Background: Building an effective Intrusion detection system in a multi-attack classification environment is challenging due to the diversity of modern, sophisticated attacks. High-performance classification methods are needed for Intrusion Detection Systems as attackers can establish intrusive methods and easily evade the detection tools deployed in a computing environment. Moreover, it is challenging to use a single classifier to efficiently detect all kinds of attacks. Aims: To propose a unique ensemble framework that can effectively detect different attack categories. Method: The proposed approach is based on building an ensemble by ranking the detection ability of different base classifiers to identify various types of attacks. The F1-score of an algorithm is used to compute the rank matrix for different attack categories. For final prediction algorithm's output for an attack is only considered if the algorithm has the highest F1-Score in the rank matrix for the particular attack category. This approach contrasts with the voting approach where the final classification is based on the voting of all classifiers in the ensemble irrespective of the fact if the algorithm is efficient enough to detect that attack or not. Results: With the proposed method, the final accuracy obtained is 96.97 %, a recall rate of 97.4%, and a better attack detection rate than the baseline classifiers and other existing approaches for different attack categories.

Background The ever increasing sophistication of intrusion approaches has led to the dire necessity for developing Intrusion Detection Systems with optimal efficacy. However, existing Intrusion Detection Systems have been developed using outdated attack datasets, with more focus on prediction accuracy and less on prediction latency. The smart Intrusion Detection System framework evolution looks forward to designing and deploying security systems that use various parameters for analyzing current and dynamic traffic trends and are highly time-efficient in predicting intrusions. Aims This paper proposes a novel approach for a time-efficient and smart Intrusion Detection System. Method Herein, we propose a Hybrid Feature Selection approach that aims to reduce the prediction latency without affecting attack prediction performance by lowering the model's complexity. Light Gradient Boosting Machine (LightGBM), a fast gradient boosting framework, is used to build the model on the latest CIC-IDS 2018 dataset. Results The proposed feature selection reduces the prediction latency ranging from 44.52% to 2.25% and the model building time ranging from 52.68% to 17.94% in various algorithms on the CIC-IDS 2018 dataset. The proposed model with hybrid feature selection and LightGBM gives 97.73% accuracy, 96% sensitivity, 99.3% precision rate, and comparatively low prediction latency. The proposed model successfully achieved a raise of 1.5% in accuracy rate and 3% precision rate over the existing model. An in-depth analysis of network parameters is also performed, which gives a deep insight into the variation of network parameters during the benign and malicious sessions.

Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.

Component based software development approach makes use of already existing software components to build new applications. Software components may be available in-house or acquired from the global market. One of the most critical activities in this reuse based process is the selection of appropriate components. Component evaluation is the core of the component selection process. Component quality models have been proposed to decide upon a criterion against which candidate components can be evaluated and then compared. But none is complete enough to carry out the evaluation.It is advocated that component users need not bother about the internal details of the components. But we believe that complexity of the internal structure of the component can help estimating the effort related to evolution of the component. In our ongoing research, we are focusing on quality of internal design of a software component and its relationship to the external quality attributes of the component.

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