utilizes the packet header, protects the end-to-end connection, and separates the online process and the offline process so that the real-time overhead is minimal for greater efficiency and practicality. We implement SEAL on a functioning Bitcoin node and demonstrate that SEAL operates efficiently with minimal overhead. Specifically, it reduces the hash rate by only 1.3% compared to an unsecured node. Additionally, we use a network simulator to emulate the Bitcoin Mainnet and analyze SEAL’s impact on block propagation delay. SEAL yields 2.04 times smaller delay and 1.25 times smaller delay in block propagation than HMAC and ChaCha20-Poly1305, respectively. The key advantage of SEAL is that it requires fewer hash computations and simpler mixing operations, resulting in significantly lower computational overhead compared to traditional authentication schemes based on message authentication codes (MACs).

Dynamic Condensation for Efficient Band-Structure Calculations of 2D Periodic Structures

Efficient band-structure calculations are essential for understanding the mechanical behaviors of periodic materials, with significant implications in material design and phononic engineering. This paper introduces the application of the improved reduced system (IRS) technique to expedite elastic band-structure calculations. The IRS, a dynamic condensation method, partitions the unit cell degrees of freedom (DOFs) into primary and secondary sets. A strategic selection of primary DOFs retains a subset of interior DOFs alongside all exterior DOFs while truncating the remaining interior DOFs. The integration of IRS with the Craig–Bampton method for additional reduction and the imposition of Bloch boundary conditions yields a notable decrease in computational overhead. Additionally, for structures with a high number of interior DOFs, a substructuring scheme can be implemented to further enhance efficiency. This approach offers a compelling combination of accuracy and expedited computation, making it applicable across diverse periodic materials.

CLOUD DATA PROTECTION USING WEIBULL DISTRIBUTED RECURRENT NEURAL ERGODIC SIGNCRYPTION

Cloud computing has become an integral part of modern computing, offering scalable storage and processing resources. However, the security of data stored in the cloud remains a major concern, especially when dealing with sensitive information. Traditional encryption schemes, while effective, often face limitations in terms of computational overhead and vulnerability to advanced attacks. To address these challenges, we propose a novel Weibull Distributed Recurrent Neural Ergodic Skewed Certificateless Signcryption scheme aimed at enhancing data protection in cloud environments. The key problem addressed by this work is the inherent inefficiency of existing cryptographic solutions that either rely on certificate-based systems or suffer from high computational and communication costs. This is especially crucial in cloud systems where real-time data processing is essential. Our approach integrates Weibull distribution for key management and optimization, recurrent neural networks (RNNs) for secure data transmission prediction, and ergodic skewed signcryption to eliminate the need for certificate authorities. This results in improved security, reduced computational overhead, and efficient communication, ensuring that the data remains secure even in dynamic cloud environments. The proposed scheme was tested using various metrics, including encryption/decryption time, data throughput, and attack resistance. Results demonstrate a significant reduction in computational cost by approximately 28% compared to traditional certificateless encryption. Furthermore, encryption times decreased from an average of 1.8 ms to 1.2 ms, and the scheme showed robustness against man-in-the-middle and chosen-ciphertext attacks with a detection accuracy of 98.6%. These results confirm the efficacy of the proposed system for enhancing security in cloud computing environments while maintaining high performance.

NEURO DEEP FUZZY GENETIC ALGORITHM APPROACH FOR CLASSIFICATION AND DETECTION OF BRAIN TUMOR FROM LARGE DATASETS

Brain tumors represent one of the most critical and life-threatening diseases. Early detection and accurate classification are essential for effective treatment planning and survival rate improvement. With the exponential growth of medical data, particularly in imaging and diagnostic datasets, traditional algorithms face limitations in handling large-scale, high-dimensional data efficiently. Conventional machine learning and diagnostic methods often struggle with classification accuracy, computational complexity, and overfitting in large, noisy datasets. Addressing these issues is crucial for the development of robust diagnostic tools capable of handling real-world clinical data. This paper presents a novel hybrid approach combining Neural Networks, Deep Learning, Fuzzy Logic, and Genetic Algorithms, termed Neuro Deep Fuzzy Genetic Algorithm (NDFGA), for the classification and detection of brain tumors from large datasets. Neural Networks and Deep Learning architectures are leveraged for feature extraction and hierarchical learning. Fuzzy Logic improves interpretability and manages uncertainty in medical data, while Genetic Algorithms optimize feature selection and model parameters. This hybrid method is designed to maximize classification accuracy while minimizing false positives and computational overhead. The proposed approach was tested on a large brain tumor dataset comprising over 10,000 MRI scans. The NDFGA approach demonstrated superior performance compared to standalone methods. It achieved a classification accuracy of 97.8%, a sensitivity of 96.5%, and a specificity of 98.1%. The model also showed improved robustness in handling large datasets, reducing false positives by 12% and computational time by 15% compared to traditional methods. This hybrid model presents a scalable and efficient solution for brain tumor detection, especially in clinical environments.

