Search Results (31)

The increasing deployment of internet of things(IoT) systems across critical domains has broadened the threat landscape, and being the catalyst for a variety of security concerns, including very stealthy slow denial of service (slow DoS) attacks. These exploit the hypertext transfer protocol’s (HTTP) application-layer protocol to either close down service requests or degrade responsiveness while closely mimicking legitimate traffic. Current available datasets fail to capture the more stealthy operational profiles of slow DoS attacks or account for the presence of genuine slow nodes (SN), which are devices experiencing high latency. These can significantly degrade detection accuracy since slow DoS attacks closely emulate SN. This paper addresses these problems by synthesising a realistic HTTP slow DoS dataset derived from a live IoT network, that incorporates both stealth-tuned slow DoS traffic and legitimate SN traffic, with the three main slow DoS variants of slow GET, slow Read, and slow POST being critically evaluated under these network conditions. A limited packet capture (LPC) strategy is adopted which focuses on just two metadata attributes, namely packet length ($lp$) and packet inter-arrival time ($\mathrm{\Delta }t$). Using a resource lightweight decision tree classifier, the proposed model achieves over 96% accuracy while incurring minimal computational overheads. Experimental results in a live IoT network reveal the negative classification impact of including SN traffic, thereby underscoring the importance of modelling stealthy attacks and SN latency in any slow DoS detection framework. Finally, a MPerf (Modelling Performance) is presented which quantifies and balances detection accuracy against processing costs to facilitate scalable deployment of low-cost detection models in resource-constrained IoT networks. This represents a practical solution to improving IoT resilience against stealthy slow DoS attacks whilst pragmatically balancing the resource-constraints of IoT nodes. By analysing the impact of SN on detection performance, a robust reliable model has been developed which can both measure and fine tune the accuracy-efficiency nexus. Full article

The pernicious impact of malicious Slow DoS (Denial of Service) attacks on the application layer and web-based Open Systems Interconnection model services like Hypertext Transfer Protocol (HTTP) has given impetus to a range of novel detection strategies, many of which use machine learning (ML) for computationally intensive full packet capture and post-event processing. In contrast, existing detection mechanisms, such as those found in various approaches including ML, artificial intelligence, and neural networks neither facilitate real-time detection nor consider the computational overhead within resource-constrained Internet of Things (IoT) networks. Slow DoS attacks are notoriously difficult to reliably identify, as they masquerade as legitimate application layer traffic, often resembling nodes with slow or intermittent connectivity. This means they often evade detection mechanisms because they appear as genuine node activity, which increases the likelihood of mistakenly being granted access by intrusion-detection systems. The original contribution of this paper is an innovative Guardian Node (GN) Slow DoS detection model, which analyses the two key network attributes of packet length and packet delta time in real time within a live IoT network. By designing the GN to operate within a narrow window of packet length and delta time values, accurate detection of all three main Slow DoS variants is achieved, even under the stealthiest malicious attack conditions. A unique feature of the GN model is its ability to reliably discriminate Slow DoS attack traffic from both genuine and slow nodes experiencing high latency or poor connectivity. A rigorous critical evaluation has consistently validated high, real-time detection accuracies of more than 98% for the GN model across a range of demanding traffic profiles. This performance is analogous to existing ML approaches, whilst being significantly more resource efficient, with computational and storage overheads being over 96% lower than full packet capture techniques, so it represents a very attractive alternative for deployment in resource-scarce IoT environments. Full article

This study presents a cost-effective IoT-based Supervisory Control and Data Acquisition system for the real-time monitoring and control of photovoltaic systems in a rural Pakistani community. The system utilizes the Blynk platform with Arduino Nano, GSM SIM800L, and ESP-32 microcontrollers. The key components include a ZMPT101B voltage sensor, ACS712 current sensors, and a Maximum Power Point Tracking module for optimizing power output. The system operates over both Global System for Mobile Communications and Wi-Fi networks, employing universal asynchronous receiver–transmitter serial communication and using the transmission control protocol/Internet protocol and hypertext transfer protocol for data exchange. Testing showed that the system consumes only 3.462 W of power, making it highly efficient. With an implementation cost of CAD 35.52, it offers an affordable solution for rural areas. The system achieved an average data transmission latency of less than 2 s over Wi-Fi and less than 5 s over GSM, ensuring timely data updates and control. The Blynk 2.0 app provides data retention capabilities, allowing users to access historical data for performance analysis and optimization. This open-source SCADA system demonstrates significant potential for improving efficiency and user engagement in renewable energy management, offering a scalable solution for global applications. Full article

