Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item DDoS attacks at the application layer: Challenges and research perspectives for safeguarding web applications(Institute of Electrical and Electronics Engineers Inc., 2019) Praseed, A.; Santhi Thilagam, P.S.Distributed denial of service (DDoS) attacks are some of the most devastating attacks against Web applications. A large number of these attacks aim to exhaust the network bandwidth of the server, and are called network layer DDoS attacks. They are volumetric attacks and rely on a large volume of network layer packets to throttle the bandwidth. However, as time passed, network infrastructure became more robust and defenses against network layer attacks also became more advanced. Recently, DDoS attacks have started targeting the application layer. Unlike network layer attacks, these attacks can be carried out with a relatively low attack volume. They also utilize legitimate application layer requests, which makes it difficult for existing defense mechanisms to detect them. These attacks target a wide variety of resources at the application layer and can bring a server down much faster, and with much more stealth, than network layer DDoS attacks. Over the past decade, research on application layer DDoS attacks has focused on a few classes of these attacks. This paper attempts to explore the entire spectrum of application layer DDoS attacks using critical features that aid in understanding how these attacks can be executed. defense mechanisms against the different classes of attacks are also discussed with special emphasis on the features that aid in the detection of different classes of attacks. Such a discussion is expected to help researchers understand why a particular group of features are useful in detecting a particular class of attacks. © 2018 IEEE.Item Image Analysis of Nuclei Histopathology Using Deep Learning: A Review of Segmentation, Detection, and Classification(Springer, 2023) Kadaskar, M.; Patil, N.Deep learning has recently advanced in its applicability to computer vision challenges, and medical imaging has become the most used technique in histopathology image analysis. Nuclei instance segmentation, detection, and classification are one such task. Reliable analysis of these image slides is critical in cancer identification, treatment, and care. Researchers have recently been interested in this issue. This study reviews the categorization and investigation of strategies utilized in recent works to improve the effectiveness of automated nuclei segmentation, detection, and classification in histopathology images. It critically examines state-of-the-art deep learning techniques, analyzes the trends, identifies the challenges, and highlights and helps with the future directions for research. The taxonomy includes deep learning techniques, enhancement, and optimization methods. The survey findings will help to overcome the challenges of nuclei segmentation, detection, and classification while improving the performance of models and, thus, aid future research plans. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item Machine learning techniques for periodontitis and dental caries detection: A narrative review(Elsevier Ireland Ltd, 2023) Radha, R.C.; Raghavendra, B.S.; Subhash, B.V.; Rajan, J.; Narasimhadhan, A.V.Objectives: In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing. Methods: An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Results: The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture. Conclusion: While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction. © 2023 Elsevier B.V.Item Fuzzy Request Set Modelling for Detecting Multiplexed Asymmetric DDoS Attacks on HTTP/2 servers(Elsevier Ltd, 2021) Praseed, A.; Santhi Thilagam, P.S.The introduction of HTTP/2 has led to a dramatic change in web traffic. The steady flow of requests in HTTP/1.1 has been replaced by bursts of multiple requests, largely due to the introduction of multiplexing in HTTP/2 which allows users to send multiple requests through a single connection. This feature was introduced in order to reduce the page loading time by multiplexing a web page and its associated resources in a single connection. While this feature has significantly improved user experience, it can be misused to launch sophisticated application layer DDoS attacks against HTTP/2 servers. Instead of the intended use of multiplexing, attackers can force the web server to process multiple random requests simultaneously, leading to increased server usage. The use of computationally intensive requests can further exacerbate the situation. These attacks, called Multiplexed Asymmetric Attacks, pose a dangerous threat to HTTP/2 servers and stem from the lack of verification of the multiplexed requests. In this work, an approach to model an HTTP/2 request set as a fuzzy multiset is presented. The proposed approach uses a combination of relative cardinality and request workload to detect multiplexed AL-DDoS attacks. Experiments on open source datasets demonstrate that the proposed approach is able to detect multiplexed AL-DDoS attacks with an accuracy of around 95%, while maintaining a low False Positive Rate (FPR) of around 3%. © 2021 Elsevier LtdItem Securing the IoT Application Layer from an MQTT Protocol Perspective: Challenges