This paper explores the concept of entropy of a flow to augment flow statistical features for encrypted DNS tunnelling detection, specifically DNS over HTTPS traffic. To achieve this, the use of flow exporters, namely...
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Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (...
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K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared ...
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Despite software startups often not handlingsensitive data, the implementation of robust security measures is crucial to mitigate significant financial and reputational risks. This study investigates the cost-benefit ...
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This paper demonstrates that Yolo V7, the latest version of the single-stage neural network Yolo, has good recognition of lunar impact craters on the Lunar CCD data and DEM data provided by the LROC camera which carri...
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作者：
Wanjari, KetanVerma, Prateek
Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India
Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have si...
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The growing use of the Internet with its vulnerabilities has necessitated the adoption of Intrusion Detection Systems (IDS) to assure security. IDSs are protective systems that detect outsider infiltrations, unauthori...
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The growing use of the Internet with its vulnerabilities has necessitated the adoption of Intrusion Detection Systems (IDS) to assure security. IDSs are protective systems that detect outsider infiltrations, unauthorised accesses and malfunctions occurring in computer networks. Intrusions can be detected and reported to the network administrator by IDSs using various pieces of information such as port scanning and irregular traffic detection. Intrusion detection is a classification problem, and identifying effective features is an essential aspect of classification methods. Standard methods used for classification are neural networks, fuzzy logic, data mining techniques and metaheuristics. One of the novel metaheuristic algorithms introduced to address optimisation problems is the Horse herd Optimisation Algorithm (HOA). This paper introduces a new approach on the basis of HOA for network intrusion detection. The new method uses horse behaviours in the herd to select effective features to detect intrusions and interactions between features. For the purpose of the new approach, HOA is first updated into a discrete algorithm using the floor function. The binarised algorithm is then converted into a quantum-inspired optimiser by integrating the concepts of quantum computing with HOA to improve the social behaviours of the horses in the herd. In quantum computing, Q-bit and Q-gate aid in striking a greater balance between the exploration and exploitation processes. The resulting algorithm is then converted into a multi-objective algorithm, where the objectives can be chosen from a set of optimal solutions. The new algorithm, MQBHOA, is then used for intrusion detection in computer networks, which is a multi-objective optimisation problem. For the classification, the K-Nearest Neighbour (KNN) classifier is applied. To evaluate the new algorithm’s performance, two data sets, NSL-KDD (Network Security Laboratory—Knowledge Discovery and Data Mining) and CSE-CIC-IDS2018, are
Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syn...
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Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.
This paper developed a new model called Single Rule Random Forest (SrRF) that enhances the performance of the Fandom Forest technique(RF)and reduces its rules, then compared the performance of this model with a set of...
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作者：
Wanjari, KetanVerma, Prateek
Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India
Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India
Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image...
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