EPAKA: An efficient and privacy-preserving authenticated key agreement scheme based on physical security for VANET

Vehicular ad hoc network (VANET) has been a promising technology in smart transportation system, which can enable information exchange between vehicles and roadside units (RSUs). However, the privacy of vehicles and RSUs is a critical challenge in VANET, as they may expose sensitive information to malicious attackers or unauthorized parties. Many existing authenticated key agreement (AKA) schemes aim to protect the privacy of vehicles and RSUs, but they often neglect the physical security of the devices involved in the communication. Therefore, we propose an efficient and privacy-preserving AKA scheme in VANET, which embeds physical unclonable function (PUF) and fuzzy extraction (FE) technology. PUF is a physical device that generates random strings based on their intrinsic characteristics and external inputs, which can protect the secrets in the devices from being stolen by attackers. FE can compensate for the drawbacks of PUF affected by environmental factors. Our scheme preserves the identity privacy of legitimate RSUs and vehicles, as well as intercepts and traces the identity of malicious attackers. In addition, we eliminate the involvement of the third party (TP) in the AKA phase to better meet the high-speed driving of vehicles. Finally, we conduct formal and informal security analyses in random oracle model (ROM), which prove that our scheme can resist various attacks. We also show in the performance analysis that our scheme has the lowest computational cost, communication overhead, and total energy consumption.

Reducing semantic ambiguity in domain adaptive semantic segmentation via probabilistic prototypical pixel contrast

Domain adaptation aims to reduce the model degradation on the target domain caused by the domain shift between the source and target domains. Although encouraging performance has been achieved by combining contrastive learning with the self-training paradigm, they suffer from ambiguous scenarios caused by scale, illumination, or overlapping when deploying deterministic embedding. To address these issues, we propose probabilistic prototypical pixel contrast (PPPC), a universal adaptation framework that models each pixel embedding as a probability via multivariate Gaussian distribution to fully exploit the uncertainty within them, eventually improving the representation quality of the model. In addition, we derive prototypes from probability estimation posterior probability estimation which helps to push the decision boundary away from the ambiguity points. Moreover, we employ an efficient method to compute similarity between distributions, eliminating the need for sampling and reparameterization, thereby significantly reducing computational overhead. Further, we dynamically select the ambiguous crops at the image level to enlarge the number of boundary points involved in contrastive learning, which benefits the establishment of precise distributions for each category. Extensive experimentation demonstrates that PPPC not only helps to address ambiguity at the pixel level, yielding discriminative representations but also achieves significant improvements in both synthetic-to-real and day-to-night adaptation tasks. It surpasses the previous state-of-the-art (SOTA) by +5.2% mIoU in the most challenging daytime-to-nighttime adaptation scenario, exhibiting stronger generalization on other unseen datasets. The code and models are available at https://github.com/DarlingInTheSV/Probabilistic-Prototypical-Pixel-Contrast.

Provenance-based APT campaigns detection via masked graph representation learning

Advanced Persistent Threats (APTs) are well-planned, persistent, and highly stealthy cyberattacks designed to steal confidential information or disrupt specific target systems. Recent studies have used system audit logs to construct provenance graphs that describe system interactions to detect potentially malicious activities. Although they are effective, they still suffer from problems such as the need for a priori knowledge, lack of attack data, and high computational overhead that limit their application. In this paper, we propose a self-supervised learning-based APT detection model, APT-MGL, which learns the embedded representations of nodes through a graph mask self-encoder and transforms the detection problem into an outlier detection problem for malicious nodes. APT-MGL characterizes the behavior of nodes based on node type, action, and interaction frequency, and fuses the features through a multi-head self-attention mechanism. Then the node embedding is obtained by combining graph features and structural information using masked graph representation learning. Finally, the unsupervised outlier detection method is used to analyze the computed embeddings and obtain the final detection results. The experimental results show that APT-MGL outperforms existing monitoring models and achieves a small overhead.