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The novel dataset called Linux-APT Dataset 2024 captures Advanced Persistent Threat (APT) attacks along with other latest and sophisticated payloads. Existing datasets lacks latest attacker's techniques and procedures, APTs tactics and configuration to capture maximum Linux log sources to observe the working and behaviour of an APT in a detailed manner. The environment which supported us in capturing the logs is composed of Linux machines and a centralized logging system configured appropriately to captures and detect all possible events and logs for an APT and other complex intrusion. Unlike Microsoft Windows, Linux logging system are investigated enough and usually systems relies on limited log sources but for an APT, all possible log sources should be evaluated and added to completely analyse the behaviour, trajectory, and operation of an APT. To keep the dataset up to date and realistic, recent payloads and APTs are emulated in the environment. A well-known cyber-security framework ‘MITRE ATT&CK’ is utilised to map the behaviour and operation in a generalized manner after capturing the events and logs. This dataset can be used for training and conducting a variety of experiments to build as well as design the solutions for detecting most recent intrusions and APT attacks for Linux System.

Software Defined Networking (SDN) has emerged as a new paradigm for managing heterogeneous networks ranging from enterprises to home network via decoupling the control plane from the data plane. In the traditional networking landscape, these two planes are tightly bound together inside a single appliance. The logically centralized and distributed control plane and programmability offer a great opportunity to improve network security, such as by implementing new mechanisms to detect and mitigate various threats, and also enable security as a service in an SDN paradigm. Due to the ever increasing and fast development of SDN, this paper provides an extensive survey of SDN controllers, SDN-related security threats, and solutions to mitigate the security threats. This study provides a comprehensive survey of 53 SDN controllers from different aspects, including language, architecture, organization, open source, scalability, consistency, reliability, API used, library, and their description. We have also provided a detailed security analysis of SDN architecture with an extensive classification of security threats endangering its different architectural components and the solutions to effectively mitigate them. This paper also identifies challenges and promising future directions on SDN deployment, standardization, implementation, and security issues that should be addressed in this field.

OBJECTIVES This study aims to identify the essential characteristics of diagnostic tests for SARS-CoV-2 and to discuss the limitations of currently available tests and their impact on the test selection process. METHODOLOGY The current study was conducted at Mardan Medical Complex (MMC). One hundred nasopharyngeal-positive samples were collected from February to March 2021. Oropharyngeal swab OPS, sputum, and blood samples were collected from the participants to detect SARS-CoV-2 RNA. RNA extraction of SARS-CoV-2 was done using a BigFish auto extractor. A Qiagen Thermal Cycler was used for genome amplification. Five different molecular assays, namely COVSIGN (N gene) Spain, BGI (ORF1ab gene) China, Maccura(ORF1ab, E and N gene) China, R-GENE (RdRp and N genes) France and Genuru (N gene, S gene and ORF ab/1) were used. RESULTS 100 % positivity was recorded in the sputum of all individuals, followed by 91 % OPS and 21% blood. The highest positivity rate for different genes was observed. ROC (Receiver operating characteristic curve) was developed through SPPS version 26.00 to compare the sensitivity and specificity. CONCLUSION By comparing the results of different diagnostic kits, it was found that BGI and Maccura are the most sensitive and specific for diagnostic purposes against COVID-19.

Internet of Things (IoT) is a system of interconnected devices that have the ability to monitor and transfer data to peers without human intervention. Authentication, Authorization and Audit Logs (AAA) are prime features of Network Security and easily attained in legacy systems, however, remains unachieved in IoT. The IoTs require due security considerations as the conventional security mechanisms are not optimized for such devices due to various aspects such as heterogeneity, resource constrained processing, storage and multiple factors. Additionally, the legacy systems are mostly centralized and thus introduce a single point of failure. In this research, a novel framework, FBASHI is presented that is based on fuzzy logic and blockchain technology to achieve AAA services. The proposed system is developed using Hyperledger that is a blockchain platform providing privacy and fast response capability, therefore, it is best suited for the healthcare IoT environments. This work proposes behavior driven adaptive security mechanism for healthcare IoTs and networks based on blockchain by utilizing fuzzy logic and presents a heuristic approach towards behavior driven adaptive security providing AAA services. FBASHI is implemented to analyze its security and practicality. Furthermore, a comparison is drawn with other blockchain-based solutions.

