Article
Computer Science, Information Systems
Michal Motylinski, Aine MacDermott, Farkhund Iqbal, Babar Shah
Summary: The rapid development and widespread adoption of the Internet of Things (IoT) has brought new security challenges. There are numerous IoT devices with underlying security vulnerabilities, making them susceptible to malware attacks due to insufficient device authentication/authorization. IoT botnets are designed to exploit these unsecure devices and networks. This paper presents a methodology for the pre-processing and classification of the IoT-Bot dataset, utilizing GPU acceleration for training and evaluating models. The proposed methodology achieves high scores for accuracy, precision, recall, and f1-score, while significantly reducing the training and estimation times.
COMPUTERS & SECURITY
(2022)
Article
Computer Science, Information Systems
Jaidip Kotak, Yuval Elovici
Summary: This research evaluates the robustness of payload-based IoT device identification solutions against adversarial examples generated using a new approach. The results show that adversarial examples generated using heatmaps can deceive existing payload-based IoT device identification solutions with up to 100% accuracy.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Chemistry, Multidisciplinary
Hend Khalid Alkahtani, Khalid Mahmood, Majdi Khalid, Mahmoud Othman, Mesfer Al Duhayyim, Azza Elneil Osman, Amani A. Alneil, Abu Sarwar Zamani
Summary: The rapid development and widespread utilization of the Internet of Things (IoT) have brought cybersecurity to the forefront. This article introduces an Optimal Graph Convolutional Neural Network based Ransomware Detection (OGCNN-RWD) technique for IoT security. By utilizing learning enthusiasm for feature subset selection and the GCNN model for ransomware classification, the OGCNN-RWD system outperforms other existing techniques with an accuracy of 99.64% according to simulation experiments on a ransomware database.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Fatma S. Alrayes, Mohammed Maray, Abdulbaset Gaddah, Ayman Yafoz, Raed Alsini, Omar Alghushairy, Heba Mohsen, Abdelwahed Motwakel
Summary: This study presents a botnet detection model using machine learning and the barnacles mating optimizer for the IoT environment. The model is important for identifying and recognizing botnets in the IoT, and experimental results demonstrate significant improvement in performance compared to existing methods.
Article
Computer Science, Theory & Methods
Nazar Waheed, Xiangjian He, Muhammad Ikram, Muhammad Usman, Saad Sajid Hashmi
Summary: The security and privacy concerns of users have become significant as IoT devices become more involved in various applications. Machine Learning algorithms and Blockchain techniques are being used to address these issues effectively in recent years.
ACM COMPUTING SURVEYS
(2021)
Article
Computer Science, Information Systems
Iqbal H. Sarker
Summary: This paper introduces CyberLearning, a machine learning-based cybersecurity modeling approach, and evaluates the effectiveness of various machine learning-based security models through empirical analysis.
INTERNET OF THINGS
(2021)
Article
Engineering, Multidisciplinary
V. Nanda Gopal, Fadi Al-Turjman, R. Kumar, L. Anand, M. Rajesh
Summary: Breast cancer is the most common disease among women worldwide, and early diagnosis is crucial for reducing mortality. This paper proposes a method for early diagnosis of breast cancer using IoT and machine learning, achieving high accuracy and low error rates. The results show that the MLP classifier outperforms LR and RF in terms of accuracy and error rate.
Article
Computer Science, Information Systems
Jose Roldan-Gomez, Jesus Martinez del Rincon, Juan Boubeta-Puig, Jose Luis Martinez
Summary: In recent years, the Internet of Things (IoT) has grown rapidly, leading to an increase in attacks against it. This paper proposes an architecture that can generate complex event processing (CEP) rules for real-time attack detection in an automatic and unsupervised manner. By integrating CEP technology with principal component analysis (PCA), Gaussian mixture models (GMM), and the Mahalanobis distance, the architecture is able to analyze and correlate large amounts of data in real time, making it suitable for IoT environments. The testing of this architecture in simulated attack scenarios shows that the generated rules achieve a high F1 score of .9890 in real-time detection of six different IoT attacks.
