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Advanced Persistent Threats (APT): evolution, anatomy, attribution and countermeasures

Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9355-9381页

作者： Sharma, Amit Gupta, Brij B. Singh, Awadhesh Kumar Saraswat, V.K. Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra136119 India International Center for AI and Cyber Security Research and Innovations Department of Computer Science and Information Engineering Asia University Taichung Taiwan NITI Aayog New Delhi110001 India Lebanese American University Beirut Lebanon School of Computing Skyline University College Sharjah United Arab Emirates Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India Birkbeck University of London London United Kingdom

In today’s cyber warfare realm, every stakeholder in cyberspace is becoming more potent by developing advanced cyber weapons. They have equipped with the most advanced malware and maintain a hidden attribution. The precocious cyber weapons, targeted and motivated with some specific intention are called as Advanced Persistent Threats (APT). Developing defense mechanisms and performing attribution analysis of such advanced attacks are extremely difficult due to the intricate design of attack vector and sophisticated malware employed with high stealth and evasive techniques. These attacks also include advanced zero-day and negative-day exploits and payloads. This paper provides a comprehensive survey on the evolution of advanced malware design paradigms, APT attack vector and its anatomy, APT attack Tactics, Techniques, and Procedures (TTP) and specific case studies on open-ended APT attacks. The survey covers a detailed discussion on APT attack phases and comparative study on threat life-cycle specification by various organizations. This work also addresses the APT attack attribution and countermeasures against these attacks from classical signature and heuristic based detection to modern machine learning and genetics based detection mechanisms along with sophisticated zero-day and negative day malware countermeasure by various techniques like monitoring of network traffic and DNS logs, moving target based defense, and attack graph based defenses. Furthermore, the survey addresses various research scopes in the domain of APT cyber-attacks. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Life cycle

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Advancing Deep Learning Models through Robust Feature Extraction and Transfer Learning for Enhanced Image Recognition 2

Advancing Deep Learning Models through Robust Feature Extrac...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSai 2023

作者： Thenmozhi, R. Kavinilavu, A. Malathi, G. Mohanambal, K. Srm Valliammai Engineering College Department of Ai&ds Chennai India Sri Sairam Institute of Technology Computer Science and Engineering Chennai India Velammal Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

Deep learning has changed image recognition forever, which has achieved amazing results in many areas. On the other hand, deep learning models depend a lot on the quality and amount of labeled training data. This study suggests a new way to improve deep learning models for image recognition tasks using transfer learning and strong feature extraction methods. First, we look into how well different feature extraction methods, like autoencoders and convolutional neural networks (CNNs), can distinguish features from pictures. Considering different network structures and pre-training methods, we look at how well they do in terms of accuracy and how quickly they can do their work. Next, we look at transfer learning as using models already trained on big datasets to make deep learning models better at generalization. We look into various transfer learning methods, such as fine-tuning, feature extraction, and domain adaptation, to make the already-trained models work for new picture recognition tasks. We also deal with the domain shift problem by suggesting ways to match the source and target domains' feature distributions. We look into methods like domain adaptation and antagonistic training to reduce the difference in distribution and make the learned features more transferable. We do a lot of tests on standard image recognition datasets like ImageNet and RobustFeatureNet to see how well the proposed method works. Our approach greatly enhances the performance of deep learning models compared to traditional training methods, as shown by the testing results. The suggested framework gets the highest level of accuracy while lowering the need for a large labeled dataset. © 2023 IEEE.

关键词： Convolutional Neural Networks Feature Extraction Image Recognition RobustFeatureNet Transfer Learning

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ARIA: Augmented Reality and Artificial Intelligence enabled mobile application for Yield and grade prediction of tomato crops

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Procedia computer science 2024年 235卷 2693-2702页

作者： Balaji Prabhu B V Shashank R Shreyas B Omkar Subbaram Jois Narsipura Dept. of Computer Science and Engineering (AI&ML) Malnad College of Engineering Hassan Visvesvaraya Technological University Belagavi India 573202 Dept. of Computer Science Viterbi School of Engineering University of Southern California Los Angeles USA 90007 Dept. of Computer Science and Engineering RNS Institute of Technology Bangalore India 560098 Dept. of Aerospace Engineering Indian Institute of Science Bangalore India 560012

Agriculture is the most crucial sector of the Indian economy. Lately, there has been a surge in the usage of technologies like deep learning and computer vision to make the process of agriculture modern and consequently, lessening the mistakes related to conventional processes. This work delivers a user-friendly, accessible, and novel approach for the detection, counting, and grading of tomatoes found on a farm. An Augmented Reality (AR) based mobile application is developed to obtain the images efficiently from a tomato farm in the pre-harvest stage subjected to open situations. The proposed approach uses Faster RCNN, a convolutional neural network model for detection of tomatoes from the input image on a large scale. The proposed model is trained and tested using 2083 images. The results are then analyzed for the overall performance of detection, segmentation, and classification of tomatoes. The results have examined the efficiency of the proposed mobile application and demonstrate the robustness it exhibits for the detection, grading and yield prediction of Tomatoes.

