Article
Engineering, Civil
Yuanzhe Wang, Qipeng Liu, Ehsan Mihankhah, Chen Lv, Danwei Wang
Summary: This paper explores the cybersecurity challenges faced by autonomous vehicles when under sensor attacks. A model-based framework is proposed to detect attacks and identify their sources for secure localization. Sensor redundancy and a bank of attack detectors are utilized to ensure robustness against cyber-attacks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Abdullah Emir Cil, Kazim Yildiz, Ali Buldu
Summary: It is suggested to use the deep neural network (DNN) as a deep learning model for detecting DDoS attacks, and experiments have shown that the model can detect and classify attacks in network traffic with high accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Ali Kazemy, Ramasamy Saravanakumar, James Lam
Summary: This paper discusses the master-slave synchronization issue of neural networks under mixed-type communication attacks, proposing a synchronization strategy based on static output feedback controller and event-triggered scheme, and investigating various types of network attacks in a unified Markovian jump framework. Design criteria are derived using Lyapunov-Krasovskii theory and stochastic analysis techniques, formulated as matrix inequalities, and a convex optimization algorithm is proposed to design the static output feedback controller. Finally, the effectiveness of the event-triggered static output feedback controller is demonstrated through two chaotic examples.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Tianyu Du, Shouling Ji, Bo Wang, Sirui He, Jinfeng Li, Bo Li, Tao Wei, Yunhan Jia, Raheem Beyah, Ting Wang
Summary: This paper presents DetectSec, a platform for analyzing the robustness of object detection models. It conducts a thorough evaluation of adversarial attacks on 18 standard object detection models and compares the effectiveness of different defense strategies. The findings highlight the differences between adversarial attacks and defenses in object detection tasks compared to image classification tasks, and provide insights for understanding and defending against such attacks.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Zainab Alshingiti, Rabeah Alaqel, Jalal Al-Muhtadi, Qazi Emad Ul Haq, Kashif Saleem, Muhammad Hamza Faheem
Summary: When it comes to Internet and communication, security is a major challenge. Phishing is the most common form of attack, where attackers aim to steal personal information such as account details, passwords, and credit card information. Phishers gather user information through mimicking legitimate websites, putting users at risk of financial harm and identity theft. Therefore, developing an efficient system to detect phishing websites is crucial. This paper proposes three deep learning-based techniques, including LSTM, CNN, and LSTM-CNN, to identify phishing websites. Experimental results show high accuracy, with CNN-based system demonstrating superiority.
Article
Computer Science, Artificial Intelligence
Rasim Alguliyev, Yadigar Imamverdiyev, Lyudmila Sukhostat
Summary: The urgency of ensuring the security of cyber-physical systems lies in their correct functioning, which has a significant impact on various industrial sectors. This paper proposes a deep hybrid model based on three parallel neural architectures and experiments show its superiority over recent works using machine learning techniques.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Hanan Zainel, Cemal Kocak
Summary: In this research, a well-trained intrusion detection system using neural networks is developed to detect attacks in LANs and other computer networks that use data. The study finds that utilizing convolutional neural networks is an effective strategy for identifying network intrusions.
APPLIED SCIENCES-BASEL
(2022)
Review
Computer Science, Artificial Intelligence
Jasleen Kaur, Urvashi Garg, Gourav Bathla
Summary: With the increase in demand for E-commerce and Social Networking websites, it is crucial to develop secure protocols to protect Internet users' privacy and security. While traditional encryption techniques and attack detection protocols can secure data transmission, hackers can easily exploit them to gain access to sensitive information. This paper surveys recent developments in Cross-Site Scripting (XSS) attacks and techniques used to secure confidential information. XSS has been recognized as a significant online application security risk, and addressing this flaw is essential for personal and financial safety. The paper presents an overview of machine learning and neural network-based XSS attack detection techniques, discusses research gaps, challenges, and provides future directions for improving XSS attack detection techniques.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Automation & Control Systems
Yihao Wan, Tomislav Dragicevic
Summary: In this paper, a data-driven cyber-attack detection method for islanded dc microgrids is proposed. The method collects data by monitoring the behavior of an intelligent attacker who can bypass conventional detection algorithms. A reinforcement learning algorithm is used to emulate the attacker's actions, who exploits the vulnerability of index-based detection methods. The collected data is then used to train a neural-network-based detector that complements the conventional method.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Information Systems
Olorunjube James Falana, Adesina Simon Sodiya, Saidat Adebukola Onashoga, Biodun Surajudeen Badmus
Summary: Recent outbreaks of pandemics have led to an increase in cyberattacks caused by malware. This study proposes a novel ensemble technique, called Mal-Detect, which combines Deep Convolutional Neural Network and Deep Generative Adversarial Neural Network to analyze, detect, and categorize malware. Experimental results demonstrate that Mal-Detect outperforms other state-of-the-art techniques with an accuracy of 99.8% in detecting malware.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Jiedong Feng, Yaqin Sun, Kefei Zhang, Yindi Zhao, Yi Ren, Yu Chen, Huifu Zhuang, Shuo Chen
Summary: This study develops an automatic method for detecting maize pests using digital technology and deep learning techniques in precision agriculture, and verifies the accuracy and robustness of the method.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Xiaodan Yan, Ke Yan, Meezan Ur Rehman, Sami Ullah
Summary: Smart health systems integrate sensor technology with the Internet of Things to monitor patients. However, communication between edge nodes and mobile users in mobile edge computing can be vulnerable to impersonation attacks. In this study, we propose a reinforcement learning approach for detecting impersonation attacks in medical and healthcare services, which outperforms traditional techniques in dynamic environments.
