Text|Su Yingmo
Editor|Su Yingmo
preface
Software has become a core part of human life, playing a pivotal role in daily activities, and with the advancement of technology, vulnerabilities are rapidly creating.
Software development organizations view the security of software as a prominent part of their customers' areas of concern, however, there are extensive sources of knowledge for entire industries, both inside and outside the organization.
As organizational priorities continue to change, security knowledge takes a back seat, and an appropriate mechanism needs to be devised to absorb all the knowledge prevalent in the industry and make it available to the software development team in a controlled manner when it is needed.
It requires an intelligent way to put information together, continuously learn from it, and use what has been learned in operations, and for the security of software systems, it needs an intelligent knowledge management system.
An intelligent knowledge management system will be one capable of integrating data from a variety of sources.
Software process analysis
Technical debt is an important consideration in this paper, and several studies have identified the factors that influence technical debt in software systems.
These are collected during the software development process when trying to balance the strategic needs of the customer with the short-term needs.
The anomaly detection method is introduced, which uses the optimization mechanism of routing the original features of the problem area in the Boltzmann machine algorithm, and proposes a software fault detection and correction modeling framework in software testing.
For the agile software development model, a software workload estimation method is proposed, using artificial neural network feed-forward, back-propagation neural network and Ehrman neural network.
Various non-functional requirements were considered for modeling and an appropriate pipeline was established to update the standard and send it to the next stage.
Security controls are identified using automated decision support that can be relevant to any particular system.
A security requirements acquisition approach based on a problem framework is proposed, which considers incorporating security into the software development process at an early stage.
It helps developers gather information about security requirements in an efficient manner, prepare a security catalog to identify security requirements, and access threats using abusive frameworks.
Machine learning techniques were evaluated to determine software requirements for stack overflows, which indicate that potential Dirichlet allocations (LDAs) are widely used to identify software requirements.
A tool is designed to discover vulnerabilities based on software component features that represent domain knowledge in other areas of software development.
Since the goal stated by this method is to predict vulnerabilities in new components, it is possible to exploit the vulnerability history of these software components in production.
It is also possible to take these solutions and integrate them into the development environment for ease of use by software developers.
The overall focus is to make the software system secure from the start, which can reduce the need for companies to make large investments in software security.
The time to fix security issues in a project can significantly affect the entire development process, so it is important to address the issue within the time frame.
Machine learning models are used to predict fixed times, using long short-term memory (LSTM) techniques to classify spam.
This technique automatically learns abstract features, where text is converted into semantic word vectors using ConceptNet and WordNet.
Later, spam is detected from the data using LSTM, and the accuracy of the K-nearest neighbor (K-NN) algorithm is improved in classification tasks.
However, it takes longer in large datasets but provides significant accuracy compared to other methods.
The security requirements mentioned in the Software Requirements Specification document are mined, which are categorized as data integrity, cryptography, access control, and authentication using decision trees.
Predictive models are developed for each security requirement, and pre-trained models can also identify misclassified requirements in documents, providing better insight for requirements engineers.
Further refinement will provide users with information about the required classification and explain why a particular classification was chosen, and neural networks need better ways to make interpretable models, which is a challenging problem.
The structural components of the system in the model
Understanding the construction of language models that are effectively applied to solving problems related to software security issues is essential, and problems related to software security can be solved by leveraging natural language data available across companies and industries.
Effective language modeling capabilities can help derive the information hidden in this data, and software development teams can leverage security-related information when needed.
Language models are the basis of the model used in this article, and language models find their roots in the N-gram modeling approach.
Gram modeling uses the thought process of using the history of a given word to evaluate the probability of a given word, for example, the probability that the next word in the phrase "Jack and Jill went up the" is "hill".
All data related to customer needs and data related to internal software development processes, such as test cases and defects, are extracted and marked as safety-related and non-safety-related data classes.
Thus, the dataset contains text and corresponding labels, and the dataset contains natural language data obtained from customer demand specifications, test cases, defects, and other software development efforts maintained by the software development team.
The data set is divided into training, testing and validation according to the proportion of 50%, 40%, and 10%, and software development requirements management experts mark the data related to customer needs.
The software development technical lead is involved in labeling data related to software development efforts, such as defects and test cases.
Use Python's train-test splitting library to randomly split datasets into training, testing, and validation. We considered various parts of the training and testing data.
It is found that when the training dataset score is 50%, the model does not have overfitting and underfitting problems.
There are a total of 31,342 data points, of which 3,082 are security-related and 28,260 are non-safety-related.
Therefore, the data augmentation technique, which considers various parts of the training and testing data, finds that when the training dataset score is 50%, the model does not have overfitting and underfitting problems.
Data preprocessing includes text tokenization to create a vocabulary where text is first converted to a sequence of words and then to a sequence of numeric IDs.
Tokenization of text involves converting text into digital representations so that these representations can be passed into machine learning or deep learning models for modeling purposes.
Text sequence padding is also done to normalize the text sequence length. The data is represented as a sequence of vectors for further modeling.
Experiments under language models
In the first experiment, CNNs explored text classification, and Algorithm 1 demonstrated the various steps involved in text classification using CNNs.
