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The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy. [ABSTRACT FROM AUTHOR]

The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhole and greyhole attacks pose significant risks to the availability and integrity of CPSs. The current detection and mitigation approaches often struggle to accurately differentiate between legitimate and malicious behavior, leading to ineffective protection. This paper introduces Gini-index and blockchain-based Blackhole/Greyhole RPL (GBG-RPL), a novel technique designed for efficient detection and mitigation of blackhole and greyhole attacks in smart health monitoring CPSs. GBG-RPL leverages the analytical prowess of the Gini index and the security advantages of blockchain technology to protect these systems against sophisticated threats. This research not only focuses on identifying anomalous activities but also proposes a resilient framework that ensures the integrity and reliability of the monitored data. GBG-RPL achieves notable improvements as compared to another state-of-the-art technique referred to as BCPS-RPL, including a 7.18% reduction in packet loss ratio, an 11.97% enhancement in residual energy utilization, and a 19.27% decrease in energy consumption. Its security features are also very effective, boasting a 10.65% improvement in attack-detection rate and an 18.88% faster average attack-detection time. GBG-RPL optimizes network management by exhibiting a 21.65% reduction in message overhead and a 28.34% decrease in end-to-end delay, thus showing its potential for enhanced reliability, efficiency, and security. [ABSTRACT FROM AUTHOR]

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature. [ABSTRACT FROM AUTHOR]

Nowadays, electric load forecasting through a data analytic approach has become one of the most active and emerging research areas. It provides future consumption patterns of electric load. Since there are large fluctuations in both electricity production and use, it is a difficult task to achieve a balance between electric load and demand. By analyzing past electric consumption records to estimate the upcoming electricity load, the issue of fluctuating behavior can be resolved. In this study, a framework for feature selection, extraction, and regression is put forward to carry out the electric load prediction. The feature selection phase uses a combination of extreme gradient boosting (XGB) and random forest (RF) to determine the significance of each feature. Redundant features in the feature extraction approach are removed by applying recursive feature elimination (RFE). We propose an enhanced support vector machine (ESVM) and an enhanced convolutional neural network (ECNN) for the regression component. Hyperparameters of both the proposed approaches are set using the random search (RS) technique. To illustrate the effectiveness of our proposed strategies, a comparison is also performed between the state-of-the-art approaches and our proposed techniques. In addition, we perform statistical analyses to prove the significance of our proposed approaches. Simulation findings illustrate that our proposed approaches ECNN and ESVM achieve higher accuracies of 98.83% and 98.7%, respectively. [ABSTRACT FROM AUTHOR]

Agricultural productivity plays a vital role in a country's economy, which can be increased by providing the proper water needed for crops. Proper water provision ensures suitable moisture and appropriate conditions essential for crops, water resource preservation, minimized water wastage, and energy consumption. However, adequate water provision is challenging due to intermittent and uncertain environmental and weather conditions. On this note, a model with uncertain and stochastic conditions (rain, wet, dry, humidity, and moisture) capturing abilities is needed. Thus, a smart-sensors-based solar-powered system is developed for monitoring and controlling the tube well that ensures proper water provision to crops. The developed system properly checks weather and environmental conditions (rain, temperature, irradiance, humidity, etc.), soil conditions (wet or dry), and crop conditions to monitor and regulate water flow accordingly to minimize water and energy consumption wastage. The developed system is an integrated system of four modules: Arduino with a built-in Atmel AT mega microcontroller, sensors, solar power, and a global system for mobile communication (GSM). The GSM module exchanges acknowledgement messages with the operator and controller about the various statuses, such as weather and environmental conditions, soil conditions (wet or dry), crop conditions, and the toggle status of the motor (OFF, ON/main power supply, or solar power). In order for the controller module to determine the motor state, the sensors module computes many parameters, including rain, wet, dry, humidity, and moisture. In addition, the sensor module also prevents the motor from dry running. The developed smart irrigation system is superior to existing irrigation systems in aspects of water wastage and energy consumption minimization. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is a physical information system developed based on traditional industrial control networks. As one of the most critical infrastructure systems, IIoT is also a preferred target for adversaries engaged in advanced persistent threats (APTs). To address this issue, we explore a deep-learning-based proactive APT detection scheme in IIoT. In this scheme, considering the characteristics of long attack sequences and long-term continuous APT attacks, our solution adopts a well-known deep learning model, bidirectional encoder representations from transformers (BERT), to detect APT attack sequences. The APT attack sequence is also optimized to ensure the model's long-term sequence judgment effectiveness. The experimental results not only show that the proposed deep learning method has feasibility and effectiveness for APT detection, but also certify that the BERT model has better accuracy and a lower false alarm rate when detecting APT attack sequences than other time series models. [ABSTRACT FROM AUTHOR]

