The proliferation of Internet of Things (IoT) devices has introduced numerous security challenges, driven by the widespread availability and rapidly increasing demand for smart devices and networks. Traditional security methods have struggled to keep pace with emerging threats, necessitating the developing of robust, continuously improved, and up-to-date security infrastructures. This paper investigates how Artificial Intelligence (AI) and Machine Learning (ML) techniques can enhance the security of IoT communication protocols. We explore current IoT routing protocols and analyze the potential of AI and ML in detecting attacks and identifying abnormal behaviours in smart devices and networks. Our research focuses on AI and ML-based security characteristics, as well as their application in improving IoT communication protocols.

Recommending appropriate drugs to patients based on their history and symptoms is a complex real-world problem. Knowing whether a drug is useful without its consumption by a variety of people followed by proper evaluation is a challenge. Modern-day recommender systems can assist in this provided they receive large data to learn. Public reviews on various drugs are available for knowledge sharing. These reviews assist in recommending the best and most appropriate option to the user. The explicit feedback underpins the entire recommender system. This work develops a novel knowledge graph-based convolutional network for recommending drugs. The knowledge graph is coupled with sentiment analysis extracted from the public reviews on drugs to enhance drug recommendations. For each drug that has been used previously, sentiments have been analyzed to determine which one has the most effective reviews. The knowledge graph effectively captures user-item relatedness by mining its associated attributes. Experiments are performed on public benchmarks and a comparison is made with closely related state-of-the-art works. Based on the obtained results, the current work performs better than the past contributions by achieving up to 98.7% Area Under Curve (AUC) score.

Recently, on social media, various kinds of social engineering (SE) have made individuals more susceptible to attacks. A phishing attempt is a widely used SE technique that takes advantage of people’s vulnerabilities to acquire personal or confidential information. These attempts are growing at an astonishing speed, causing harm to both individuals and corporations. According to the latest studies, certain individuals are more vulnerable to such kinds of attacks than others. However, the relationship between psychological characteristics and phishing attacks has not been adequately investigated. This study empirically explores the connection between phishing vulnerability that causes data biases and the Big Five personality traits. Recognizing personality traits that make people more vulnerable to phishing attempts is a key step in developing protection and safeguarding individuals. The individuals who scored high in some traits are more probable to suffer from such assault. To the best of our knowledge, no prior quantitative study has attempted to find many genuine phishing victims and their personality behavior. This problem lacks the availability of publically accessible data. It is also challenging to estimate the probability distribution of rows in tabular data and generate realistic synthetic data to train/test the model on more data. This work employs a conditional generative adversarial network (C-GAN) for both data generation and classification to find the correlation between personality traits and phishing attacks.

With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named KGCAN (Knowledge Graph Enhanced Contextualized Attention-Based Network), which explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing knowledge graph based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics.

Age of information (AoI) is used to evaluate the performance of 5G health monitoring systems because stale data can be fatal for patients with serious illness. Recently, grant-free ultrareliable and low latency communications (URLLC) have shown greater potential of minimizing AoI than conventional grant-based approaches; however, existing grant-free schedulers cannot provide guaranteed performance in 5G health monitoring systems because they involve two fundamental problems in time and frequency domains, namely the joint scheduling problem and physical resource block (PRB) allocation. In this study, we investigate two resource allocation problems for the first time, aiming to enable grant-free URLLC to minimize AoI in 5G health monitoring systems. Specifically, we propose two adaptive solutions based on an open radio access network-coordinated wireless system: 1) a joint scheduling algorithm and 2) an adaptive PRB allocation algorithm. To verify the effectiveness of the proposed solutions, we built a simulation environment similar to a real health monitoring system and captured the performance variations under realistic deployment scenarios.

Multi-objective optimization (MOO) has been a topic of intense interest in providing flexible trade-offs between conflicting optimization criteria in wireless body area networks (WBANs). To solve diverse multi-objective optimization problems (MOPs), conventional resource management schemes have dealt with the classic issues of WBANs, such as traffic heterogeneity, emergency response, and body shadowing. However, existing approaches have difficulty achieving MOO because, despite the personalization of WBANs, they still miss the new constraints or considerations derived from user-specific characteristics. To address this problem, in this paper, we propose an adaptive scheduling and power control scheme for MOO in personalized WBANs. Specifically, we investigate the existing scheduling and power control schemes for solving MOPs in WBANs, clarify their limitations, and present two feasible solutions: priority-based adaptive scheduling and deep reinforcement learning (DRL) power control. By integrating these two mechanisms in compliance with the IEEE 802.15.6 standard, we can jointly improve the optimization criteria, that is, differentiated quality of service (QoS), transmission reliability, and energy efficiency. Through comprehensive simulations, we captured the performance variations under realistic WBAN deployment scenarios and verified that the proposed scheme can achieve a higher throughput and packet delivery ratio, lower power consumption ratio, and shorter delay compared with a conventional approach.

