International Journal of Intelligent Systems

In response to the limitations of traditional fuzzing approaches that rely on static mutators and fail to dynamically adjust their test case mutations for deeper testing, resulting in the inability to generate targeted inputs to trigger vulnerabilities, this paper proposes a directed fuzzing methodology termed DocFuzz, which is predicated on a feedback mechanism mutator. Initially, a sanitizer is used to target the source code of the tested program and stake in code blocks that may have vulnerabilities. After this, a taint tracking module is used to associate the target code block with the bytes in the test case, forming a high-value byte set. Then, the reinforcement learning mutator of DocFuzz is used to mutate the high-value byte set, generating well-structured inputs that can cover the target code blocks. Finally, utilizing the feedback mechanism of DocFuzz, when the reinforcement learning mutator converges and ceases to optimize, the fuzzer is rebooted to continue mutating toward directions that are more likely to trigger vulnerabilities. Comparative experiments are conducted on multiple test sets, including LAVA-M, and the experimental results demonstrate that the proposed DocFuzz methodology surpasses other fuzzing techniques, offering a more precise, rapid, and effective means of detecting vulnerabilities in source code.

With the wide application of deep learning (DL) across various fields, deep joint source–channel coding (DeepJSCC) schemes have emerged as a new coding approach for image transmission. Compared with traditional separated source and CC (SSCC) schemes, DeepJSCC is more robust to the channel environment. To address the limited sensing capability of individual devices, distributed cooperative transmission is implemented among edge devices. However, this approach significantly increases communication overhead. In addition, existing distributed DeepJSCC schemes primarily focus on specific tasks, such as classification or data recovery. In this paper, we explore the wireless semantic image collaborative nonorthogonal transmission for distributed edge networks, where edge devices distributed across the network extract features of the same target image from different viewpoints and transmit these features to an edge server. A two-view distributed cooperative DeepJSCC (two-view‐DC-DeepJSCC) with or without information disentanglement scheme is proposed. In particular, the two-view‐DC-DeepJSCC with information disentanglement (two-view‐DC-DeepJSCC-D) is proposed for achieving balancing performance between multitasking of image semantic communication; while the two-view‐DC-DeepJSCC without information disentanglement only pursues outstanding data recovery performance. Through curriculum learning (CL), the proposed two-view‐DC-DeepJSCC-D effectively captures both common and private information from two-view data. The edge server uses the received information to accomplish tasks such as image recovery, classification, and clustering. The experimental results demonstrate that our proposed two-view‐DC-DeepJSCC-D scheme is capable of simultaneously performing image recovery, classification, and clustering tasks. In addition, the proposed two-view‐DC-DeepJSCC has better recovery performance compared to the existing schemes, while the proposed two-view‐DC-DeepJSCC-D not only maintains a competitive advantage in image recovery but also has a significant improvement in classification and clustering accuracy. However, the proposed two-view‐DC-DeepJSCC-D will sacrifice some image recovery performance to balance multiple tasks. Furthermore, two-view‐DC-DeepJSCC-D exhibits stronger robustness across various signal-to-noise ratios.

This paper addressed the trade-off between timeliness and reliability in joint communication and over-the-air computation offloading (JCACO) system under short-packet communications (SPCs). The inevitable decoding errors introduced by SPC lead to errors in the data aggregation process of over-the-air computation (AirComp). Due to limited resources, pursuing high reliability may prevent the JCACO system from meeting delay requirements, resulting in a trade-off between reliability and timeliness. To address this issue, this paper investigates the timeliness and reliability of the JCACO system. Specifically, the moment generating function method is used to derive the delay outage probability (DOP) of the JCACO system, and the outage probability of AirComp is calculated based on the errors that occur during its data aggregation process. The paper established an asymptotic relationship between blocklength, DOP, and AirComp outage probability (AOP). To balance timeliness and reliability, an AOP minimization problem is formulated under constraints of delay, queue stability, and limited resources based on computation offloading strategies and beamformer design. To overcome the issues of slow convergence and susceptibility to local optima in traditional algorithms, this paper proposed a stochastic successive mean-field game (SS-MFG) algorithm. This algorithm utilizes stochastic continuous convex approximation methods to leverage Nash equilibria among different users, achieving faster convergence to the global optimal solution. Numerical results indicate that SS-MFG reduces AOP by up to 60%, offering up to a 20% improvement in optimization performance compared to other algorithms while also converging faster.