Checking the equivalence of two Boolean functions, or combinational circuits modeled as Boolean functions, is often desired when reliable and correct hardware components are required. The most common approaches to equivalence checking are based on simulation and model checking, which are constrained due to the popular memory and state explosion problems. Furthermore, such tools are often not user-friendly, thereby making it tedious to check the equivalence of large formulas or circuits. An alternative is to use mathematical tools, called interactive theorem provers, to prove the equivalence of two circuits; however, this requires human effort and expertise to write multiple output functions and carry out interactive proof of their equivalence. In this paper, we (1) define two simple, one formal and the other informal, gate-level hardware description languages, (2) design and develop a formal automatic combinational circuit equivalence checker (CoCEC) tool, and (3) test and evaluate our tool. The tool CoCEC is based on human-assisted theorem prover Coq, yet it checks the equivalence of circuit descriptions purely automatically through a human-friendly user interface. It either returns a machine-readable proof (term) of circuits' equivalence or a counterexample of their inequality. The interface enables users to enter or load two circuit descriptions written in an easy and natural style. It automatically proves, in few seconds, the equivalence of circuits with as many as 45 variables (3.5 × 10 13 states). CoCEC has a mathematical foundation, and it is reliable, quick, and easy to use. The tool is intended to be used by digital logic circuit designers, logicians, students, and faculty during the digital logic design course. [ABSTRACT FROM AUTHOR]

In the era of the Internet of Things (IoT), connected objects produce an enormous amount of data traffic that feed big data analytics, which could be used in discovering unseen patterns and identifying anomalous traffic. In this paper, we identify five key design principles that should be considered when developing a deep learning-based intrusion detection system (IDS) for the IoT. Based on these principles, we design and implement Temporal Convolution Neural Network (TCNN), a deep learning framework for intrusion detection systems in IoT, which combines Convolution Neural Network (CNN) with causal convolution. TCNN is combined with Synthetic Minority Oversampling Technique-Nominal Continuous (SMOTE-NC) to handle unbalanced dataset. It is also combined with efficient feature engineering techniques, which consist of feature space reduction and feature transformation. TCNN is evaluated on Bot-IoT dataset and compared with two common machine learning algorithms, i.e., Logistic Regression (LR) and Random Forest (RF), and two deep learning techniques, i.e., LSTM and CNN. Experimental results show that TCNN achieves a good trade-off between effectiveness and efficiency. It outperforms the state-of-the-art deep learning IDSs that are tested on Bot-IoT dataset and records an accuracy of 99.9986% for multiclass traffic detection, and shows a very close performance to CNN with respect to the training time. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is becoming more and more popular due to its key features like scalability, flexibility, and monitoring. SDN simplifies the network management and enables innovations in the network architecture and protocols. However, this flexible architecture also makes it vulnerable to different types of attacks, such as flooding, spoofing, and denial-of-service (DoS). Among these attacks, the DoS attack has the most severe impact as it can overwhelm the major components of SDN to degrade its performance. In this paper, we propose a simple and lightweight detection and mitigation system (DAISY) to secure SDN from DoS attacks by blocking malicious traffic from the attacker, after analyzing the collected statistics. The proposed system specifically blocks malicious traffic rather than blocking the whole port or a host. In addition, it unblocks a port or a host when there is no more malicious traffic originating from it. We evaluate our proposed system by comparing it with other approaches, and the simulation results show increased performance of SDN with DAISY in terms of CPU usage, response time, control channel bandwidth, packet delivery ratio, and flow requests sent to the controller. [ABSTRACT FROM AUTHOR]

