An Automatic Source Code Vulnerability Detection Approach Based on KELM

PurposeOne of the paramount challenges in the field of structural health monitoring (SHM) is the precise assessment of structural damage. Recently, many methods have been proposed to diagnose damage in structures using time series data. Nevertheless, these techniques are restricted in their ability to capture the bidirectional temporal and spatial relationships among the data. To remedy these shortcomings, this study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN.Design/methodology/approachThis study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN. The novelty of BiLSTM-1DCNN comes in its effective fusion of 1DCNN’s local feature extraction and BiLSTM’s capacity to capture long-term information from both directions (forward and backward).FindingsValidation using a dataset from the Nam O steel truss bridge demonstrates that the proposed method surpasses conventional models, including 1DCNN (87.9%), LSTM (72.5%), BiLSTM (76.1%) and LSTM-1DCNN (91.1%), achieving an accuracy rate of 96.4%. This approach provides a significant advancement in SHM, paving the way for more accurate and reliable damage detection.Research limitations/implicationsThe study’s limitations include potential overfitting due to the model’s complexity and the high computational resources required. The BiLSTM-1DCNN model’s performance is also influenced by the quality and quantity of the training data as well as the specific characteristics of the dataset. Additionally, while the model performs well in detecting damage in the Nam O steel truss bridge, its generalizability to other types of structures and environmental conditions remains untested.Practical implicationsThe BiLSTM-1DCNN model offers significant practical benefits for structural health monitoring by enhancing damage detection accuracy. Its ability to effectively capture both local features and long-term temporal dependencies makes it a valuable tool for real-time monitoring and early detection of structural issues. This can lead to more timely maintenance actions, potentially reducing repair costs and improving safety. The model’s high accuracy in detecting damage can also support infrastructure management decisions, contributing to better resource allocation and enhanced overall safety of critical structures.Social implicationsThe BiLSTM-1DCNN model’s advanced damage detection capabilities have positive social implications, such as improving the safety and reliability of public infrastructure like bridges. Enhanced structural health monitoring can reduce the risk of catastrophic failures, protecting lives and reducing accidents. By providing more accurate and timely assessments, the model can also lead to more efficient use of maintenance and repairs, ensuring that resources are allocated effectively.Originality/valueThe originality of the BiLSTM-1DCNN model lies in its innovative integration of one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks. This unique combination leverages 1DCNN’s strength in local feature extraction and BiLSTM’s capacity to capture temporal dependencies in both directions, addressing the limitations of existing methods. The model’s high accuracy in detecting structural damage, as demonstrated by its performance on the Nam O steel truss bridge dataset, highlights its value in advancing structural health monitoring technologies and offers a significant improvement over traditional approaches.

Nowadays this concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. In this paper, to increase the air traffic management accuracy and legitimacy we used the bidirectional long short-term memory (Bi-LSTMs) and extreme learning machines (ELM) to design the structure of a deep learning network method. The Kaggle data set and different performance parameters and statistical criteria have been used in MATLAB to validate the proposed method. Using the proposed method has improved the criteria factors of this study. The proposed method has had a % increase in air traffic management in comparison to other papers. Therefore, it can be said that the proposed method has a much higher air traffic management capacity in comparison to the previous methods.

Prediction of PM2.5 concentration in urban agglomeration of China by hybrid network model