Rapid development and adaptation of the Internet of Things (IoT) has created new problems for securing these interconnected devices and networks. There are hundreds of thousands of IoT devices with underlying security vulnerabilities , such as insufficient device authentication/authorisation making them vulnerable to malware infection . IoT botnets are designed to grow and compete with one another over unsecure devices and networks. Once infected, the device will monitor a Command-and-Control (C&C) server indicating the target of an attack via Distributed Denial of Service (DDoS) attack. These security issues, coupled with the continued growth of IoT, presents a much larger attack surface for attackers to exploit in their attempts to disrupt or gain unauthorized access to networks, systems, and data. Large datasets available online provide good benchmarks for the development of accurate solutions for botnet detection , however model training is often a time-consuming process. Interestingly, significant advancement of GPU technology allows shortening the time required to train such large and complex models. This paper presents a methodology for the pre-processing of the IoT-Bot dataset and classification of various attack types included. We include descriptions of pre-processing actions conducted to prepare data for training and a comparison of results achieved with GPU accelerated versions of Random Forest , k-Nearest Neighbour, Support Vector Machine (SVM) and Logistic Regression classifiers from the cuML library. Using our methodology, the best-trained models achieved at least 0.99 scores for accuracy, precision, recall and f1-score. Moreover, the application of feature selection and training models on GPU significantly reduced the training and estimation times.

Real-time strategy (RTS) games differ as they persist in varying scenarios and states. These games enable an integrated correspondence of non-player characters (NPCs) to appear as an autodidact in a dynamic environment, thereby resulting in a combined attack of NPCs on human-controlled character (HCC) with maximal damage. This research aims to empower NPCs with intelligent traits. Therefore, we instigate an assortment of ant colony optimization (ACO) with genetic algorithm (GA)-based approach to first-person shooter (FPS) game, i.e., Zombies Redemption (ZR). Eminent NPCs with best-fit genes are elected to spawn NPCs over generations and game levels as yielded by GA. Moreover, NPCs empower ACO to elect an optimal path with diverse incentives and less likelihood of getting shot. The proposed technique ZR is novel as it integrates ACO and GA in FPS games where NPC will use ACO to exploit and optimize its current strategy. GA will be used to share and explore strategy among NPCs. Moreover, it involves an elaboration of the mechanism of evolution through parameter utilization and updation over the generations. ZR is played by 450 players with varying levels having the evolving traits of NPCs and environmental constraints in order to accumulate experimental results. Results revealed improvement in NPCs performance as the game proceeds.

Abstract Malware analysis and detection over the Android have been the focus of considerable research, during recent years, as customer adoption of Android attracted a corresponding number of malware writers. Antivirus companies commonly rely on signatures and are error‐prone. Traditional machine learning techniques are based on static, dynamic, and hybrid analysis; however, for large scale Android malware analysis, these approaches are not feasible. Deep neural architectures are able to analyze large scale static details of the applications, but static analysis techniques can ignore many malicious behaviors of applications. The study contributes to the documentation of various approaches for detection of malware, traditional and state‐of‐the‐art models, developed for analysis that facilitates the provision of basic insights for researchers working in malware analysis, and the study also provides a dynamic approach that employs deep neural network models for detection of malware. Moreover, the study uses Android permissions as a parameter to measure the dynamic behavior of around 16,900 benign and intruded applications. A dataset is created which encompasses a large set of permissions‐based dynamic behavior pertaining applications, with an aim to train deep learning models for prediction of behavior. The proposed architecture extracts representations from input sequence data with no human intervention. The state‐of‐the‐art Deep Convolutional Generative Adversarial Network extracted deep features and accomplished a general validation accuracy of 97.08% with an F1‐score of 0.973 in correctly classifying input. Furthermore, the concept of blockchain is utilized to preserve the integrity of the dataset and the results of the analysis.

In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients' estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model's prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients' count of the proposed model is much closer to the actual patient count.

In wireless sensor networks for the Internet of Things (WSN‐IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat‐based WSN‐IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN‐IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large‐size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN‐IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN‐IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH‐set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new‐fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state‐of‐the‐art methods like MobAC (Singh and Lohani, 2019), EPSO‐C (Pathak, 2020), and PBC‐CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime.

