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SLF-ADM: Securing Linux frontiers: Advanced persistent threat (APT) detection using machine learning

Neural computing and Applications 2025年 1-20页

作者： Karim, Syed Sohaib Afzal, Mehreen Iqbal, Waseem Abri, Dawood Al Abbas, Yawar Islamabad44000 Pakistan Electrical and Computer Engineering Department College of Engineering Sultan Qaboos University Al-Khud Muscat123 Oman School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia

Cyber warfare has reached its peak and the most dangerous threat to any country/state/organization in cyber attack space is Advanced Persistent Threat (APT) due to its nature, intention and impact. These attack campaigns are sophisticated and complex enough to get easily detected, specially in the Linux environment on which most of the back-end technologies and super computers are based. There is no proper solution to detect and predict these sophisticated attack campaign. This article presents a novel machine learning approach that utilizes the FastText embedding with support vector machine (SVM) algorithm for rapid and systematic APT attack detection and prediction. The dataset used in the study is developed by simulating several latest APTs and executing payloads in the Linux environment to effectively analyse, detect and predict the campaign. As per the APT life cycle, there are different stages and each stage has different characteristics and paths to observe, which are taken into consideration in the selected dataset. Utilizing the proposed methodology, there are four ML-model which are trained and tested against the accuracy. The maximum predicted accuracy of 96% has been achieved for SVM. The results are compared with other machine learning models on same dataset as well. The experiment shows that SVM has performed significantly better in comparison to the other proposed models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Support vector machines

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series Data

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IEEE Transactions on Services computing 2025年第3期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this article, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666 x speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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4K Video Coding Efficiency in UAV Systems 45

4K Video Coding Efficiency in UAV Systems

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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Benjak, J. Hofman, D. Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Zagreb Croatia

ISBN: (纸本)9789532331035

With increasing requirements for high quality, low bandwidth video transmission systems, comes a demand for more ad hoc video encoders. Unmanned Aerial Vehicles (UAVs), or just drones, have recently grown in popularity and use. Therefore, in this paper, we provide an efficiency comparison of the two most popular video encoders in a First-person view (FPV) drone piloting system. We film all the test sequences ourselves, using a drone with an attached 4K camera. Each sequence captures a scene with different spatiotemporal characteristics. Because video encoders are sensitive to video content or scene complexity, instead of doing a comprehensive comparison of all sequences at once, we first classify all the sequences into four groups. Finally, we examine the compression efficiency by varying the parameters with the highest impact on encoder operation. The quality of compressed footage is determined using four objective video quality measures. Due to high computational complexity, tests were performed on several nodes of a supercomputer and the relative encoding times were also considered. © 2022 Croatian Society MIPRO.

关键词： Antennas

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Enhancing Early Diagnosis of Autism Spectrum Disorder Using a Bayesian-Optimized Random Forest Algorithm 3

Enhancing Early Diagnosis of Autism Spectrum Disorder Using ...

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

作者： Babu, Tina Nair, Rekha R Kishore, S. Mahapatra, Sheila Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Nitte Institute of Professional Education Mangalore India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for timely intervention and improved outcomes. Traditional diagnostic methods, which rely on clinical observations and assessments, can be subjective and time-consuming. This study introduces a novel approach to predicting ASD using a machine learning technique, specifically a Bayesian-optimized Ran- dom Forest classification algorithm. The proposed method uses a comprehensive dataset containing various behavioral, cognitive, and demographic features associated with ASD. The Optimized Random Forest with Bayesian approach is used to categorize indi- viduals into two groups: those who have ASD and those who do not. To develop this ensemble learning technique, multiple decision trees are used, and to achieve higher performance as well as better capability to generalize, the hyperparameters are optimized with Bayesian optimization. Preliminary analysis and comparison with existing databases substantiated the findings and revealed the effectiveness of the proposed approach. The Bayesian-optimized Random Forest classifier achieved an accuracy value of 0.9890. Such performance is highly promising and could enable increased use of machine learning methods in timely and accurate diagno- sis of ASD. The proposed approach provides recommendations for a verified diagnostic technique when implemented and creates a strong foundation for future investigations into the use of machine learning for identifying and supporting children with ASD. © 2025 The Author(s).

关键词： Random forests

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FacialEmoNet: A Novel Facial Expression Recognition Technique 18

FacialEmoNet: A Novel Facial Expression Recognition Techniqu...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Govindarajan, Priya Sadhika, C. Sunil, Gopika Tesfahun, Abebe School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India Debre Markos University School of Electrical and Computer Engineering Debre Markos Institue of Technology Ethiopia

ISBN: (纸本)9789380544519

Facial emotion recognition plays a crucial role in human-computer interaction and artificial intelligence applications, serving as a cornerstone for understanding user emotions. The accurate interpretation of facial expressions is particularly vital in domains such as virtual assistants, human-computer interfaces, and emotion-aware technologies, allowing systems to respond precisely and tailor interactions based on the user's emotional state. In order to overcome challenges associated with conventional approaches, this study introduces the FacialEmoNet technique, an innovative algorithm that seamlessly integrates deep learning and machine learning methods for the efficient classification of facial expression datasets. The primary focus is on capturing and interpreting facial expressions, marking its efficacy and significant advancement in emotion recognition systems. This work investigates the utilization of existing classifiers to classify the dataset effectively. FacialEmoNet utilizes a dynamic methodology to improve recognition accuracy by implementing pioneering feature extraction techniques, resulting in an impressive accuracy of 97.56% and an exceptionally low error rate of 2.44%. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Classifiers Facial Emotion FacialEmoNet Recognization