Abstract Electro‐organic synthesis has attracted a lot of attention in pharmaceutical science, medicinal chemistry, and future industrial applications in energy storage and conversion. To date, there has not been a detailed review on electro‐organic synthesis with the strategy of heterogeneous catalysis. In this review, the most recent advances in synthesizing value‐added chemicals by heterogeneous catalysis are summarized. An overview of electrocatalytic oxidation and reduction processes as well as paired electrocatalysis is provided, and the anodic oxidation of alcohols (monohydric and polyhydric), aldehydes, and amines are discussed. This review also provides in‐depth insight into the cathodic reduction of carboxylates, carbon dioxide, CC, C≡C, and reductive coupling reactions. Moreover, the electrocatalytic paired electro‐synthesis methods, including parallel paired, sequential divergent paired, and convergent paired electrolysis, are summarized. Additionally, the strategies developed to achieve high electrosynthesis efficiency and the associated challenges are also addressed. It is believed that electro‐organic synthesis is a promising direction of organic electrochemistry, offering numerous opportunities to develop new organic reaction methods.

The structural alteration of carbon nitride (CN) for photocatalytic CO2 reduction is a promising research topic in the environmental and energy sectors. This work discusses the fabrication of photocatalyst through a heterojunction architecture obtained from the molecular engineering of electron-rich organic monomer 2,6-pyridinedicarboxylic acid (PDA) with CN precursor (CN/PDAx). The successful integration of PDA in the structure of CN served as a charge inducting entity to enhance charge separation and photocatalytic CO2 reduction under visible light (λ = 420 nm). The DFT results indicated that the upshift in the HOMO level of CN after integration of PDA in its framework was the most lawful for the charge separation and for obtaining a high reduction potential. As-synthesized photocatalysts were demonstrated for various integral analysises and after evaluating the process of photocatalytic CO2 reduction under visible light region (λ = 420 nm). The optimized sample CN/PDA10 has the most excellent photocatalytic activity producing 85.4 μmol/h of CO and 21.3 μmol/h of H2, achieving a 7.5-fold enhanced catalytic efficiency as compared to pure CN. We hope that this work will attract more attention to synthesizing efficient photocatalysts for energy production and environmental remediation.

Objectives: To compare the mean improvement in pain on Visual Analogue Scale by Suprascapular Nerve block and Intra-articular steroid injection in patients with hemiplegic shoulder pain. Study Design: Randomized Controlled Trial Place and Duration of Study: Pain Clinic, Armed Forces Institute of Rehabilitation Medicine Rawalpindi from 25th October 2017 to 24th April 2018 Patients & Methods: 60 patients with CVA and shoulder pain on hemiplegic side for at least 2 weeks were included in study after non-probability consecutive sampling after meeting inclusion criteria. Subjects were randomized by lottery into two groups; Group A receiving Suprascapular Nerve block with 5ml of 1% Lignocaine with Group B receiving Intra-articular 40mg Triamcinolone Acetonide plus 1 ml of 1% Lignocaine injections. Mean change in pain scores on VAS was calculated between score at baseline and at 4 weeks post-intervention Results: The mean age of patients in group A was 59.17 ± 9.81 years and in group B was 39.96 ± 11.11 years. Out of 60 patients, 34 (56.67%) were males and 26 (43.33%) were females with male to female ratio of 1.3:1. Mean duration of stroke was 7.50 ± 2.66 months. Mean pain change on VAS in Group A (Suprascapular Nerve block) was 3.83 ± 1.12 cm while in Group B (Intra-articular steroid injection) it was 2.17 ± 0.99 cm (p-value = 0.0001). Conclusion: This study concluded that Suprascapular Nerve block offered better improvement in hemiplegic shoulder pain on Visual Analogue Scale at 4 weeks as compared to intraarticular steroid inje