The COVID-19 pandemic has triggered a global crisis in health and economic sectors, causing profound impacts on sea transport and trade. This research paper investigates the ramifications of the pandemic on maritime shipping prices, and, hence, their subsequent influence on the stocks of shipping companies. This global upheaval disrupted international trade significantly, resulting in an unprecedented demand surge for shipping services and a substantial spike in freight rates. This study is intended to propose a predictive method based on Multivariate Multi-step convolutional neural network - Bidirectional Long Short-Term Memory (Multivariate Multi-step CNNBiLSTM) networks in order to forecast the prices of three of the most prominent stocks of big organizations operating in maritime transport. The proposed method is composed of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), where the research utilizes CNN to help for feature extraction from the inputted data, alongside BiLSTM that forecasts the closing stock price for the upcoming five days, utilizing the extracted feature data. Hence, stock price prediction can be realized by applying a novel optimization strategy, which was founded on the Multivariate Multi-step CNN-BiLSTM model and utilizing the Adam optimizer. Prediction accuracy can be assessed by incorporating four metrics into the system: Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and Median Absolute Percentage Error. Experimental findings demonstrate that Multivariate Multi-step CNN-BiLSTM yields the most dependable stock price forecasts with the highest accuracy. The proposed prediction method, correctly applied, can yield economic benefits at the macr and micro levels; the prediction accuracy can help policy makers make better future outlook estimates in relation to inflation, gross domestic product (GDP), and unemployment levels that might be impacted by the volatile, uncontrolled, or unexpected fluctuations of stock prices of some leading economic sectors that are closely connected to global shipping and supply chain operations; thus, leading to serious impacts at the microeconomic level in relation to costs, supply and demand, and behavior of individual consumers and companies.

The developed research includes an Long Short-Term Memory (LSTM) network with IoT sensor data for the management of rice crop irrigation. Optimizing the amount of water required for rice crops is very critical, based on real-time predictions of the actual environmental and soil parameters including moisture, temperature, humidity, and rainfall as well as all other possible related factors. IoT sensors installed in agricultural fields gathered data, which was transmitted to a controller and then uploaded to the cloud for further processing. The training dataset contained 5,600 records. In this research, 70% was used for training and the rest 30% for testing. Three LSTM models were used for analyzing sensor data and predicting the need for irrigation and controlling the irrigation pump accordingly. These were Vanilla LSTM, Stacked LSTM, and Bidirectional LSTM. It was evaluated the performance of the models developed using precision, recall, F1 score, accuracy, and other performance measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Score. Bidirectional LSTM was determined to have the best performance, with MAE at 0.15, RMSE at 0.24, and an R2 score of 0.93, exhibiting high accuracy and predictive power. The Stacked LSTM and Vanilla LSTM also gave fair results, in terms of having R2 values of 0.89 and 0.84, respectively. The number of training and memory used for the computation were also given, with Vanilla LSTM taking least, followed by Stacked LSTM and Bidirectional LSTM. These results suggest that the LSTM networks can potentially be capable of improving the irrigation management such that more water is conserved and, subsequently, more suitable for agricultural productivity.

Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short-term memory (Bi-LSTM) and bidirectional convolutional long short-term memory (Bi-ConvLSTM) network to decompress and recover the CSI for single-user and multi-user, respectively. The proposed DL-based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in massive MIMO systems.

Cyber-physical system (CPS) security has become more important in the age of Industry 4.0 because of the quick integration of automation and the Internet of Things. The goal of this project is to create a strong intrusion detection and control system that can recognize and lessen security risks in CPS settings. The suggested approach makes use of deep learning (DL) and reinforcement learning (RL) techniques. To guarantee data consistency, pre-processing procedures such as mean-based imputation and min–max scaling come after data collection. ADASYN data augmentation is used to address class imbalance, while entropy analysis and statistical techniques are used to extract key features. The intrusion detection phase uses a combination of deep convolutional neural networks (DCNN) and bidirectional long short-term memory (BI-LSTM) networks to capture both spatial and temporal relationships in the data, while a hybrid feature selection technique improves the model’s performance. A deep Q-network (DQN) handles attack mitigation and uses reinforcement learning to adjust to new threats. Detecting attack patterns with high sensitivity (0.984), specificity (0.983), and accuracy (0.991626) for dataset 1, the accuracy of dataset 2 is 0.985 for 70% of training and 0.988 for 80% of training, and the Proposed-DBID-Net architecture enhances CPS security in Industry 4.0. The evaluation phase emphasizes how crucial feature selection is to maximize the model’s accuracy. In conclusion, this study offers a thorough and flexible method for protecting CPS in Industry 4.0, guaranteeing accuracy and scalability across changing cyber threats.