APPLIED SCIENCES-BASEL
(2022)
Article
Construction & Building Technology
Elyas Asadi Shamsabadi, Chang Xu, Aravinda S. Rao, Tuan Nguyen, Tuan Ngo, Daniel Dias-da-Costa
Summary: This research proposes a ViT-based framework for crack detection on asphalt and concrete surfaces, achieving enhanced real-world crack segmentation performance through transfer learning and IoU loss function. Compared to CNN-based models, TransUNet with a CNN-ViT backbone shows better average IoU on small and multiscale crack semantics and ViT helps the encoder-decoder network exhibit robust performance against various noisy signals.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Chemistry, Analytical
Lorenzo Canese, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Hamed Famil Ghadakchi, Marco Re, Sergio Spano
Summary: Traffic sign detection systems are crucial for autonomous driving and driver safety and assistance. This study compares multiple learning systems under the same conditions and finds that Vgg 16_bn, Vgg19_bn, and AlexNet perform the best for detecting traffic signs.
Article
Mathematics
Abdulaziz Almalaq, Saleh Albadran, Mohamed A. Mohamed
Summary: This study proposes an attack detection model for energy systems based on deep learning, which can efficiently manage energy supply and consumption while avoiding security risks.
Article
Computer Science, Artificial Intelligence
Bhuvaneswari N. G. Amma, S. Selvakumar
Article
Computer Science, Information Systems
N. G. Bhuvaneswari Amma, S. Selvakumar
Summary: The paper proposes a class center based triangle area vector (CCTAV) method for DoS attack detection, which reduces the complexity of feature extraction and enhances attack detection accuracy by computing the mean of target classes and extracting correlations between features. The proposed method is evaluated using tenfold cross validation and demonstrates significant results compared to existing attack detection methods.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Theory & Methods
Bhuvaneswari N. G. Amma, S. Selvakumar
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Computer Science, Information Systems
N. G. Bhuvaneswari Amma, S. Selvakumar, R. Leela Velusamy
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2020)
Article
Engineering, Electrical & Electronic
Bhuvaneswari N. G. Amma, Jitaksh Kapoor
Summary: Security of data is crucial in our digitized world, and a novel approach using encryption and LSB embedding to hide sensitive information without loss of quality has been proposed.
IETE JOURNAL OF RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
N. G. Bhuvaneswari Amma, S. Selvakumar
Summary: This article introduces an optimized deep neural network structure for detecting DDoS attacks, using the CuI optimization technique. Experimental results show that this optimization method outperforms existing techniques and achieves significant performance improvement.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Nageswari N. G. Amma, Bhuvaneswari N. G. Amma
Summary: The text discusses an authentication mechanism based on fingerprint biometrics, optimizing the extraction of unique points using partition clustering for higher uniqueness and security. The proposed mechanism, PClusBA, uses a cluster count of eight to achieve a 192-bit UID in accordance with NIST standards, and was found to provide unique results for each image tested.
IETE JOURNAL OF RESEARCH
(2022)
Article
Computer Science, Information Systems
Bhuvaneswari Amma Narayanavadivoo Gopinathan, Velliangiri Sarveshwaran, Vinayakumar Ravi, Rajasekhar Chaganti
Summary: This article suggests a method for anomaly detection in IoT networks to protect smart devices from cyberattacks. By selecting an optimal set of IoT traffic features and using a learning algorithm for classification, efficient detection at the edge of the IoT network can be achieved. The proposed approach utilizes a layered paddy crop optimization algorithm for feature selection and employs a capsule network for labeling traffic as normal or attack. The experimental results demonstrate the effectiveness of the proposed strategy, outperforming existing base classifiers and feature selection approaches.
Article
Computer Science, Information Systems
N. G. Bhuvaneswari Amma, Vikrant Rajput
Summary: Nowadays, autonomous vehicles are evolving with the advancements in cutting edge technologies. Traffic recognition system is required to efficiently recognize traffic signals. It consists of sign detection and classification. The proposed approach utilizes a support vector machine based fast detection module to detect traffic signs into different traffic classes, and deep convolutional neural networks for further classification into subclasses. Experimental results on benchmark traffic sign image datasets demonstrate that the proposed approach significantly improves traffic sign recognition accuracy compared to state-of-the-art systems.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
S. Velliangiri, N. G. Bhuvaneswari Amma, Nam-Kyun Baik
Summary: This paper proposes a statistical method for identifying DoS attacks in smart city networks, and develops a DoS attack detection model with low computational complexity and low false positive rate. Using smart city network traffic data set and feature distance map method for statistical analysis, this approach enhances the accuracy of attack detection.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Proceedings Paper
Computer Science, Information Systems
N. G. Bhuvaneswari Amma, P. Valarmathi
Summary: The evolution of technology has led to an increase in cyberattacks on IoT devices. Statistical methods can be used to detect intrusions in IoT traffic, but current techniques suffer from the curse of dimensionality. To address this issue, a method called IoT Intrusion Detection (IoTInDet) is proposed, which identifies intrusions by selecting relevant features and calculating their correlation.
INFORMATION SYSTEMS SECURITY, ICISS 2022
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
N. G. Bhuvaneswari Amma, S. Selvakumar
COMPUTATIONAL VISION AND BIO-INSPIRED COMPUTING
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Narayanavadivoo Gopinathan Bhuvaneswari Amma, Selvakumar Subramanian
PROCEEDINGS OF TENCON 2018 - 2018 IEEE REGION 10 CONFERENCE
(2018)
Proceedings Paper
Green & Sustainable Science & Technology
K. Shankari, N. G. Bhuvaneswari Amma
PROCEEDINGS OF 2015 ONLINE INTERNATIONAL CONFERENCE ON GREEN ENGINEERING AND TECHNOLOGIES (IC-GET)
(2015)