Semiconductor-based photocatalytic technology is regarded as an efficient pathway for resolving the energy scarcity across the globe. In this regard, graphitic carbon nitride (g-C3N4)-based materials could be alternatively employed in photochemical applications such as photovoltaic energy generation via CO2 photoreduction and water splitting, along with natural resource purification via organic/inorganic pollutant degradation. Indeed, this kind of assertion has been made by considering the intrinsic physicochemical properties of g-C3N4 nanomaterials, owing to their increased surface area, quantum yield, surface charge isolation, distribution, and ease of modification through material configuration or incorporation of preferred interfacial capabilities. This review article has been designed to provide the most up-to-date information regarding the further assessment of the important advancements in fabrication along with photochemical applications of various g-C3N4 nanomaterials, while specifically focusing on the scientific reason behind its success in each assessment. The discovery of interventions to alleviate such restrictions and boost photocatalytic performance has gained substantial interest. Following photo-excitation fundamentals, this work explains two distinct photoexcitation mechanisms, the carrier and charge transfer techniques, wherein the significant exciting state impact of g-C3N4 has still not been widely focused on in past studies. In this regards, we cautiously introduce the updated advances and associated functions of the alteration techniques, including morphological features, elemental dopants, deficiency engineering, and heterojunction implemented in photocatalytic performance, which are equated from the carrier and charge transport perceptions. The future perspectives in designing and properly tuning the highly active hierarchical or copolymer g-C3N4 nanoparticles in a photocatalytic system, which may improve the renewable energy cultivation and reduction efficiency are critically deciphered in detail and outlined thoroughly.

Most of the commercial antiviruses are signature based, that is, they use existing database signature to detect the malware. Malware authors use code obfuscation techniques in their variety of malware with the aim of bypassing detection by antiviruses. Metamorphic malware change their internal structure hence evading signature based detection. For effective defense against the malware, their construction needs to be explored. This paper includes the study of different obfuscation techniques and possibilities of their extension with focus on garbage code insertion, instruction substitution and subroutine reordering. The objective is to make detection difficult by implementing subject techniques which bypass detection. Havex malware is used as a proof of concept for our antivirus evasion strategy. We have used Hidden Markov Models (HMM), which is a statistical based machine learning detection method, to test the effectiveness of our code morphing. This has shown the strength of our implemented obfuscation techniques.

Encryption, which is essential for the protection of sensitive information can also transform any malicious content to illegible form, which can then reside in any network, undetected. Encryption of malicious payload is used by malware authors to mask their code, however, the objective of hiding the malicious code can be further improved by techniques of re-randomization. The concept of re-randomization using asymmetric cryptography has been emerged as a new area of interest for malware designers. Re-randomizing is a technique which can prevent detection of source path of a malware and makes it indistinguishable. This article extends the idea of using asymmetric cryptography for re-randomization and has proposed a novel scheme using Pailliar’s asymmetric cryptosystem. Moreover, this research work illustrates the limitations of RSA for malware re-randomization. A comprehensive performance analysis of the re-randomization techniques for various malware payloads is also presented, which can be used for the detection of re-randomized malware effectively.

The most detrimental cyber attacks are usually not originated by malicious outsiders or malware but from trusted insiders. The main advantage insider attackers have over external elements is their ability to bypass security checks and remain undiscovered, this may cause serious damage to the organizational assets. This paper focuses on insider threat detection through behavioral analysis of users. User behavior is categorized as normal or malicious based on user activity. A series of events and activities are analyzed for feature selection to efficiently detect adversarial behavior. Selected feature vectors are used for model training during the implementation phase. A deep learning based approach is proposed that detects insiders with greater accuracy and low false positive rate. A rich event / user role based feature set containing Logon/Logoff events, User_role, Functional_unit etc are used for detection. The dataset used is the CMU CERT synthetic insider threat dataset r4.2. Performance of our proposed algorithm has been compared to other well-known techniques i.e. long short term Memory- convolutional neural network, random forest, long short term memory- recurrent neural network, one class support vector machine, Markov chain model, multi state long short term memory & convolutional neural network, gated recurrent unit & skipgram. The comparison proved that our novel approach produces relatively good accuracy(90.60%), precision(97%) and F1 Score (94%).