The expanding importance of technology, particularly mobile banking, in the financial industry, is examined in this literature review, as well as the crucial role that cybersecurity knowledge plays in protecting online transactions. Users now have the flexibility to conduct payments whenever and wherever they wish thanks to the advent of mobile banking. Further consumer behavior study is necessary due to difficulties with its acceptability. Given the hazards involved in online and mobile banking, cybersecurity is revealed as a critical component. Users’ actions might lead to financial losses since they represent security concerns. The evaluation places a strong emphasis on the necessity of increasing user cybersecurity knowledge and comprehension. Wireless banking is still in its early phases and needs more study on consumer acceptability and behavior despite the greater accessibility of technology. Furthermore, the research study emphasizes the socio-technical difficulties governments encounter in tackling cybersecurity and emphasizes how urgently better readiness is needed in the face of cyberwarfare threats. It investigated how user behavior in mobile banking in particular geographic areas, such as Thailand, relates to cyberspace knowledge and consciousness. The assessment emphasizes the value of technology in banking, the difficulties associated with cybersecurity, and the demand for increased customer knowledge and comprehension to ensure safe digital transactions. To conduct this research activity, standardized questionnaires are used. The technique employed to get this data was convenience sampling. The statistics collection size stood at 500 and was gathered from males as well as females of all ages, belonging to diverse revenue groups, and numerous professional backgrounds. The survey finds that even while these services are becoming widespread in the UAE, customers’ awareness and understanding of cyber security are still insufficient. Users frequently underrate the security dangers involved with online transactions, which might create openings. Additionally, the study underlines the necessity of more effective training programs and efforts to raise mobile banking consumers’ knowledge of cybersecurity issues. It also emphasizes how crucial it is to build cybersecurity precautions into the structure and functioning of services for mobile banking. The purpose of this study was to examine the demographic characteristics of consumers and companies (online transactions) that use mobile banking apps to get special advantages. It was shown that people’s satisfaction with mobile banking applications was influenced by a number of criteria, including age, employment, income, marital status, and educational attainment. Younger consumers, such as students and recent graduates, are seen to be happy than customers of various ages and vocations, and males are thought to be happier than women.

Breast cancer is ranked as the second most common cancer among women globally, highlighting the critical need for precise and early detection methods. Our research introduces a novel approach for classifying benign and malignant breast ultrasound images. We leverage advanced deep learning methodologies, mainly focusing on the vision transformer (ViT) model. Our method distinctively features progressive fne-tuning, a tailored process that incrementally adapts the model to the nuances of breast tissue classifcation. Ultrasound imaging was chosen for its distinct benefts in medical diagnostics. Tis modality is noninvasive and cost-efective and demonstrates enhanced specifcity, especially in dense breast tissues where traditional methods may struggle. Such characteristics make it an ideal choice for the sensitive task of breast cancer detection. Our extensive experiments utilized the breast ultrasound images dataset, comprising 780 images of both benign and malignant breast tissues. Te dataset underwent a comprehensive analysis using several pretrained deep learning models, including VGG16, VGG19, DenseNet121, Inception, ResNet152V2, DenseNet169, DenseNet201, and the ViT. Te results presented were achieved without employing data augmentation techniques. Te ViT model demonstrated robust accuracy and generalization capabilities with the original dataset size, which consisted of 637 images. Each model's performance was meticulously evaluated through a robust 10-fold cross-validation technique, ensuring a thorough and unbiased comparison. Our fndings are signifcant, demonstrating that the progressive fne-tuning substantially enhances the ViT model's capability. Tis resulted in a remarkable accuracy of 94.49% and an AUC score of 0.921, signifcantly higher than models without fne-tuning. Tese results afrm the efcacy of the ViT model and highlight the transformative potential of integrating progressive fne-tuning with transformer models in medical image classifcation tasks. Te study solidifes the role of such advanced methodologies in improving early breast cancer detection and diagnosis, especially when coupled with the unique advantages of ultrasound imaging.