Summary The rapid proliferation of Internet of things (IoT) devices, such as smart meters and water valves, into industrial critical infrastructures and control systems has put stringent performance and scalability requirements on modern Supervisory Control and Data Acquisition (SCADA) systems. While cloud computing has enabled modern SCADA systems to cope with the increasing amount of data generated by sensors, actuators, and control devices, there has been a growing interest recently to deploy edge data centers in fog architectures to secure low‐latency and enhanced security for mission‐critical data. However, fog security and privacy for SCADA‐based IoT critical infrastructures remains an under‐researched area. To address this challenge, this contribution proposes a novel security “toolbox” to reinforce the integrity, security, and privacy of SCADA‐based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature: a cryptographic‐based access approach to the cloud services using identity‐based cryptography and signature schemes at the fog layer. We present the implementation details of a prototype for our proposed secure fog‐based platform and provide performance evaluation results to demonstrate the appropriateness of the proposed platform in a real‐world scenario. These results can pave the way toward the development of a more secure and trusted SCADA‐based IoT critical infrastructure, which is essential to counter cyber threats against next‐generation critical infrastructure and industrial control systems. The results from the experiments demonstrate a superior performance of the secure fog‐based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform.

Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.

In this paper, we propose a new enhanced temperature aware routing protocol to assign the temperature of node by considering current temperature and expected rise caused by the packets in the buffer. Also, two hops ahead algorithm is employed to ensure further packet forwarding to the sink. The simulation results are shown to prove that the proposed scheme is able to increase packet delivery ratio and network lifetime.

rough wireless sensor networks (WSN), we can acquire the various interesting event information around sensor nodes through multihop communications.In WSN, there are two types of applications, that is, event or query based.Commonly, in these application, the value on each sensor node is very sensitive to delay or latency.So, it is strongly required to deliver data to sink node within the deadline since data received aer the deadline is not acceptable at all in WSN. e good example of application demanding real-time communication in WSN includes tracking of moving object and intrusion detection.However, compared to typical networks, it is very difficult to achieve real-time communication in WSN.Severe constraints such as limited computing power and narrow bandwidth are not suitable to provide real-time communication accordingly.So, a number of important issues and research challenges have to be addressed to provide realtime communication in WSN.Based on this demand, this special issue is planned to contribute to advances in real-time communications in WSN.While considering our objective, editors believe that this special issue provides collection of articles on networking technique in real-time communications.We have selected valuable papers by evaluating several aspects such as relevance to special issue and novelty of solution.e topic of these papers is roughly categorized into the following areas: multichannel transmission, MAC protocol, testbed for routing protocol, and comprehensive survey for real-time in WSN.

With the emergence of smartphones and the widespread use of social media services, distribution of multimedia files over the Internet, and using mobile phones, has increased exponentially over the past few years. A significant number of cybercrimes pertain to illicit possession, modification, and distribution of multimedia files. The use of smartphones for this purpose makes these mobile phones rich sources of evidence. Therefore, it is crucial for forensic examiners to have the capability of recovering, analyzing, and authenticating the source of multimedia contents stored on these devices. This paper focuses on the analysis of multimedia files created on the most popular smartphones in order to ascertain the source and examine whether the files are original or edited through these devices. The popular smartphones brands analyzed in this paper include iPhone 5, iPhone 6, Blackberry Z10, Samsung Galaxy Note 3, Nokia Lumia 930, and Lenovo A536. Experimental results on all these brands are also presented.

Due to the stringent energy constraint wireless "s, network management cost for lifetime becomes" "sensor node" "one of the crucial issues in designing wireless sensor networks (WSNs). In such situation, guaranteed network lifetime is more preferred than unpredictable pro-long one. However, despite of its importance and necessity in real world deployment, few research works have been proposed to solve this complex problem by ideal approach with the lack of applicability. In this paper, we present a new centralized scheduling mechanism to guarantee network lifetime to a pre-determined time while maintaining k-coverage of network field. In order to guarantee network lifetime, each node are enforced to remain steadfast in the idle state for a known duration of its lifetime. In addition, the scheduling algorithm is extended to provide real-time service by adjusting transmission range according to current energy state of a sensor node. These two core functions are implemented by deciding each nodes role in the next round according to nodes energy consumption and active time history in decision phase. The performance of proposed scheme is compared with well-known A-MAC and MMSPEED protocols in terms of guaranteed lifetime and real-time respectively.

Typical mobile P2P protocols largely relied on inflexible techniques such as, flooding, replicating, random walks and selective forwarding to route queries and discover objects of interest, which incur a relatively high search time due to remarkable network traffic, multiple copies and duplication of query messages. Recently proposed P2P protocols are able to reduce search time but due to peer mobility, these protocols cause low hit rate and high overhead. Thus, this article proposes novel fuzzy controller based probabilistic walk for unstructured mobile P2P networks to reduce search time. The search time is reduced by jumping a query walker to 2-hop away selected ultrapeer through fuzzy controller. Furthermore, each ultrapeer share its pong cache with its directly connected ultrapeer to increase hit rate and reduce network overhead. Simulations show that the fuzzy search controller gives better performance than the competing protocols in terms of reducing response time by 10% and increasing hit rate by 15% in different mobility scenarios.