Article
Multidisciplinary Sciences
Arvind Prasad, Shalini Chandra
Summary: This paper introduces BotDefender, a collaborative framework for protecting against botnet attacks. The framework combines a network traffic analyzer and machine learning techniques to detect and defend against botnet attacks. The network traffic analyzer performs in-depth analysis to identify and filter out botnet-related traffic, significantly reducing the network traffic load and forwarding only a reduced amount of traffic to the machine learning model for further analysis. The machine learning model, powered by a novel feature selection technique and an ensemble-based approach, exhibits consistent performance in detecting bots. Experimental results show that BotDefender filters out 99.8% of botnet traffic and achieves an overall accuracy of 100%.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
Article
Mathematics
Tariq Ahamed Ahanger, Usman Tariq, Fadl Dahan, Shafique A. Chaudhry, Yasir Malik
Summary: In this research paper, the use of machine learning techniques to enhance ransomware defense in IoT devices running on the PureOS operating system is explored. A ransomware detection framework using XGBoost and ElasticNet algorithms in a hybrid approach is developed and tested using a dataset of real-world ransomware attacks on IoT devices. The framework achieved high accuracy (90%) and low false-positive rates, demonstrating its effectiveness in detecting and preventing ransomware attacks on IoT devices running PureOS.
Article
Chemistry, Multidisciplinary
Merve Ozkan-Okay, Refik Samet, Omer Aslan, Selahattin Kosunalp, Teodor Iliev, Ivaylo Stoyanov
Summary: The fast development of communication technologies and computer systems poses security challenges, with growing and sophisticated network-related attacks. Traditional methods are no longer effective in detecting complicated cyber attacks, calling for new techniques that utilize data mining, machine learning, and deep learning to distinguish intrusions from normal network traffic.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Sai Huang, Chunsheng Lin, Wenjun Xu, Yue Gao, Zhiyong Feng, Fusheng Zhu
Summary: The article proposes a novel cyclic correntropy vector (CCV)-based automatic modulation classification (AMC) method using long short-term memory densely connected network (LSMD), demonstrating superior performance compared to recent schemes. The method extracts CCV features from received signals and utilizes data-driven LSMD with an additive cosine loss to train the model for maximizing interclass feature differences and minimizing intraclass feature variations.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Amit Kumar Mishra, Shweta Paliwal
Summary: This study examines the relationship between network traffic and security attacks, finding that attacks are becoming more synchronized and surpassing existing network analytic solutions. Machine learning approaches successfully detect and mitigate modern attacks. Three benchmark datasets were utilized, and the stacking model of LGBM and random forest yielded the best predictions.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Abhijit Singh, Biplab Sikdar
Summary: In this article, the authors propose a white-box adversarial attack mechanism to generate adversarial examples for data obtained from smart meters. They demonstrate that the statistical properties of the adversarial datapoints are indistinguishable from those of the true datapoints. The effectiveness of defense mechanisms for white-box adversarial attacks is also evaluated, showing that the original models are significantly affected.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Mathematics, Applied
Youseef Alotaibi, R. Deepa, K. Shankar, Surendran Rajendran
Summary: Internet of Things (IoT) edge devices are facing security challenges and the need for data protection. This study proposes a security solution using an inverse chi square-based flamingo search optimization algorithm with machine learning to address these issues.