Medical images play a significant part in biomedical diagnosis, but they have a significant feature. The medical images, influenced by factors such as imaging equipment limitations, local volume effect, and others, inevitably exhibit issues like noise, blurred edges, and inconsistent signal strength. These imperfections pose significant challenges and create obstacles for doctors during their diagnostic processes. To address these issues, we present a pathology image segmentation technique based on the multiscale dual attention mechanism (MSDAUnet), which consists of three primary components. Firstly, an image denoising and enhancement module is constructed by using dynamic residual attention and color histogram to remove image noise and improve image clarity. Then, we propose a dual attention module (DAM), which extracts messages from both channel and spatial dimensions, obtains key features, and makes the edge of the lesion area clearer. Finally, capturing multiscale information in the process of image segmentation addresses the issue of uneven signal strength to a certain extent. Each module is combined for automatic pathological image segmentation. Compared with the traditional and typical U-Net model, MSDAUnet has a better segmentation performance. On the dataset provided by the Research Center for Artificial Intelligence of Monash University, the IOU index is as high as 72.7%, which is nearly 7% higher than that of U-Net, and the DSC index is 84.9%, which is also about 7% higher than that of U-Net.

The unique fingerprints of radio frequency (RF) devices play a critical role in enhancing wireless security, optimizing spectrum management, and facilitating device authentication through accurate identification. However, high-accuracy identification models for radio frequency fingerprint (RFF) often come with a significant number of parameters and complexity, making them less practical for real-world deployment. To address this challenge, our research presents a deep convolutional neural network (CNN)–based architecture known as the separation and fusion convolutional neural network (SFCNN). This architecture focuses on enhancing the identification accuracy of RF devices with limited complexity. The SFCNN incorporates two customizable modules: the separation layer, which is responsible for partitioning the data group size adapted to the channel dimension to keep the low complexity, and the fusion layer which is designed to perform deep channel fusion to enhance feature representation. The proposed SFCNN demonstrates improved accuracy and enhanced robustness with fewer parameters compared to the state-of-the-art techniques, including the baseline CNN, Inception, ResNet, TCN, MSCNN, STFT-CNN, and the ResNet-50-1D. The experimental results based on the public datasets demonstrate an average identification accuracy of 97.78% among 21 USRP transmitters. The number of parameters is reduced by at least 8% compared with all the other models, and the identification accuracy is improved among all the models under any considered scenarios. The trade-off performance between the complexity and accuracy of the proposed SFCNN suggests that it is an effective architecture with remarkable development potential.

Federated learning (FL) is a novel approach to privacy-preserving machine learning, enabling remote devices to collaborate on model training without exchanging data among clients. However, it faces several challenges, including limited client-side processing capabilities and non-IID data distributions. To address these challenges, we propose a partitioned FL architecture that a large CNN is divided into smaller networks, which train concurrently with other clients. Within a cluster, multiple clients concurrently train the ensemble model. The Jensen–Shannon divergence quantifies the similarity of predictions across submodels. To address discrepancies in model parameters between local and global models caused by data distribution, we propose an ensemble learning method that integrates a penalty term into the local model’s loss calculation, thereby ensuring synchronization. This method amalgamates predictions and losses across multiple submodels, effectively mitigating accuracy loss during the integration process. Extensive experiments with various Dirichlet parameters demonstrate that our system achieves accelerated convergence and enhanced performance on the CIFAR-10 and CIFAR-100 image classification tasks while remaining robust to partial participation, diverse datasets, and numerous clients. On the CIFAR-10 dataset, our method outperforms FedAvg, FedProx, and SplitFed by 6%–8%; in contrast, it outperforms them by 12%–18% on CIFAR-100.

Counterfactual regret minimization (CFR) is an effective algorithm for solving extensive-form games with imperfect information (IIEGs). However, CFR is only allowed to be applied in known environments, where the transition function of the chance player and the reward function of the terminal node in IIEGs are known. In uncertain situations, such as reinforcement learning (RL) problems, CFR is not applicable. Thus, applying CFR in unknown environments is a significant challenge that can also address some difficulties in the real world. Currently, advanced solutions require more interactions with the environment and are limited by large single-sampling variances to narrow the gap with the real environment. In this paper, we propose a method that combines CFR with information gain to compute the Nash equilibrium (NE) of IIEGs with unknown environments. We use a curiosity-driven approach to explore unknown environments and minimize the discrepancy between uncertain and real environments. In addition, by incorporating information into the reward, the average strategy calculated by CFR can be directly implemented as the interaction policy with the environment, thereby improving the exploration efficiency of our method in uncertain environments. Through experiments on standard testbeds such as Kuhn poker and Leduc poker, our method significantly reduces the number of interactions with the environment compared to the different baselines and computes a more accurate approximate NE within the same number of interaction rounds.