关键词： tomato detection tomato grading tomato yield prediction augmented reality deep learning

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Holistic Cyber Security Framework for Deep Web using Federated Learning in Healthcare and Distributed Computing 3

Holistic Cyber Security Framework for Deep Web using Federat...

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3rd International Conference on Machine Learning and Data engineering, ICmlDE 2024

作者： Paul, Subrata Mitra, Anirban Shambhavi, Shreya Ghosh, Shivnath Bandyopadhyay, Anjan Department of CSE-AI Brainware University Ramkrishnapur Road West Bengal Barasat700125 India Department of Computer Science and Engineering Amity University Kolkata Kadampukur West Bengal New Town700135 India Odisha Bhubaneswar751024 India

There are special possibilities as well as issues for knowledge management when it comes to the Deep Web, which contains hidden and non-indexed information. In order to overcome the difficulties presented by concealed and non-indexed data, this study presents a unique framework for improving cyber security in Deep Web contexts. Federated Learning (FL) and graph-based analysis are utilised in this way. Proposed approach includes a hybrid web crawler that retrieves URLs from surface websites that are security-focused as well as illegal dark web services, enabling a thorough assessment of any risks. To ensure a thorough evaluation procedure, these URLs are evaluated using an ontology-based scoring system that compares them to predetermined reliability criteria. The findings show a significant increase in detection accuracy, with recall and precision scores more than 85% for all sources examined. These ratings are transformed into practical recommendations through the decision mapping procedure, which focuses on protecting healthcare data and stopping the unauthorised sale of personal information. This technique leverages decentralised data analysis to protect data confidentiality in cloud, fog, and edge computing settings while improving threat detection. This comprehensive approach provides a strong defence against changing cyber threats and demonstrates a flexible and dynamic approach to address modern cyber security issues. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Federated learning

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Salt Deposits Identification Using Enhanced U-net-based Mixed Attention Loss Function for Semantic Segmentation 2

Salt Deposits Identification Using Enhanced U-net-based Mixe...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Anitha, D. Al-Attabi, Kassem Priyanka, M.S. Soujanya, Thota Bhuvaneshwarri, I. Srm Institute Of Science And Technology School Of Computing Department Of Computational Intelligence Tamilnadu Kattankulathur India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of AI-ML Bengaluru India Sr University School Of Agricultural Sciences Telangana Warangal India Government College Of Engineering Department Of Information Technology Tamil Nadu Erode India

ISBN: (纸本)9798350381665

Numerous areas of Earth's surface with huge accumulations of oil and gas have high salt deposits under surface. Determining such deposits assists several countries in enhancing the storage capacity of petroleum reserves and exploring new ones. However, determining such a deposit is challenging because expert seismic images need human interpretation of salt bodies, which causes biased and greatly variable translations. This research proposes the Enhanced U-net with Mixed Attention Loss Function (EU-net-based MALF) to automatically and accurately identify whether or not the seismic images have salt deposit regions. Initially, the images are gathered from Tomlinson Geophysical Services (TGS) dataset to evaluate the model performance. Then, the nearest neighbor interpolation method and min-max normalization are employed for image resizing and normalizing. At last, the EU-net-MALF is performed for semantic segmentation. The EU-net-MALF achieves a better accuracy of 98.26% compared to existing methods like fully convolutional DenseNet, U-Net and deep supervision, and Interactive Salt Segmentation based on Convolutional Neural Network and GraphCut (SaltISCG). © 2024 IEEE.

关键词： Image enhancement

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Automatic Highlight Generation of Soccer Videos 14

Automatic Highlight Generation of Soccer Videos

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Park, Jiwoo Jwa, Younkyung Kwak, Jiin Lim, Jihun Kim, Sehee Pohang University of Science and Technology Ai Graduate School Pohang Korea Republic of Gwangju Institute of Science and Technology Ai Graduate School Gwangju Korea Republic of Ulsan National Institute of Science and Technology Ai Graduate School Ulsan Korea Republic of Korea University Department of Mathematics Seoul Korea Republic of Seoul National University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

Due to soccer's immense popularity, numerous soccer matches are played each year and people watch or record these matches. However, considering that the duration of a game of soccer is at least 90 minutes and that important scenes such as goals and passes take up a small portion of the total time, we inevitably look for highlights rather than the entire game. Therefore, we propose a model that automatically generates a highlight video of a full soccer match in this paper. For this purpose, we searched diverse action-spotting models to choose a backbone model for our highlight classifier. Through various experiments, NetVLAD++ is selected as a backbone model and we created a fairly natural highlight generator by combining NetVLAD++ and our highlight classifier. Our results are meaningful because they can be applied in various fields and have huge room for improvement. © 2023 IEEE.