Article
Computer Science, Artificial Intelligence
V. D. Ambeth Kumar, S. Sharmila, Abhishek Kumar, A. K. Bashir, Mamoon Rashid, Sachin Kumar Gupta, Waleed S. Alnumay
Summary: Postpartum haemorrhage (PPH) is a significant and potentially fatal complication of childbirth worldwide. This research proposes an automation system using wearable devices to predict the risk of PPH in pregnant women by measuring various parameters. Based on the predicted risk, medical attention is provided through an Internet of Things infrastructure.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ayesha Rashid, Muhammad Shoaib Farooq, Adnan Abid, Tariq Umer, Ali Kashif Bashir, Yousaf Bin Zikria
Summary: This article presents a systematic literature review of intention mining, a promising research area in data mining that aims to determine end-users' intentions. The analysis reveals eight prominent categories of intention, discusses the taxonomy of approaches and techniques used for intention mining, and explores six important types of datasets used in this field. Future challenges and research gaps are also presented.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Information Systems
Praveen Gorla, Mohammad Saif, Vinay Chamola, Biplab Sikdar, Mohsen Guizani
Summary: This article presents a novel machine learning-based framework for intelligent resource provisioning mechanisms for micro-grid connected green small cell base stations. By using prediction and energy flow control mechanisms, the article proposes an algorithmic implementation for redistribution of renewable resources, improving the resource management and traffic provisioning capability of small cell base stations.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Civil
Moayad Aloqaily, Haya Elayan, Mohsen Guizani
Summary: The advancement of wireless connectivity in smart cities enhances connections between key elements, and the federated intelligent health monitoring systems in autonomous vehicles contribute to improving quality of life. This study proposes C-HealthIER, a cooperative health intelligent emergency response system that monitors passengers' health and conducts cooperative behavior to reduce emergency treatment time and distance by sharing information between vehicles and infrastructure.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Peiran Li, Haoran Zhang, Wenjing Li, Keping Yu, Ali Kashif Bashir, Ahmad Ali Alzubi, Jinyu Chen, Xuan Song, Ryosuke Shibasaki
Summary: Tracking demographic dynamics is important for smart city development. We proposed a reliable approach based on the Industrial Internet of Things to track the demographic dynamics in the built environment, and inferred demographic data based on life-pattern features.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Ikram Ud Din, Aniqa Bano, Kamran Ahmad Awan, Ahmad Almogren, Ayman Altameem, Mohsen Guizani
Summary: The increasing usage of the Internet has improved the quality of trust in the Internet of Things (IoT). Trust plays a crucial role in providing a secure environment for users to share private information and enable easy and trustworthy data exchange among IoT devices. Trust management is essential for secure data transmission in a large-scale IoT network, and a lightweight approach called LightTrust is proposed to address security issues in Industrial IoT nodes. LightTrust utilizes a centralized trust agent to generate and manage trust certificates, and direct observations and recommendations are used to develop trust between nodes. Comparative simulations demonstrate the effectiveness and resilience of the proposed approach.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Hardware & Architecture
Qiaolun Zhang, Jun Wu, Michele Zanella, William Fornaciari, Wu Yang, Ali Kashif Bashir
Summary: In large-scale emergency scenarios, massive content is generated and transmitted in intelligent Internet of vehicular things (IIoVT). Existing IP-networks-based emergency systems suffer from inefficient content dissemination and high-latency response. Previous works fail to address trust issues, resulting in fake content and malicious emergency services. To overcome these challenges, we propose an emergent semantic-based information-centric fog system, which ensures trustworthy and intelligent emergency analysis and management. The proposed system achieves a short average semantic analyzing time and a low failure rate of emergency services.
IEEE CONSUMER ELECTRONICS MAGAZINE
(2023)
Article
Computer Science, Information Systems
Yuan Liu, Chuang Zhang, Yu Yan, Xin Zhou, Zhihong Tian, Jie Zhang
Summary: This study proposes a semi-centralized trust management system architecture based on blockchain to support various applications and services with massive IoT devices. The IoT devices are centralized organized by cloud servers, which maintain a rating data ledger within each domain using the proposed rotation-based consensus protocol. A computational trust model is proposed to identify and mitigate the influence of malicious devices by aggregating direct and indirect trust information. Simulation experiments and comparisons with classical models demonstrate the effectiveness of the proposed trust model in identifying and mitigating the influence of malicious devices.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Green & Sustainable Science & Technology
Rashid Abbasi, Ali Kashif Bashir, Alaa Omran Almagrabi, Md Belal Bin Heyat, Ge Yuan
Summary: Future sustainable energy-efficient computing solutions in e-healthcare, smart cities, and intelligent robotics applications benefit from the internet of things and cloud computing. Reversible Data Hiding in Encrypted Images (RDHEI) is being used in 6G technology for privacy protection. In this research, a sustainable, energy-efficient, multi-MSB-based dynamic quadtree partition with enhanced Huffman coding is proposed, resulting in optimum embedding capacity.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2023)
Article
Computer Science, Artificial Intelligence
Arshee Ahmed, Haroon Rasheed, Ali Kashif Bashir, Marwan Omar
Summary: This article proposes a comprehensive and tractable model for VANET using millimeter waves, which ensures ultra-high reliability in wireless transmission. The model combines Space-Time-Block-Coding (STBC) with Reed Solomon (RS) coding and outperforms IEEE 802.11bd, comparable to V2X NR. The simulation and numerical results demonstrate the accuracy of the proposed model.