Eye tracking has emerged as a valuable tool for both research and clinical applications. However, traditional eye-tracking systems are often bulky and expensive, limiting their widespread adoption in various fields. Smartphone eye tracking has become feasible with advanced deep learning and edge computing technologies. However, the field still faces practical challenges related to large-scale datasets, model inference speed, and gaze estimation accuracy. The present study created a new dataset that contains over 3.2 million face images collected with recent phone models and presents a comprehensive smartphone eye-tracking pipeline comprising a deep neural network framework (MGazeNet), a personalized model calibration method, and a heuristic gaze signal filter. The MGazeNet model introduced a linear adaptive batch normalization module to efficiently combine eye and face features, achieving the state-of-the-art gaze estimation accuracy of 1.59 cm on the GazeCapture dataset and 1.48 cm on our custom dataset. In addition, an algorithm that utilizes multiverse optimization to optimize the hyperparameters of support vector regression (MVO–SVR) was proposed to improve eye-tracking calibration accuracy with 13 or fewer ground-truth gaze points, further improving gaze estimation accuracy to 0.89 cm. This integrated approach allows for eye tracking with accuracy comparable to that of research-grade eye trackers, offering new application possibilities for smartphone eye tracking.

Collaborative edge and cloud computing is a promising computing paradigm for reducing the task response delay and energy consumption of devices. In this paper, we aim to jointly optimize task offloading strategy, power control for devices, and resource allocation for edge servers within a collaborative device-edge-cloud computing system. We formulate this problem as a constrained multiobjective optimization problem and propose a joint optimization algorithm (JO-DEC) based on a multiobjective evolutionary algorithm to solve it. To address the tight coupling of the variables and the high-dimensional decision space, we propose a decoupling encoding strategy (DES) and a boundary point sampling strategy (BPS) to improve the performance of the algorithm. The DES is utilized to decouple the correlations among decision variables, and BPS is employed to enhance the convergence speed and population diversity of the algorithm. Simulation results demonstrate that JO-DEC outperforms three state-of-the-art algorithms in terms of convergence and diversity, enabling it to achieve a smaller task response delay and lower energy consumption.

Application programming interface (API) misuse refers to misconceptions or carelessness in the anticipated usage of APIs, threatening the software system’s security. Moreover, API misuses demonstrate significant concealment and are challenging to uncover. Recent advancements have explored enhanced LLMs in a variety of software engineering (SE) activities, such as code repair. Nonetheless, the security implications of using LLMs for these purposes remain underexplored, particularly concerning the issue of API misuse. In this paper, we present an empirical study to observe the bug-fixing capabilities of LLMs in addressing API misuse related to monitoring resource management (MRM API misuse). Initially, we propose APImisRepair, a real-world benchmark for repairing MRM API misuse, including buggy programs, corresponding fixed programs, and descriptions of API misuse. Subsequently, we assess the performance of several LLMs using the APImisRepair benchmark. Findings reveal the vulnerabilities of LLMs in repairing MRM API misuse and find several reasons, encompassing factors such as fault localization and a lack of awareness regarding API misuse. Additionally, we have insights on improving LLMs in terms of their ability to fix MRM API misuse and introduce a crafted approach, APImisAP. Experimental results demonstrate that APImisAP exhibits a certain degree of improvement in the security of LLMs.

Federated learning (FL) is a machine learning technique in which a large number of clients collaborate to train models without sharing private data. However, FL’s integrity is vulnerable to unreliable models; for instance, data poisoning attacks can compromise the system. In addition, system preferences and resource disparities preclude fair participation by reliable clients. To address this challenge, we propose a novel client selection strategy that introduces a security-fairness value to measure client performance in FL. The value in question is a composite metric that combines a security score and a fairness score. The former is dynamically calculated from a beta distribution reflecting past performance, while the latter considers the client’s participation frequency in the aggregation process. The weighting strategy based on the deep deterministic policy gradient (DDPG) determines these scores. Experimental results confirm that our method fairly effectively selects reliable clients and maintains the security and fairness of the FL system.