Algorithm 1
First, the text data is tokenized using the TensorFlow Keras preprocessor. Text sequences are converted to numeric ID sequences. So there will be three sets of text sequences for training, testing, and validation.
Visualize the sentence length distribution to examine the length distribution. The maximum sequence length for the fill can be kept at 250 because most long sequences are within the sequence length of 250. The TensorFlow Keras preprocessor is used to create a 250-length text sequence for all three sets of datasets.
In the next stage, you will explore pre-trained fast text embedding matrices, an open-source lightweight library that helps learn text representations in language models.
The fast text is configured as a matrix of data that it has learned during pre-training, in the form of an embedded representation of a number.
Quick Text works like word2vec, where each word is treated as its character-based N-gram package.
The pretrained word embedding architecture is built in the standard way, for which an embedding size of 300 is chosen for the pretrained model built on 300 dimensions.
Based on TensorFlow Keras' CNN model architecture construction (see algorithm 1), three sets of Conv1D and maximum pooling layers are built using 256, 128 and 64 filters in each Conv1D layer, and the pool size of the maximum pooling layer is 5.
The activation function applied is "ReLU" (Rectified Linear Unit), which has three sets of dense layers and dropout layers with a dropout setting of 25%.
Binary cross-entropy loss and the Adam optimizer are configured for model compilation, and the model architecture runs on the training dataset.
Although the model was configured to run 100 epochs and 128 batch sizes and stopped early, the model achieved the best accuracy at the 7th epoch, with a validation accuracy of 95.95%.
Evaluate model performance on test datasets; It has an accuracy of 71.89%, a weighted average precision of 0.88, a recall of 0.72, and an F1 score of 0.77.
Without too much fine-tuning of the parameters, the model will provide 71.89% accuracy, so these schemas can be further fine-tuned for insurance data to achieve better accuracy.
The experiment further complements the bidirectional LSTM and attention layer (see algorithm 2). The embedding layer holds the pre-trained FastText model.
Tokenization, vectorization, and filling are performed similarly to earlier parts of the experiment. The output of the last layer of the Long Short-Term Memory Gated Loop Unit (LSTM GRU) is fed into the global attention layer sequence.
Algorithm 2
Vectors from hidden sequences are passed to the learning function (h t), including the product vector.
c is the final context vector, T is the total time step of the input sequence, and the attention layer architecture is based on the TensorFlow Keras attention mechanism for temporal data and masking.
TensorFlow is a machine learning library, Keras is a high-level API for TensorFlow, and attention mechanics are cutting-edge methods that leverage learning from important parts of the language rather than trying to learn everything.
The attention mechanism of TensorFlow and Keras provides tools to implement this functionality in the model, and the constructs used in this experiment are taken from FastText-based embeddings and were built in the first part of the experiment.
The core model architecture is built with LSTMs to form sequential models, and LSTMs are superior to RNNs in memorizing long sequence data.
The LSTM manages the input of the current time step, as well as the output of the previous LSTM cell and the memory of the state of the cell in the previous cell.
Bidirectional LSTMs help provide a forward and backward sequence of content, each of which provides outputs that are combined at each time step.
A better context of the text can be preserved by considering the past and future order, and the architecture includes embeddings as inputs, a bidirectional LSTM GGRU with 256 units, an attention layer, and three sets of dense and discarded layers (see algorithm 2).
The thick layer of 256 units and 0.25 dropout rate are also used interchangeably, and "Relu" is the activation function for the middle layer; On the last layer, "sigmoid" is used.
Binary cross-entropy loss and the "Adam" optimizer are also considered, and the model is trained using an early stop mechanism of 100 epochs in training and validation.
Reserve a batch size of 128; The best result came in the 6th EPOCH, with a verification accuracy of 98.69%. The model has an accuracy of 84.33%, a weighted average accuracy of 0.91, a recall of 0.84, and an F1 score of 0.87.
In the next phase of the lab, you'll explore Google's Universal Sentence Encoder (USE). It can encode high-dimensional vectors of different lengths into standard sizes. Figure 1 shows sentence encoding at work.
Figure 1
In this lab, USE is implemented on TensorFlow 1.0. USE models are loaded from the TensorFlow hub.
The USE-based embedding layer is built and fed into a model architecture with two dense layers and a missing layer (see Algorithm 3).
Algorithm 3
The dense layer has 256 cells and a ReLU activation layer with a dropout value of 0.25.
epilogue
The sigmoid activation function is used to classify the results, and binary cross-entropy and the Adam optimizer are also used. Model training is achieved by considering early stopping.
It achieved the best validation accuracy of 97.32% in the first period, with an average accuracy of 92.61% and an average recall, accuracy, and F1 score of 93.0%, 95.0%, and 93.0%, respectively.
Software requirements modeling is one of the prominent parts of this work, and most of the time, the focus is on software correctness during development, which can lead to performance issues later in the development process.
A comprehensive study of all the work done to model software performance throughout the software development lifecycle.
Factors that affect the time to fix security issues using linear regression methods are also evaluated.