In cybersecurity, the rise of fileless malware poses a significant challenge to endpoint security. Traditional detection methods often fail against these sophisticated attacks, necessitating advanced techniques like deep learning models. This study highlights the limitations of Bi-Directional Long Short-Term Memory (BLSTM) models in dynamic malware analysis and proposes enhancements through Convolutional Long Short-Term Memory (ConvLSTM) architecture. BLSTM models process input sequences in forward and backward directions, combining the results into one output. While this dual-layer approach improves analysis, it is time-consuming, potentially increasing the risk of fileless malware attacks. A key limitation of BLSTM is the lack of parameter sharing between forward and backward directions. This reduces its ability to capture spatial and temporal features simultaneously, hindering effectiveness in detecting fileless malware. To address this, the ConvLSTM model consolidates feature extraction within a single LSTM cell layer. ConvLSTM breaks down samples into subsequence and uses timesteps for additional feature extraction, enabling spatial-temporal data analysis and improving malware prediction accuracy. The model was tested using a dynamic malware dataset. Unlike traditional LSTM, ConvLSTM integrates convolutional layers, allowing parameter sharing across both spatial and temporal dimensions. This reduces computational complexity and improves model performance in handling multidimensional data. The research re-simulated prior work with BLSTM using the same malware dataset. The Spyder app ran the event simulator and the ConvLSTM model's results replaced BLSTM's using identical parameters. Time, accuracy and loss were the main performance metrics. ConvLSTM outperformed BLSTM, achieving 98% detection accuracy compared to BLSTM's 90%. It also significantly reduced processing time, averaging 10 seconds, while BLSTM took 22 seconds. ConvLSTM experienced lower losses, averaging 10% per epoch versus BLSTM's 20%. In conclusion, ConvLSTM offers superior performance over BLSTM in fileless malware detection. Its enhanced computational efficiency and ability to quickly mitigate threats make it a robust solution for fortifying endpoint security against evolving cyber threats. ConvLSTM holds potential in strengthening defence mechanisms against sophisticated malware attacks, providing a proactive approach to safeguarding networks and data.

The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production, and it has become a challenging task to enhance the accuracy of coal burst disaster prediction. To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction, this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory (Bi-LSTM) network. The method involves three main modules, including microseismic spatio-temporal characteristic indicators construction, temporal prediction model, and spatial prediction model. To validate the effectiveness of the proposed method, engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia, focusing on 13 high-energy microseismic events with energy levels greater than 105 J. In terms of temporal prediction, the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions, and there is no false alarm detected throughout the entire testing period. Moreover, compared to the traditional threshold-based coal burst temporal prediction method, the accuracy of the proposed method is increased by 38.5%. In terms of spatial prediction, the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions, 3 medium hazard predictions, and 4 weak hazard predictions.

Nowadays, most smartphones feature a number of strong sensors, such as direction, network, location, and motion sensors. Motion and inertial sensors (i.e., accelerometer, gyroscope, etc.) are particularly popular in human activity recognition (HAR) for detecting human physical activity which helps in many research areas of the Internet of healthcare things (IoHT) including several patient’s diseases such as Parkinson, obesity, cardiovascular, and diabetes. Deep learning (DL) techniques have been increasingly popular over the years as a result of their significant outcomes. The use of deep learning techniques to recognize human physical activity in wearable and mobile sensor situations has also gotten a lot of attention from around the world. In this paper, a deep neural network (DNN) combining bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) is proposed. The performance of the model has been evaluated on two publicly available datasets: WISDM and UCI-HAR. The model has achieved 97.96 and 97.15% accuracy for the WISDM and UCI-HAR, respectively. Moreover, the simulation results show the effectiveness of the proposed work compared to other state-of-the-art methods.KeywordsHuman activity recognitionDeep learningIoHTConvolutional neural networks (CNN)Bidirectional long short-term memory networks (Bi-LSTM)