The conventional K-Means clustering algorithm is widely used for grouping similar data points by initially selecting random centroids. However, the accuracy of clustering results is significantly influenced by the initial centroid selection. Despite different approaches, including various K-Means versions, suboptimal outcomes persist due to inadequate initial centroid choices and reliance on common normalization techniques like min-max normalization. In this study, we propose an improved algorithm that selects initial centroids more effectively by utilizing a novel formula to differentiate between instance attributes, creating a single weight for differentiation. We introduce a preprocessing phase for dataset normalization without forcing values into a specific range, yielding significantly improved results compared to unnormalized datasets and those normalized using min-max techniques. For our experiments, we used five real datasets and five simulated datasets. The proposed algorithm is evaluated using various metrics and an external benchmark measure, such as the Adjusted Rand Index (ARI), and compared with the traditional K-Means algorithm and 11 other modified K-Means algorithms. Experimental evaluations on these datasets demonstrate the superiority of our proposed methodologies, achieving an impressive average accuracy rate of up to 95.47% and an average ARI score of 0.95. Additionally, the number of iterations required is reduced compared to the conventional K-Means algorithm. By introducing innovative techniques, this research provides significant contributions to the field of data clustering, particularly in addressing modern data-driven clustering challenges.

Age-related macular degeneration (AMD) diagnosis using fundus images is one of the critical missions of the eye-care screening program in many countries. Various proposed deep learning models have been studied for this research interest, which aim to achieve the mission and outperform human-based approaches. However, research efforts are still required for the improvement of model classification accuracy, sensitivity, and specificity values. In this study, we proposed the model named as ViT-AMD, which is based on the latest Vision Transformer (ViT) structure, to diagnosis a fundus image as normal, dry AMD, or wet AMD types. Unlike convolution neural network models, ViT consists of the attention map layers, which show more effective performance for image classification task. Our training process is based on the 5-fold cross-validation and transfer learning techniques using Chula-AMD dataset at the Department of Ophthalmology, the King Chulalongkorn Memorial Hospital, Bangkok. Furthermore, we also test the performance of trained model using an independent image datasets. The results showed that for the 3-classes AMD classification (normal vs. dry AMD vs. wet AMD) on the Chula-AMD dataset, the averaged accuracy, precision, sensitivity, and specificity of our trained model are about 93.40%, 92.15%, 91.27%, and 96.57%, respectively. For result testing on independent datasets, the averaged accuracy, precision, sensitivity, and specificity of trained model are about 74,20%, 75.35%, 74.13%, and 87.07%, respectively. Compared with the results from the baseline CNN-based model (DenseNet201), the trained ViT-AMD model has outperformed significantly. In conclusion, the ViT-AMD model have proved their usefulness to assist the ophthalmologist to diagnosis the AMD disease.

The networked control systems (NCSs) under cyberattacks have received much attention in both industrial and academic fields, with rare attention on the delayed networked control systems (DNCSs). In order to well address the control problem of DNCSs, in this study, we consider the resilient event-triggered safety control problem of the NCSs with time-varying delays based on the switched observer subject to aperiodic denial-of-service (DoS) attacks. The observer-based switched event-triggered control (ETC) strategy is devised to cope with the DNCSs under aperiodic cyberattacks for the first time so as to decrease the transmission of control input under limited network channel resources. A new piecewise Lyapunov functional is proposed to analyze and synthesize the DNCSs with exponential stability. The quantitative relationship among the attack activated/sleeping period, exponential decay rate, event-triggered parameters, sampling period, and maximum time-delay are explored. Finally, we use both a numerical example and a practical example of offshore platform to show the effectiveness of our results.

In most peer-to-peer (P2P) networks, peers are placed randomly or based on their geographical position, which can lead to a performance bottleneck. This problem can be solved by using peer clustering algorithms. In this paper, the significant results of the paper can be described in the following sentences. We propose two innovative swarm-based metaheuristics for peer clustering, slime mold and slime mold K-means. They are competitively benchmarked, evaluated, and compared to nine well-known conventional and swarm-based algorithms: artificial bee colony (ABC), ABC combined with K-means, ant-based clustering, ant K-means, fuzzy C-means, genetic K-means, hierarchical clustering, K-means, and particle swarm optimization (PSO). The benchmarks cover parameter sensitivity analysis and comparative analysis made by using 5 different metrics: execution time, Davies–Bouldin index (DBI), Dunn index (DI), silhouette coefficient (SC), and averaged dissimilarity coefficient (ADC). Furthermore, a statistical analysis is performed in order to validate the obtained results. Slime mold and slime mold K-means outperform all other swarm-inspired algorithms in terms of execution time and quality of the clustering solution.

