Abstract Intrusion Detection Systems (IDSs) with a Machine Learning (ML) technique have shown efficacy in securing Internet of Things (IoT) networks in recent years. As cyber threats continue to evolve, IDS have become increasingly reliant on advanced ML and deep learning (DL) techniques to improve detection accuracy. However, the growing complexity of these models often makes it challenging for security analysts to interpret the reasoning behind specific alerts. While extensive research has been conducted on IDS using ML and DL methods, the issue of interpretability remains largely unaddressed. One of the interpretable methods in machine learning is to use model-agnostic interpretation tools that can be applied to any supervised machine learning model. To address this issue, a new hybrid model composed of a lightweight one-dimensional convolutional Neural Network (1D-CNN) is proposed with the interpretation ability of the results in which, resource-constrained IoT devices can execute the proposed model. In the first phase, the SHapley Additive exPlanations (SHAP) technique is used for feature selection to detect the most important features. These features can be considered for redesigning the model by using a smaller set of features and reducing the computation and complexity of the model, leading to the creation of a lighter deep network. After the prediction of the proposed model, to interpret and explain the results and analyze the influential factors in predictions, Agnostic methods are employed both globally(SHAP) and locally(SHAP, LIME) to clarify the reasons for the predictions. Experimental results using the TON-IoT dataset showed accuracy, precision, recall, and F1-score criteria to 0.995, 0.9949, 0.9947, and 0.9947, respectively. Therefore, besides accurately predicting attacks in the area of IoT with high precision and lightweight models, the proposed method increases transparency to assist cybersecurity personnel in gaining a better understanding of IDS judgments.

REST Web Services is a lightweight, maintainable, and scalable service accelerating client application development. The antipatterns of these services are inadequate and counter-productive design solutions. They have caused many qualitative problems in the maintenance and evolution of REST web services. This paper proposes an automated approach toward antipattern detection of the REST web services using Genetic Programming (GP). Three sets of generic, REST-specific and code-level metrics are considered. Twelve types of antipatterns are examined. The results are compared with the manual rule-based approach. The statistical analysis indicates that the proposed method has an average precision and recall scores of 98% (95% CI, 92.8% to 100%) and 82% (95% CI, 79.3% to 84.7%) and effectively detects REST antipatterns.

Stereo vision cameras are flexible sensors due to providing heterogeneous information such as color, luminance, disparity map (depth), and shape of the objects. Today, Convolutional Neural Networks (CNNs) present the highest accuracy for the disparity map estimation [1]. However, CNNs require considerable computing capacity to process billions of floating-point operations in a real-time fashion. Besides, commercial stereo cameras produce huge size images (e.g., 10 Megapixels [2]), which impose a new computational cost to the system. The problem will be pronounced if we target resource-limited hardware for the implementation. In this paper, we propose DenseDisp, an automatic framework that designs a Siamese neural architecture for disparity map estimation in a reasonable time. DenseDisp leverages a meta-heuristic multi-objective exploration to discover hardware-friendly architectures by considering accuracy and network FLOPS as the optimization objectives. We explore the design space with four different fitness functions to improve the accuracy-FLOPS trade-off and convergency time of the DenseDisp. According to the experimental results, DenseDisp provides up to 39. 1x compression rate while losing around 5% accuracy compared to the state-of-the-art results.

In bug fixing process, estimating the ‘Time to Fix Bug’ is one of the factors that helps the triager to allocate jobs in a better way. Due to the limitation of resources for bug fixing, the bugs with long fixing time must be identified, as soon as possible, after receiving the report. This helps the prioritisation and fixing process of the bug reports. In the process of bug fixing, a temporal sequence of activities is done. Each activity is represented by a term. Useful semantic information and long‐term dependency are available between terms in the sequence, but it is usually underutilised by existing bug fixing time predictor approaches. This work presents a novel deep learning‐based model (called DeepLSTMPred) that (i) converts constituent terms to a vector of real numbers by considering their semantic meaning, (ii) finds the long‐term dependencies between terms by deep long short term memory (LSTM) and (iii) classifies sequences to short fixing time or long fixing time. DeepLSTMPred is evaluated on bug reports extracted from the Mozilla project. The results show that the proposed method has better performance in comparison with a state‐of‐the‐art approach (that is the hidden Markov‐based model). The experimental results show that DeepLSTMPred achieves 15–20% improvement in terms of accuracy, precision, f ‐score, and recall.