In phishing attack detection, machine learning-based approaches are more effective than simple blacklisting strategies, as they can adapt to new types of attacks and do not require manual updates. However, for these approaches, the choice of features and classifiers directly influences detection performance. Therefore, in this work, the contributions of various features and classifiers to detecting phishing attacks were thoroughly analyzed to find the best classifier and feature set in terms of different performance metrics including accuracy, precision, recall, F1-score, and classification time. For this purpose, a brand-new phishing dataset was prepared and made publicly available. Using an exhaustive strategy, every combination of the feature groups was fed into various classifiers to detect phishing websites. Two existing benchmark datasets were also used in addition to ours for further analysis. The experimental results revealed that the features based on the uniform resource locator (URL) and hypertext transfer protocol (HTTP), rather than all features, offered the best performance. Also, the decision tree classifier surpassed the others, achieving an F1-score of 0.99 and being one of the fastest classifiers overall. Full article

Standardization and collaborative integration are fundamental for the implementation of Industry 4.0. The Open Communication Platform Unified Architecture (OPC UA) standard plays a crucial role in communication by enabling the development of heterogeneous systems and facilitating the seamless exchange of data between devices. To take full advantage of OPC UA’s capabilities, it is necessary to unlock other application services, such as cloud computing. By having more tools at their disposal, industries can increase their efficiency and optimize data-driven decision making. In this study, it was proposed to use OPC UA standard in a Client/Server model to leverage OPC UA paradigms along with Software Development Kits (SDKs). The development was carried out using Python as the programming language, both to host the server on a Raspberry Pi 4B and to set up the client on a Personal Computer (PC). This client will centralize the data for sending to the Clever Cloud logging platforms and for web visualization on Tago.IO. The interoperability problem of communicating between two platforms with different Operating Systems (OS) was addressed by integrating the OPC UA standard. The configured interval of 10 s recorded an average of 11.11 s, with a maximum timeout of 59 s for data-logging temperature, pressure, humidity, and altitude. The client proved to be Hypertext Transfer Protocol Secure (HTTPS) compliant, allowing connection to Web platforms. Full article

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users’ hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased. Full article

Digitalization of our economy and society has ushered in notable productivity increases but has also exposed more of our infrastructures and systems to cyberattacks. This trend is exacerbated by the proliferation of poorly designed Internet of Things (IoT) devices and cloud services, which often lack appropriate security measures, either due to bugs or configuration mistakes. In this article, we propose, validate, and critically evaluate a flexible honeypot system based on the Hypertext Transfer Protocol (HTTP) that can mimic any HTTP-based service and application. This covers a large share of IoT devices, including black box devices with no software or firmware available for emulation, as well as cloud- and web-based services. We validate the system by implementing 14 services and by running a 4-month experiment, collecting data from attackers. We propose a novel data enrichment mechanism for identifying internet scanning services, as well as several other data collection and enrichment approaches. Finally, we present some results and visualizations of the data collection experiment, demonstrating possible applications and future use cases, as well as potential drawbacks of such systems. Full article

The Hypertext Transfer Protocol (HTTP) is a common target of distributed denial-of-service (DDoS) attacks in today’s cloud computing environment (CCE). However, most existing datasets for Intrusion Detection System (IDS) evaluations are not suitable for CCEs. They are either self-generated or are not representative of CCEs, leading to high false alarm rates when used in real CCEs. Moreover, many datasets are inaccessible due to privacy and copyright issues. Therefore, we propose a publicly available benchmark dataset of HTTP-GET flood DDoS attacks on CCEs based on an actual private CCE. The proposed dataset has two advantages: (1) it uses CCE-based features, and (2) it meets the criteria for trustworthy and valid datasets. These advantages enable reliable IDS evaluations, tuning, and comparisons. Furthermore, the dataset includes both internal and external HTTP-GET flood DDoS attacks on CCEs. This dataset can facilitate research in the field and enhance CCE security against DDoS attacks. Full article