Detecting deepfake media remains an ongoing challenge, particularly as forgery techniques rapidly evolve and become increasingly diverse. Existing face forgery detection models typically attempt to discriminate fake images by identifying either spatial artifacts (e.g., generative distortions and blending inconsistencies) or predominantly frequency-based artifacts (e.g., GAN fingerprints). However, a singular focus on a single type of forgery cue can lead to limited model performance. In this work, we propose a novel cross-domain approach that leverages a combination of both spatial and frequency-aware cues to enhance deepfake detection. First, we extract wavelet features using wavelet transformation and residual features using a specialized frequency domain filter. These complementary feature representations are then concatenated to obtain a composite frequency domain feature set. Furthermore, we introduce an adaptive feature fusion module that integrates the RGB color features of the image with the composite frequency domain features, resulting in a rich, multifaceted set of classification features. Extensive experiments conducted on benchmark deepfake detection datasets demonstrate the effectiveness of our method. Notably, the accuracy of our method on the challenging FF++ dataset is mostly above 98%, showcasing its strong performance in reliably identifying deepfake images across diverse forgery techniques.

Users’ online activities serve as a mirror, reflecting their unique personas, affiliations, interests, and hobbies within the real world. Network information dissemination is inherently targeted, as users actively seek information to facilitate precise and swift communication. Delving into the nuances of information propagation on the Internet holds immense potential for facilitating commercial endeavors such as targeted advertising, personalized product recommendations, and insightful consumer behavior analyses. Recognizing that the intensity of information transmission diminishes with the proliferation of competing messages, increased transmission distances, and the passage of time, this paper draws inspiration from the concept of heat attenuation to formulate an innovative information propagation model. This model simulates the “heat index” of each node in the transmission process, thereby capturing the dynamic nature of information flow. Extensive experiments, bolstered by comparative analyses of multiple datasets and relevant algorithms, validate the correctness, feasibility, and efficiency of our proposed algorithm. Notably, our approach demonstrates remarkable accuracy and stability, underscoring its potential for real-world applications.

The proliferation of photorealistic images synthesized by generative adversarial networks (GANs) has posed serious threats to society. Therefore a new challenge task, named image attribution, is arising to attribute fake images to a specific GAN. However, existing approaches focus on model-specific features but neglect the misguidance of semantic-relevant features in image attribution, which leads to a significant performance decrease in cross-dataset evaluation. To tackle the above problem, we propose a semantic-agnostic fake image attribution (SFIA) method, which effectively distinguishes fake images by disentangling the GANs fingerprint and semantic-relevant features in latent space. Specifically, we design a semantic eliminator based on residual block with skip connections that take images as input and outputs GAN fingerprint features. A classifier with an attention module for feature refinement is introduced to make the final decision. In addition, we develop a well-trained reconstructor and classifier which supervise the semantic eliminator to achieve semantic-agnostic feature extraction. Moreover, we propose an improved data augmentation combined with meta-learning to enhance the model’s generalization in detecting unseen image categories. Comprehensive experiments on various datasets, namely, CelebA, LSUN-church, and LSUN-bedroom, demonstrate the effectiveness of our proposed SFIA. It achieves over 95% accuracy on three datasets and exhibits superior performance in terms of generalization to unseen data.

Application-layer distributed denial of service (DDoS) attacks have become the main threat to Web server security. Because application-layer DDoS attacks have strong concealability and high authenticity, intrusion detection technologies that rely solely on judging client authenticity cannot accurately detect such attacks. In addition, application-layer DDoS attacks are periodic and repetitive, and attack targets suddenly in a short period. In this study, we propose an efficient application-layer DDoS detection system based on improved random forest. Firstly, the Web logs are preprocessed to extract the user session characteristics. Subsequently, we propose a Session Identification based on Separation and Aggregation (SISA) method to accurately capture user sessions. Lastly, we propose an improved random forest classification algorithm based on feature weighting to address the issue of an increasing number of features leading to prolonged calculation times in the random forest algorithm, and as the feature dimension increases, there might be instances where no subfeature is related to the category to be classified. More importantly, we compare the request source IP with the malicious IP in the threat intelligence library to deal with the periodicity and repetition of application-layer DDoS attacks. We conducted a comprehensive experiment on the publicly available Web log dataset and the threat intelligence database of the laboratory as well as the simulated generated attack log dataset in the laboratory environment. The experimental results show that the proposed detection system can control the false alarm rate and false alarm rate within a reasonable range, improving the detection efficiency further, the detection rate is 99.85%. In secondary attack detection experiments, our proposed detection method achieves a higher detection rate in a shorter time.