and Research Prospects(Institute of Electrical and Electronics Engineers Inc., 2024) Lakshminarayana, S.; Praseed, A.; Santhi Thilagam, P.S.The Internet of Things (IoT) is one of the most promising new millennial technologies, having numerous applications in our surrounding environment. The fundamental goal of an IoT system is to ensure effective communication between users and their devices, which is accomplished through the application layer of IoT. For this reason, the security of protocols employed at the IoT application layer are extremely significant. Message Queuing Telemetry Transport (MQTT) is being widely adopted as the application layer protocol for resource-constrained IoT devices. The reason for the widespread usage of the MQTT protocol in IoT devices is its highly appealing features, such as packet-agnostic communication, high scalability, low power consumption, low implementation cost, fast and reliable message delivery. These capabilities of the MQTT protocol make it a potential and viable target for adversaries. Therefore, we initially emphasize on the emerging MQTT vulnerabilities and provide a classification of identified MQTT vulnerabilities for the IoT paradigm. Then, this paper reviews attacks against the MQTT protocol and the corresponding defense mechanisms for MQTT-based IoT deployments. Furthermore, MQTT attacks are categorized and investigated with reference to crucial characteristics that aid in comprehending how these attacks are carried out. The defense mechanisms are discussed in detail, with a particular focus on techniques for identifying vulnerabilities, detecting and preventing attacks against the MQTT protocol. This work also discloses lessons learned by identifying and providing insightful findings, open challenges, and future research directions. Such a discussion is anticipated to propel more research efforts in this burgeoning area and pave a secure path toward expanding and fully realizing the MQTT protocol in IoT technology. © 2024 IEEE.Item Deep learning-based arecanut detection for X-ray radiography: improving performance and efficiency for automated classification and quality control(Taylor and Francis Ltd., 2025) Naik, P.M.; Rudra, B.X-ray radiography is a valuable, non-destructive tool and can be used to examine the internal components or quality attributes of agricultural commodities, including arecanut. The true quality of an arecanut can be determined using destructive methods through visual inspection. However, dissected arecanuts do not have a shelf life. There is no non-destructive method available for grading arecanuts. We employ X-ray imaging as an aid to conduct internal examinations of arecanuts, allowing for thorough inspection without causing damage. A custom X-ray image dataset of arecanuts is created for automated interpretation of grades. We developed a hybrid arecanut grading model using YOLOv5s architecture incorporating the Stem, the GhostNet and the Transformer blocks. The proposed hybrid architecture outperforms in comparison with state-of-the-art models with a mean average precision (mAP) of 97.30%. The proposed 9.5 MB lightweight model can easily fit into X-ray devices, making it ideal for detecting arecanut grades in the industry. This method could transform the standards of quality inspection for arecanut. Its incorporation could establish a new industry benchmark for unparalleled quality assessment using X-ray technology as a non-destructive tool. © 2024 Informa UK Limited, trading as Taylor & Francis Group.Item All-printed WO3 films on an Ag-interdigitated electrode derived from aqueous screen-printable inks for room-temperature ammonia gas detection(Institute of Physics, 2025) Praveen, L.L.; Singh, N.P.; Vardhan, R.V.; Mandal, S.In this work, all-printed tungsten oxide (WO3) sensors were fabricated from nanoparticle-based screen-printable inks, where the WO3 nanopowders were hydrothermally synthesized with various HCl concentrations to give enhanced room-temperature detection of ammonia (NH3) gas. The monoclinic phase of WC powders (calcined WO3) with square nanoplate-like morphology and porosities was identified from x-ray diffraction, field-emission scanning electron microscopy and Brunauer-Emmett-Teller surface area analysis. The silver precursor ink-derived interdigitated electrodes were found to be crystalline with an average finger width and Ag film thickness of 1 ± 0.4 mm and 3.8 ± 0.5 µm, respectively. The formulated WO3 inks with hydroxyethyl cellulose showed a thixotropic fluid-like behavior and exhibited a viscosity of ?9 × 104 mPa s, which is a key requirement for screen printing. Rheological study of the formulated WC inks revealed a thixotropic nature, with all WC inks showing a viscosity of 85 ± 3 Pa s and a recovery rate of 80% in the recovery stage. This work explains the role of pH in hydrothermally synthesis of WO3 by correlating the gas-sensing characteristics of the screen-printed sensors fabricated from formulated inks, where the WC-15 gas sensor showed a maximum gas response of ?340 towards 100 ppm of NH3 gas. This facile and cost-effective method for fabricating chemiresistive gas sensors could pave the way for the development of flexible and printable devices for ppb-level detection of NH3 gas and its monitoring. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.