关键词： Automatic Video Summarization Highlight Generation Soccer Video Highlight

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Deep Learning IoT Malware Analysis: Investigation and Understanding

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Neural Computing and Applications 2025年 1-28页

作者： Abdullah, Muhammed Amin Yu, Yongbin Cai, Jingye Addo, Daniel Bankas, Edem K. Gu, Yeong Hyeon Alqahtani, Ali Al-antari, Mugahed A. School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu 611731 China School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 South Korea Center for Artificial Intelligence and Computer Science Department King Khalid University Abha 61421 Saudi Arabia

This empirical study explores the ability of lightweight convolutional neural networks (CNNs) for malware analysis in Internet of Things (IoT) environments, emphasizing the impact of input dimensionality and size on feature extraction quality. By converting malware binaries into 1D and 2D grayscale images, we systematically evaluate how varying input resolutions (e.g., 64 × 64 for 2D, 4096 for 1D) influence model performance, resource efficiency, and interpretability. Experiments on the Microsoft Malware Dataset (MMD) and IoT Malware Dataset (IMD) reveal that 1D CNN models outperform their 2D counterparts in accuracy, parameter efficiency, and training time, with gated recurrent unit (GRU) classifiers demonstrating superior performance across both dimensions. Post hoc visual interpretability techniques, including saliency maps and activation heatmaps, uncover critical feature patterns driving classification decisions, addressing the "black box" limitations of deep learning in cybersecurity. Our findings challenge the assumption that 2D models inherently excel in image-based tasks and provide actionable insights for designing resource-efficient IoT malware detection systems. By bridging the gap between model complexity and deployment feasibility, this work advances the application of explainable ai to protect IoT ecosystems against evolving cyber threats. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional Neural Networks (CNN) Deep learning Feature selection Internet of things Interpretability saliency maps Malware analysis Malware Images

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Anomaly Detection in Social Networks using Improved Local Outlier Factor

Anomaly Detection in Social Networks using Improved Local Ou...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Rana Veer Samara Sihman Bharattej R Muntader Mhsnhasan Sowmya M Prabhakaran M V K. Ghamya Department of Computer Science Saint Leo University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering (Cyber Security) Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively.

关键词： Measurement Accuracy Social networking (online) High dimensional data Transfer learning Feature extraction Real-time systems Intelligent systems Anomaly detection Principal component analysis

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Enhancing Predictive Maintenance in Industrial IoT through Machine Learning Models 4

Enhancing Predictive Maintenance in Industrial IoT through M...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Rani, A. Jaya Mabel Prithivirajan, V. Shiney, S Arumai Babaiah, Ch. Swamy, Hemanth Sivagamidevi, G. Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India S A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Dundigal Hyderabad India Motorola Solutions India Dhanalakshmi College of Engineering Department of Ai & Ds Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

Significant production losses and increased maintenance costs can arise from unplanned downtime in industrial settings caused by machine faults. In order to identify issues early and prevent significant breakdowns, predictive maintenance approaches make use of data collected from Internet of Things (IoT)-enabled devices installed in operating equipment. Here, we present a predictive maintenance system that employs machine learning methods, namely AdaBoost, to categorize various types of machine pauses in real-time, with a focus on knitting machines. Six different types of machine stops were classified using machine learning: idle stop, Lycra stop, full roll stop, gate stop, feeder stop, and needle stop. We did this by preprocessing and inputting machine learning model data, which included machine speeds and steps. After a rigorous training and optimization process that combines cross-validation and hyperparameter tuning, the model achieves an impressive accuracy rate of 92% on the test dataset. These results highlight how our suggested solution might help the textile sector become more productive and efficient overall by precisely detecting machine stoppage and enabling timely maintenance interventions. © 2024 IEEE.

关键词： Predictive maintenance

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Multi Label Classification Algorithm Based on Conditional Local Correlations among Labels and Ensemble Learning 25

Multi Label Classification Algorithm Based on Conditional Lo...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Nasir, Ola Hadi, Wael Samara, Ghassan Al Daoud, Essam Aljaidi, Mohammad Khouj, Mohammed Talat Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Multi-Label Classification (mlC) is an advanced form of traditional classification, where each data instance is simultaneously associated with multiple labels. The importance of mlC has increased significantly in recent years, due to its wide range of application areas. Modern algorithms, when faced with the challenge of multiple labels, continue to show suboptimal performance, conversely, they show significantly superior performance when processing single-label datasets. This paper proposes a new algorithm to deal with the problem of transforming datasets from multiple labels to single labels by exploiting conditional and local correlations between labels after applying the Apriori predictive algorithm to extract relationships. Six ensemble learning models: Voting Hard, Voting Soft, Voting Weighted, Stacking LR, Stacking RF, and Stacking SVM, along with four classification models: SMO, DecisionStump, Random Forest, and Random Committee, were applied to three datasets. The algorithm showed effective and good performance by comparing the results of the proposed Multi Label Classification algorithm based on Conditional Local Correlations among Labels (mlC-CLCL) with several popular algorithms for dealing with multiple labels. © 2024 IEEE.

关键词： Contrastive Learning

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