The National Disaster Management Center has declared a drought disaster in the Northern Cape, South Africa, due to persistent dry conditions that impact regions such as the Western, Eastern, and Northern Cape provinces. Accurate drought predictions are vital for decision-making and planning in vulnerable areas. This study introduces a hybrid intelligence model, combining long short-term memory (LSTM) and convolutional neural networks (CNNs), to forecast short-term meteorological droughts using the Standardized Precipitation Evapotranspiration Index (SPEI). Applied to Kimberley and Upington in the Northern Cape, the model predicts 1-month and 3-month SPEI indices (SPEI-1 and SPEI-3). The hybrid model’s performance, compared to benchmark models such as artificial neural networks (ANNs), LSTM, and CNN, is measured through statistical analysis. In Kimberley, the CNN–LSTM model displayed a robust positive correlation of 0.901573 and a low mean absolute error (MAE) of 0.082513. Similarly, in Upington, the CNN–LSTM model exhibited strong performance, achieving a correlation coefficient of 0.894805 and a MAE of 0.085212. These results highlight the model’s remarkable precision and effectiveness in predicting drought conditions in both regions, underscoring its superiority over other forecasting techniques. SPEI, incorporating potential evapotranspiration and rainfall, is superior for drought analysis amidst climate change. The findings enhance understanding of drought patterns and aid mitigation efforts. The CNN–LSTM hybrid model demonstrated noteworthy results, outperforming ANN, CNN, and LSTM, emphasizing its potential for precise meteorological drought predictions.

Through modeling the characteristics of wireless transmission channels, channel estimation can improve signal detection and demodulation techniques, enhance the spectrum utilization, optimize communication performance, and enhance the quality, reliability, and efficiency of intelligent wireless communication systems. In this paper, we propose a deep convolutional autoencoder–based channel estimation method in intelligent wireless communication systems. At first, the channel time-frequency response matrix between the transmitter and receiver can be represented as 2D images. Then they are fed into the convolutional autoencoder to learn key channel features. To reduce the structural complexity of the deep learning model and improve its inference efficiency, we adopt the method of removing redundant parameters to achieve model compression. Iterative training and pruning based on stochastic gradient descent (SGD) and weight importance evaluation are alternated to obtain a lightweight deep learning model for channel estimation. Finally, extensive simulation results have verified the effectiveness and superiority of the proposed method.

Anonymous network tracing is a significant research subject in the field of network security, and flow correlation technology serves as a fundamental technique for deanonymizing network traffic. Existing flow correlation techniques are considered ineffective and unreliable when applied on a large scale because they exhibit high false-positive rates or require impractically long periods of traffic observation to achieve reliable correlations. To address this issue, this paper proposed an innovative flow correlation approach for the typical and most widely used Tor anonymous network by combining graph convolutional neural networks with triplet networks. Our proposed method involves extracting features such as packet intervals, packet lengths, and directions from Tor network traffic and encoding each flow into a graph representation. The integration of triplet networks enhances the internode relationships, which can effectively fuse flow representations with node associations. The graph convolutional neural network extracts features from the input graph topology, mapping them to distinct representations in the embedding space, thus effectively distinguishing different Tor flows. Experimental results demonstrate that with a false-positive rate as low as 0.1%, the correlation accuracy reaches 86.4%, showcasing a 5.1% accuracy improvement compared to the existing state-of-the-art methods.

In the present era, where competition pervades across all domains, profitability holds crucial economic importance for numerous companies, including the banking industry. Offering the right products to customers is a fundamental problem that directly affects banks’ net revenue. Machine learning (ML) approaches can address this issue using customer behavior analysis from historical customer data. This study addresses the issue by processing customer transactions using a bank’s current account debt (CAD) product with state-of-the-art ML approaches. In the first step, exploratory data analysis (EDA) is performed to examine the data and detect patterns and anomalies. Then, different regression methods (tree-based methods) are tested to analyze the model’s performance. The obtained results show that the light gradient boosting machine (LGBM) algorithm outperforms other methods with an 84% accuracy rate in the light gradient boosting algorithm, which is the most accurate of the three methods used.