IoT platforms for the transportation industry are portable with limited battery life and need real-time and long-term monitoring operations. Since MQTT and HTTP are widely used as the main communication protocols in the IoT, it is imperative to analyze their power consumption to provide quantitative results that help maximize battery life in IoT transportation systems. Although is well known that MQTT consumes less power than HTTP, a comparative analysis of their power consumption with long-time tests and different conditions has not yet been conducted. In this sense, a design and validation of an electronic cost-efficient platform system for remote real-time monitoring is proposed using a NodeMCU module, in which experimentation is carried out for HTTP and MQTT with different QoS levels to make a comparison and demonstrate the differences in power consumption. Furthermore, we characterize the behavior of the batteries in the systems and compare the theoretical analysis with real long-time test results. The experimentation using the MQTT protocol with QoS 0 and 1 was successful, resulting in power savings of 6.03% and 8.33%, respectively, compared with HTTP, demonstrating many more hours in the duration of the batteries, which could be very useful in technological solutions for the transport industry. Full article

In this research paper, the spatial distributions of five different services—Voice over Internet Protocol (VoIP), Video Conferencing (VC), Hypertext Transfer Protocol (HTTP), and Electronic Mail—are investigated using three different approaches: circular, random, and uniform approaches. The amount of each service varies from one to another. In certain distinct settings, which are collectively referred to as mixed applications, a variety of services are activated and configured at predetermined percentages. These services run simultaneously. Furthermore, this paper has established a new algorithm to assess both the real-time and best-effort services of the various IEEE 802.11 technologies, describing the best networking architecture as either a Basic Service Set (BSS), an Extended Service Set (ESS), or an Independent Basic Service Set (IBSS). Due to this fact, the purpose of our research is to provide the user or client with an analysis that suggests a suitable technology and network configuration without wasting resources on unnecessary technologies or requiring a complete re-setup. In this context, this paper presents a network prioritization framework for enabling smart environments to determine an appropriate WLAN standard or a combination of standards that best supports a specific set of smart network applications in a specified environment. A network QoS modeling technique for smart services has been derived for assessing best-effort HTTP and FTP, and the real-time performance of VoIP and VC services enabled via IEEE 802.11 protocols in order to discover more optimal network architecture. A number of IEEE 802.11 technologies have been ranked by using the proposed network optimization technique with separate case studies for the circular, random, and uniform geographical distributions of smart services. The performance of the proposed framework is validated using a realistic smart environment simulation setting, considering both real-time and best-effort services as case studies with a range of metrics related to smart environments. Full article

Although alcohol consumption may produce effects that can be beneficial or harmful, alcohol consumption prevails among communities around the globe. Additionally, alcohol consumption patterns may be associated with several factors among communities and individuals. Numerous technologies and methods are implemented to enhance the detection and tracking of alcohol consumption, such as vehicle-integrated and wearable devices. In this paper, we present a cellular-based Internet of Things (IoT) implementation in a breath analyzer to enable data collection from multiple users via a single device. Cellular technology using hypertext transfer protocol (HTTP) was implemented as an IoT gateway. IoT integration enabled the direct retrieval of information from a database relative to the device and direct upload of data from the device onto the database. A manually developed threshold algorithm was implemented to quantify alcohol concentrations within a range from 0 to 200 mcg/100 mL breath alcohol content using electrochemical reactions in a fuel-cell sensor. Two data collections were performed: one was used for the development of the model and was split into two sets for model development and on-machine validation, and another was used as an experimental verification test. An overall accuracy of 98.16% was achieved, and relative standard deviations within the range from 1.41% to 2.69% were achieved, indicating the reliable repeatability of the results. The implication of this paper is that the developed device (an IoT-integrated breath analyzer) may provide practical assistance for healthcare representatives and researchers when conducting studies involving the detection and data collection of alcohol consumption patterns. Full article