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit In Kim, Dong Jamalipour, Abbas Ben Letaief, Khaled Tafazolli, Rahim University of Electronic Science and Technology of China National Key Laboratory on Wireless Communications Chengdu610054 China University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Nanyang Technological University College of Computing and Data Science Jurong West Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon03063 Korea Republic of University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Institute for Communication Systems School of Computer Science and Electronic Engineering University of Surrey GuildfordGU2 7XH United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Ship Detection in Satellite Images using You Only Look Once-Neural Architecture Search 9

Ship Detection in Satellite Images using You Only Look Once-...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Anand, R.N. Singh, Rimjhim Padam Gupta, Deepa Palaniappan, Kannappan Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical Engineering and Computer Science University of Missouri Missouri United States

ISBN: (纸本)9798350383201

Ship Detection in remotely sensed satellite imagery is a challenging task due to tiny size of the objects and low resolution of the images. Several object detection methods that perform very well for medium-sized and large-sized objects, miserable fail to perform decently in such applications. From the vast variety of the models available, few You only look once (YOLO) have proven to yield decent efficiency and accuracy in ship detection in satellite images. Hence, the paper proposes to detect and classify ships in remotely sensed satellite images by applying transfer learning to fine-tune the latest YOLO-Neural Architecture Search (YOLO-NAS) model. To the best of our knowledge this paper is the first attempt to test YOLO-NAS model for ship detection in satellite imagery. To demonstrate the strength of the YOLO NAS model, the paper also trains YOLOv5 and YOLOv8 on the same dataset allowing to comprehensively compare the efficiency of the proposed model against previous state-of-art versions. The findings demonstrate high mean average precision scores for the proposed model, indicating the effectiveness of the YOLO-NAS algorithm in accurately locating ships in satellite images. © 2023 IEEE.

关键词： Object detection

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Smooth Motion Planning of an Omnidirectional Mobile Robot in Dynamic Environments

Smooth Motion Planning of an Omnidirectional Mobile Robot in...

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2022 International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Šelek, Ana Seder, Marija Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this paper, a real-time smooth motion planning method for a four mecanum wheeled omnidirectional mobile robot in dynamic environments that generates a smooth collision-free trajectory is proposed. The method employs a graph-search-based path planning algorithm and a smoothing technique based on clothoids. It is suitable for real-time operation due to its computational simplicity and allows fast path replanning in case the robot encounters unknown and dynamic obstacles. Such a path is feasible for holonomic mobile robots since it does not contain sharp turns so the energy consumption is reduced. Our method is compared to the reactive model predictive control scheme in simulations. The comparison showed advantages of the proposed method in the reduced task execution time, path length, cumulative replanning time, and smooth velocity profile. © 2022 IEEE.

关键词： Motion planning

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Enhanced Lightweight Facial Emotion Recognition Systems for Visually Impaired People 12

Enhanced Lightweight Facial Emotion Recognition Systems for ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Shehada, Dina Turky, Ayad Rabie, Tamer Hussain, Abir University of Dubai College of Engineering and IT Dubai United Arab Emirates University of Sharjah College of Engineering Department of Electrical Engineering Sharjah United Arab Emirates University of Sharjah College of Computing & Informatics Department of Computer Science Sharjah United Arab Emirates University of Sharjah College of Computing & Informatics Department of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350391398

Facial expressions are a vital component of human communication, conveying emotional information that enhances the social experience. However, for individuals with visual impairments, perceiving and interpreting facial expressions can be challenging, hindering their ability to engage fully in social interactions. This paper presents an enhanced facial emotion recognition system specifically designed to aid visually impaired individuals. Two enhanced approaches for facial emotion recognition systems are proposed. The first approach consolidates negative emotions (anger, fear, sadness, disgust) into a single class, while the second approach incorporates diverse training datasets from real-world environments. The negative emotion consolidation approach achieved 90.9% and 94.7% accuracy on the FER2013 and CK+ datasets, respectively. While, the diverse dataset integration approach achieved 84.9% and 70.6% accuracy on FER2013 and CK+ datasets, respectively. This is a significant improvement when benchmarked with other approaches available in the literature. The feasibility of the enhanced proposed systems as assistive technology for the visually impaired indicated its ability to be applied in real-world applications. © 2024 IEEE.

关键词： Assistive technology

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Blockchain Technology for Land Registry Management in Developing Countries 2

Blockchain Technology for Land Registry Management in Develo...

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2nd International Conference on Emerging Trends in electrical, Control, and Telecommunication engineering, ETECTE 2023

作者： Mansoor, Mohammad Abdullah Ali, Mohsin Mateen, Abdul Kaleem, Mohammad Nazir, Sajid COMSATS University Electrical and Computer Engineering Department Islamabad Pakistan Glasgow Caledonian University Department of Computing Glasgow United Kingdom

ISBN: (纸本)9798350305654

Blockchain technology provides a secure framework for dealing with digital assets, conducting financial transactions, and managing property information. The traditional process of land registration often suffers from inefficiency and complexity, primarily due to the involvement of multiple intermediaries, which increases the risk of fraudulent land transfers. In developing countries, property records are often not easily accessible, relevant processes are susceptible to corruption, and have a lengthy turn-around time. This not only leads to disputes over land ownership but hinders the economic activity, such as, disused land, legal complications for agricultural loans and mortgages. By leveraging blockchain technologies, land ownerships can be transparent, supporting accessible and trustworthy land transfers with a reduced risk of fraud and corruption. We propose a blockchain based solution to facilitate access to land documents for verifying land ownership prior to transferring funds from a buyer to the seller. The transparent and unalterable public ledger can thus validate and preserve the land transactions. © 2023 IEEE.

关键词： Smart contract

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