PEERJ COMPUTER SCIENCE
(2023)
Article
Engineering, Civil
Cheng Qiao, Jing Qiu, Zhiyuan Tan, Geyong Min, Albert Y. Zomaya, Zhihong Tian
Summary: This paper studies the problem of performance evaluation in IoV and proposes a general approach to measure the performance of individual agents by exploring the common knowledge and correlation between different agents. Experimental results show that our evaluation scheme is efficient in these settings.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Shudong Li, Yuan Li, Xiaobo Wu, Sattam Al Otaibi, Zhihong Tian
Summary: In this paper, a malware family classification approach based on multimodal fusion and weight self-learning is proposed, which can efficiently identify and classify malware families, improving the efficiency of malware analysis in Intelligent Transportation Systems.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xujian Liang, Zhaoquan Gu, Yushun Xie, Le Wang, Zhihong Tian
Summary: Based on the assumption of isomorphism, approaches for generating high-quality, low-cost multilingual word embeddings have been critical for knowledge transfer. However, recent studies have shown limitations in these approaches, leading to stagnation in multilingual natural language processing. To address this, we propose MUSEDA, a framework for building multilingual word embeddings for domain transfer learning.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Xiaoyu Yi, Jun Wu, Gaolei Li, Ali Kashif Bashir, Jianhua Li, Ahmad Ali Alzubi
Summary: This article proposes a fast vulnerability detection mechanism based on recurrent semantic learning, which can detect vulnerabilities from binary codes of multiple programming languages, and ensure accuracy while maintaining high availability.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Telecommunications
Muhammad Ali Naeem, Yousaf Bin Zikria, Rashid Ali, Usman Tariq, Yahui Meng, Ali Kashif Bashir
Summary: This paper comprehensively discusses fog computing, Internet of Things (IoTs), and the issues of data security and dissemination in fog computing. Various caching schemes are proposed to address the problems in fog computing, and machine learning-based approaches for cache security and management are explored, as well as potential future research directions.
DIGITAL COMMUNICATIONS AND NETWORKS
(2023)
Article
Computer Science, Information Systems
Kashan Ahmed, Syed Khaldoon Khurshid, Sadaf Hina
Summary: This paper mainly introduces the construction of the cyber threat intelligence knowledge graph and the information extraction technique. By using joint extraction technique, it solves the problem of traditional techniques becoming ineffective due to the increasing size of CTI data. Experimental results show that this technique outperforms state-of-the-art models in knowledge triple extraction on CTI data and improves the F1 score.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Xinlong He, Yang Xu, Sicong Zhang, Weida Xu, Jiale Yan
Summary: This paper proposes a new membership inference attack method in federated learning, which utilizes data poisoning and sequence prediction confidence. The attack is effective and results in minimal overall model performance degradation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Tieming Chen, Huan Zeng, Mingqi Lv, Tiantian Zhu
Summary: In this paper, the authors propose a deep learning based dynamic malware detection method called CTIMD, which integrates threat knowledge from CTIs into the learning process of API call sequences with runtime parameters. Experimental results show that CTIMD outperforms existing methods in terms of performance.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wonwoo Choi, Minjae Seo, Seongman Lee, Brent Byunghoon Kang
Summary: This paper proposes SUM, a backward-edge control flow protection scheme for ARM Cortex-M processors. It combines MPU and the overlooked hardware feature FaultMask to achieve efficient and robust protection. The empirical evaluation shows minimal runtime overhead for the proposed solution.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Liliana Ribeiro, Ines Sousa Guedes, Carla Sofia Cardoso
Summary: Phishing susceptibility is influenced by individual and contextual factors. The study found that individuals who perceive themselves as capable of detecting phishing and those who use online services more frequently are more susceptible to phishing. However, technology competencies and other individual variables do not predict phishing susceptibility.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wenjie Wang, Yuanhai Shao, Yiju Wang