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client–server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video. Full article

To provide a low-cost methodical way for inference-driven insight into the assessment of SDN operations, a behavioral study of key network parameters that predicate the proper functioning and performance of software-defined networks (SDNs) is presented to characterize their alterations or variations, given various emulated SDN scenarios. It is standard practice to use simulation environments to investigate the performance characteristics of SDNs, quantitatively and qualitatively; hence, the use of emulated scenarios to typify the investigated SDN in this paper. The key parameters studied analytically are the jitter, response time and throughput of the SDN. These network parameters provide the most vital metrics in SDN operations according to literature, and they have been behaviorally studied in the following popular SDN states: normal operating condition without any incidents on the SDN, hypertext transfer protocol (HTTP) flooding, transmission control protocol (TCP) flooding, and user datagram protocol (UDP) flooding, when the SDN is subjected to a distributed denial-of-service (DDoS) attack. The behavioral study is implemented primarily via univariate and multivariate exploratory data analysis (EDA) to characterize and visualize the variations of the SDN parameters for each of the emulated scenarios, and linear regression-based analysis to draw inferences on the sensitivity of the SDN parameters to the emulated scenarios. Experimental results indicate that the SDN performance metrics (i.e., jitter, latency and throughput) vary as the SDN scenario changes given a DDoS attack on the SDN, and they are all sensitive to the respective attack scenarios with some level of interactions between them. Full article

Artificial intelligence (AI) has brought lots of excitement to our day-to-day lives. Some examples are spam email detection, language translation, etc. Baby monitoring devices are being used to send video data of the baby to the caregiver’s smartphone. However, the automatic understanding of the data was not implemented in most of these devices. In this research, AI and image processing techniques were developed to automatically recognize unwanted situations that the baby was in. The monitoring device automatically detected: (a) whether the baby’s face was covered due to sleeping on the stomach; (b) whether the baby threw off the blanket from the body; (c) whether the baby was moving frequently; (d) whether the baby’s eyes were opened due to awakening. The device sent notifications and generated alerts to the caregiver’s smartphone whenever one or more of these situations occurred. Thus, the caregivers were not required to monitor the baby at regular intervals. They were notified when their attention was required. The device was developed using NVIDIA’s Jetson Nano microcontroller. A night vision camera and Wi-Fi connectivity were interfaced. Deep learning models for pose detection, face and landmark detection were implemented in the microcontroller. A prototype of the monitoring device and the smartphone app were developed and tested successfully for different scenarios. Compared with general baby monitors, the proposed device gives more peace of mind to the caregivers by automatically detecting un-wanted situations. Full article

The amazing fusion of the internet of things (IoT) into traditional health monitoring systems has produced remarkable advances in the field of e-health. Different wireless body area network devices and sensors are providing real-time health monitoring services. As the number of IoT devices is rapidly booming, technological and security challenges are also rising day by day. The data generated from sensor-based devices need confidentiality, integrity, authenticity, and end-to-end security for safe communication over the public network. IoT-based health monitoring systems work in a layered manner, comprising a perception layer, a network layer, and an application layer. Each layer has some security, and privacy concerns that need to be addressed accordingly. A lot of research has been conducted to resolve these security issues in different domains of IoT. Several frameworks for the security of IoT-based e-health systems have also been developed. This paper introduces a security framework for real-time health monitoring systems to ensure data confidentiality, integrity, and authenticity by using two common IoT protocols, namely constrained application protocol (CoAP) and message query telemetry transports (MQTT). This security framework aims to defend sensor data against the security loopholes while it is continuously transmitting over the layers and uses hypertext transfer protocols (HTTPs) for this purpose. As a result, it shields from the breach with a very low ratio of risk. The methodology of this paper focuses on how the security framework of IoT-based real-time health systems is protected under the tiers of CoAP and HTTPs. CoAP works alongside HTTPs and is responsible for providing end-to-end security solutions. Full article