Hardware components, such as memory and arithmetic units, are integral part of every computer-controlled system, for example, Unmanned Aerial Vehicles (UAVs). The fundamental requirement of these hardware components is that they must behave as desired; otherwise, the whole system built upon them may fail. To determine whether or not a component is behaving adequately, the desired behaviour of the component is often specified in the Boolean algebra. Boolean algebra is one of the most widely used mathematical tools to analyse hardware components represented at gate level using Boolean functions. To ensure reliable computer-controlled system design, simulation and testing methods are commonly used to detect faults; however, such methods do not ensure absence of faults. In critical systems' design, such as UAVs, the simulation-based techniques are often augmented with mathematical tools and techniques to prove stronger properties, for example, absence of faults, in the early stages of the system design. In this paper, we define a lightweight mathematical framework in computer-based theorem prover Coq for describing and reasoning about Boolean algebra and hardware components (logic circuits) modelled as Boolean functions. To demonstrate the usefulness of the framework, we (1) define and prove the correctness of principle of duality mechanically using a computer tool and all basic theorems of Boolean algebra, (2) formally define the algebraic manipulation (step-by-step procedure of proving functional equivalence of functions) used in Boolean function simplification, and (3) verify functional correctness and reliability properties of two hardware components. The major advantage of using mechanical theorem provers is that the correctness of all definitions and proofs can be checked mechanically using the type checker and proof checker facilities of the proof assistant Coq. [ABSTRACT FROM AUTHOR]

In this paper, the performance analysis of a hierarchical routing protocol for mobile ad hoc networks (MANETs) called Location-aware Grid-based Hierarchical Routing (LGHR) is performed. In LGHR, the network comprises nonoverlapping zones and each zone is further partitioned into smaller grids. Although LGHR is a location-aware routing protocol, the routing mechanism is similar to the link-state routing. The protocol overcomes some of the weaknesses of other existing location-based routing protocols such as Zone-based Hierarchical Link State (ZHLS) and GRID. A detailed analysis of the LGHR routing protocol is performed and its performance is compared with both the above-mentioned protocols. The comparison shows that LGHR works better than ZHLS in terms of storage overhead as well as communication overhead, whereas LGHR is more stable than GRID especially in scenarios where wireless nodes are moving with very high velocities. [ABSTRACT FROM AUTHOR]

The constant evolution of virtualisation technologies and the availability of anti-forensic techniques and tools complicate efforts by forensic investigators to investigate a crime or a cyber security incident. Forensic collection can be complicated and requires significant efforts to investigate incidents involving contemporary technologies (e.g. crime launched from a virtual machine and there had been attempts to erase evidence after the incident). This paper presents a forensic process to collect and analyse traces of a virtual machine and its corresponding manager, recorded across multiple sources including the file system, Windows registry, history, and log files from a forensic viewpoint. To demonstrate utility of the forensic mechanism, the Virtual Machine Forensic Artefact Collector (VMFAC) prototype is developed and presented in this paper. [ABSTRACT FROM PUBLISHER]

Peer-to-Peer (P2P) applications especially in the domain of file sharing have become very popular during the past few years. These applications have huge traffic volume on the Internet as compared to any other application. In this paper, we propose a mobile P2P file-sharing framework that uses dedicated Session Traversal Utilities for NAT (STUN) servers for Network Address Translation (NAT) traversal. In our case, we divide the nodes into two types: Node Peers (mobile applications) and Super Peers (dedicated servers connected to a fixed P2P network). We develop the proposed framework for Android operating system and test it on a 3rd Generation (3G) network testbed. The experimental results obtained are quite positive and support the implementation of the system. [ABSTRACT FROM AUTHOR]