Summary: In this paper, we investigate the adversarial perturbations of twin support vector machines (TWSVMs) and propose an optimization framework, which provides explicit solutions to increase the interpretability of the conclusion and convenience for calculation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Snofy D. Dunston, V. Mary Anita Rajam
Summary: This paper proposes a novel adversarial attack technique that can synthesize adversarial images to mislead deep learning models, and also studies interpretability plots. The research findings show that the proposed attack technique influences the interpretability plots, regardless of the success of the attack.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Junchen Li, Guang Cheng, Zongyao Chen, Peng Zhao
Summary: Protocol Reverse Engineering (PRE) is a direct approach for analyzing unknown traffic. This paper proposes a method for clustering unknown traffic based on private protocol labels, and the experimental results demonstrate its advantages on real-world network traffic.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Rafal Kozik, Massimo Ficco, Aleksandra Pawlicka, Marek Pawlicki, Francesco Palmieri, Michal Choras
Summary: The inclusion of Explainability of Artificial Intelligence (xAI) has become a mandatory requirement for designing and implementing reliable, interpretable, and ethical AI solutions. However, it has been shown that xAI can enable successful adversarial attacks in the domain of fake news detection, leading to a decrease in AI security. This paper presents an attack scheme that uses an explainable solution to reshape the structure of the original message, allowing the adversary to manipulate the model's prediction while keeping the message's meaning intact.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Benyuan Yang, Lili Luo, Zhimeng Wang
Summary: Interoperation is widely used in practical industrial applications, but merging local access control policies may lead to security violations. Dealing with these issues in a multidomain environment is critical, but finding the maximum secure interoperation among individual systems poses a challenge due to the large number of entities and access involved.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Binghui Zou, Chunjie Cao, Longjuan Wang, Sizheng Fu, Tonghua Qiao, Jingzhang Sun
Summary: The ongoing struggle between security researchers and malware has led to the exploration of using convolutional neural networks and capsule networks for classification and identification of malware. However, training these networks requires a significant amount of data and parameters, and the research on capsule networks is still in its early stages, posing challenges.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Hongsong Chen, Xingyu Li, Wenmao Liu
Summary: Multivariate time-series anomaly detection is crucial for maintaining normal operation of physical equipment. Recent advances have been made in this field, but two challenges have limited the model's ability to generalize. To address these challenges, a multivariate time-series anomaly detection model consisting of a characterization network and a forecasting network is proposed. Experimental results demonstrate that this method outperforms baseline methods in terms of detection performance and robustness.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Roberto Doriguzzi-Corin, Domenico Siracusa
Summary: This paper discusses the application of federated learning in the field of cybersecurity and proposes an adaptive mechanism-based federated learning solution for DDoS attack detection in dynamic cybersecurity scenarios. Through experiments, it is demonstrated that the proposed solution outperforms state-of-the-art federated learning algorithms in terms of convergence time and accuracy.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Antonio Giovanni Schiavone
Summary: The usage of HTTPS protocol is crucial for secure communication with websites, ensuring the confidentiality, integrity, and authenticity of online data transmissions. The Municipality2HTTPS research project analyzed the implementation of HTTPS in Italian municipalities' websites and identified areas for improvement.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Domna Bilika, Nikoletta Michopoulou, Efthimios Alepis, Constantinos Patsakis
Summary: Voice Assistants (VAs) are widely used in smart devices, but are vulnerable to attacks, as shown by experiments with popular VAs revealing successful attack rates exceeding 30% and statistical variations among vendors, calling for additional countermeasures to protect user information.
COMPUTERS & SECURITY
(2024)