In the context of informatized combat, fast and accurate identification of the target’s tactical intentions is a crucial prerequisite for seizing superiority and winning the war. Traditional air target intention recognition methods rely on a large amount of prior knowledge and struggle to effectively capture the characteristic information of time-series data, which fails to meet the objectivity and accuracy requirements of modern battlefield decision-making. Considering that tactical maneuvers are the flight actions taken by target aircraft to achieve tactical intentions, the identification of maneuver types can provide important reference information for predicting tactical intentions. In this paper, an air target tactical intention recognition method combined with maneuver identification is proposed. The motion characteristics of the target are analyzed on the basis of a kinematic knowledge model to identify its maneuver motion. The identified maneuver types, as secondary features of the target’s motion state, are jointly modeled with the selected tactical intention features in a temporal network based on the Bidirectional Long Short-Term Memory (BiLSTM) networks to achieve intention classification. The experimental results demonstrate that the recognition accuracy of the tactical intention inference model combined with maneuver identification can reach 95.76%, which outperforms other recent intention recognition methods. The visualized results using the t-distributed stochastic neighbor embedding technology satisfy certain interpretability requirements. The proposed method effectively improves the recognition capability of air target tactical intention, which is of great significance for efficient battlefield situation analysis and optimized decision-making.

Current temperature prediction methods often focus on time-series information while neglecting the contributions of different meteorological factors and the context of varying time steps. Accordingly, this study developed a Dual-Attention-BiLSTM (a bidirectional long short-term memory network with dual attention mechanisms) network model, which integrates a bidirectional long short-term memory (BiLSTM) network model with random forest-based feature selection and two self-designed attention mechanisms. A sensitivity analysis was conducted to evaluate the influence of the attention mechanisms. This study focuses on Shijiazhuang City, China, which has a temperate continental monsoon climate with significant seasonal and daily variations. The data were sourced from ERA5-Land, comprising hourly near-surface temperature and related meteorological variables for the year of 2022. The results indicate that integrating the two attention mechanisms significantly improves the model’s prediction performance compared to using BiLSTM alone. The mean absolute error between simulation results ranges from 0.80 °C to 1.08 °C, with a reduction of 0.17 °C to 0.39 °C, and the root mean square error ranges from 1.17 °C to 1.37 °C, with a reduction of 0.12 °C to 0.22 °C.

In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).

Herein, to accurately predict tool wear, we proposed a new deep learning network—that is, the IE-Bi-LSTM—based on an informer encoder and bi-directional long short-term memory. The IE-Bi-LSTM uses the encoder part of the informer model to capture connections globally and to extract long feature sequences with rich information from multichannel sensors. In contrast to methods using CNN and RNN, this model could achieve remote feature extraction and the parallel computation of long-sequence-dependent features. The informer encoder adopts the attention distillation layer to increase computational efficiency, thereby lowering the attention computational overhead in comparison to that of a transformer encoder. To better collect location information while maintaining serialization properties, a bi-directional long short-term memory (Bi-LSTM) network was employed. After the fully connected layer, the tool-wear prediction value was generated. After data augmentation, the PHM2010 basic dataset was used to check the effectiveness of the model. A comparison test revealed that the model could learn more full features and had a strong prediction accuracy after hyperparameter tweaking. An ablation experiment was also carried out to demonstrate the efficacy of the improved model module.

Extreme Learning Machine (ELM) is an extension of feed forward Neural Network model with extremely learning capacity and good generalization capabilities. Generally, the classification performance of ELM highly focused on three parameters such as the input weight matrix, the value of bias and the number of hidden neurons presented. ELM randomly chooses the input weights and bias values to minimize the training time. The arbitrary selection of biases and the input weight produce an unforeseen result which causes training error and also produces lesser prediction accuracy. For improving efficiency of ELM training, Ant Colony Optimization with continuous domain algorithm (ACO R ) used to find the optimum input weight and hidden bias values for ELM. With the unequal distribution of classes in imbalanced data sets, ELM algorithms tussle to find good accuracy. ELM algorithm doesn't acquire the mandatory information about the minority class to make an accurate classification. To deal the issues of ELM, in this paper the hybrid algorithms Weighted ELM and Weighted ELM with ACO R are proposed. The main objective of weighted ELM is that the related weight value is computed and assigned for each training sample to increase the classification rate. ACO R method is also integrated with the weighted ELM to find the optimum input weight and bias to maximize the accuracy of classification task. The comparative analysis has been accomplished over Hepatitis dataset. Further, the experimental results clearly revealed that Weighted ELM with ACO R is superior to other proposed methods.