In this paper, an improved electrocardiogram (ECG) beats classification system is proposed, which is based on Fuzzy C-Means (FCM) clustering algorithm. The classification of ECG beats is necessary in order to diagnose the type of arrhythmia (e.g., Atrial Premature Contraction (APC), Premature Ventricular Contraction (PVC), Right Bundle Branch Block (RBBB) etc.) present in the ECG records. The efficiency of any classification model highly depends on the “most relevant” set of features used. The primary goal of this study is to classify different arrhythmic beats with reduced set of relevant-only ECG attributes. The attribute selection model is based on Mahalanobis-Taguchi System (MTS); a multi-dimensional pattern recognition tool, which can dynamically choose the important set of ECG features. The number of most relevant features can vary from person to person according to the type of arrhythmia present in the respective ECG signals. A traditional Euclidian Distance (ED) based FCM can detect the spherical clusters but it may lead to improper clustering in some cases. As a solution to this problem, Mahalanobis Distance (MD) is used in the proposed model in order to improve the distance measurement procedure. In our proposed system, MD based improved Fuzzy C-Means (FCM-M) clustering is used to classify the arrhythmic beats. Experimental results show that the performance of FCM-M is significantly better than the conventional FCM for arrhythmia classification. Another direction of our proposed research is to use the concept of initial cluster centroid in order to reduce the number of program iterations. In our experiments, the number of program iterations is reduced to an average of 53% when initial centroid is assigned to FCM-M with the same classification results. [ABSTRACT FROM AUTHOR]

Social media have emerged as an inexpensive and efficient way of communication during the past few years. Facebook is leading the global social networking that provides multiple channels for sharing information with the community. There are various ways to access a Facebook account such as through a web browser, mobile applications and third-party desktop applications for Facebook. Platforms like desktop applications for social media are of benefit to users as they provide ease of access and availability, but in the case of a user's data privacy breach including illegitimate social behavior, it becomes a challenge for the forensic examiners to track such activities for learning purposes. The aim of digital forensics is to get hold of the existing legal evidence present within the digital media. The major focus of this research is to investigate and analyze the two Facebook desktop applications: Fosimo and Sobees, with respect to the tracking of the user activities. The Facebook desktop applications are examined by analyzing theWindows registry, browser, cookie, cache, connection analysis and the local installation directory. As a result of this research, we ensure that the footprint gathering of the user activity from Windows registry and browser files is performed in such a way that it could be used for forensic artifacts modeling keeping the integrity of the whole process. [ABSTRACT FROM AUTHOR]

Network security risks grow with increase in the network size. In recent past, the attacks on computer networks have increased tremendously and require efficient network intrusion detection mechanisms. Data mining and machine-learning techniques have been used for network intrusion detection during the past few years and have gained much popularity. In this paper, we propose an intrusion detection mechanism based on binary particle swarm optimization (PSO) and random forests (RF) algorithms called PSO-RF and investigate the performance of various dimension reduction techniques along with a set of different classifiers including the proposed approach. Binary PSO is used to find more appropriate set of attributes for classifying network intrusions, and RF is used as a classifier. In the preprocessing step, we reduce the dimensions of the dataset by using different state-of-the-art dimension reduction techniques, and then this reduced dataset is presented to the proposed PSO-RF approach that further optimizes the dimensions of the data and finds an optimal set of features. PSO is an optimization method that has a strong global search capability and is used here for dimension optimization. We perform extensive experimentation to prove the worth of the proposed approach by using different performance metrics. The standard benchmark, that is, KDD99Cup dataset, is used that contains the information about various kinds of network intrusions. The experimental results indicate that the proposed approach performs better than the other approaches for the detection of all kinds of attacks present in the dataset. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]