Humic acid (HA) is natural product obtained by plant decomposition. It improves systematic resistance in plants and the shelf life of food products. Oyster mushrooms occupy important place in human food due to their palatability and nutritional enrichment. Little is known about the impacts of HA on mushrooms yield. Therefore, a trial was conducted to study the role of HA improving growth, nutritional and chemical composition of two oyster mushroom strains (Pleurotus ostreatus, Lentinus sajor-caju). Pure cotton waste amalgamated with five levels of HA, i.e., 2, 4, 6, 8 and 10 mM/L was used as growth media. The responses of oyster mushroom to HA were recorded in various traits i.e. time to spawn initiation, time to mycelium growth initiation, time to maturity of flushes, time to initiation of pinheads, yield, biological efficiency (BE), minerals (N, P, K, and ascorbic acid, Zn, Cu, Mg, Mn, Fe, Na and Ca), sugars (total sugars, reducing and non-reducing sugars), proximate, total soluble solids (TSS), acidity, and Fourier-transform infrared spectroscopy (FTIR). The HA amalgation notably improved the growth, nutritional and chemical composition of oyster mushroom; however, strains differences were non-significant (>0.05) to various level of HA on dry weight basis TSS ranged from 6 to 6.8 °Brix, total sugar was 5.8–11.9%, reducing sugar was 2.6–3%, non-reducing sugar was 9.2–9.6%, ascorbic acid was 35.9–43 mg/100 g, carbohydrates were 68–74%, crude protein was 62–69%, crude fiber was 22–37%, fat contents were 2.5–17%, ash content was 9–11%. These results suggest that HA is an innovative substrate for valuable and high-quality production of the oyster mushroom.

Abstract Objective: To report the seasonal trends of Guillain-Barré Syndrome in a cohort of Pakistani population from southern Punjab, Pakistan. Study Design: A retrospective observational study. Place and Duration of Study: Department of Physical Medicine & Rehabilitation, Combined Military Hospital Bahawalpur, from Jan 2016 to Oct 2018. Methodology: One-hundred and fifty-two individuals fulfilling the electrophysiological criteria of Guillain-Barré Syndrome were included from the records of the department and subdivided into four main subtypes on the basis of electrodiagnostic studies including Acute Inflammatory Demyelinating Polyneuropathy, Acute Motor Axonal Neuropathy, Acute Motor and Sensory Axonal Neuropathy, and Miller Fisher Syndrome. The seasons were divided as: summer; May to August, autumn; September and October, winter; November to February, and spring; March and April. Demographic characteristics, type of Guillain-Barré Syndrome, and month and season of presentation were investigated. Data were analyzed with SPSS version 20. Results: The sample (mean age: 39 ± 21 years) consisted of 114 (75%) males and 38 (25%) females. The highest reporting (63, 41.4%) was in winter followed by (53, 34.9%) summer (p

Solar energy is a long-established technology, which has zero CO2 emissions, and provides low-cost energy for a given area of land. The concentrator photovoltaic (CPV) has been given preference over the photovoltaic due to its high efficiency. In a CPV system, most of the solar cell area has been replaced with an optical concentrator. Various parabolic trough based CPV systems have been presented where a concentration of

The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

Background: Highly infectious respiratory disease COVID-19 emerged in Wuhan, China, and spread worldwide. Different measures have been adopted worldwide to contain the COVID-19, and these measures have various impacts on health-related quality of life (HRQoL). This study aimed to assess the impact of the COVID-19 pandemic (CP) and lockdown policy on physical health (PH)–psychological health (PsH), physical activity (PA), and overall well-being (OW) in the context of HRQoL, exploring the mediating role of emotional regulation (ER).Method: The current study was conducted in two provincial cities of China. An online survey was conducted in both the cities to collect the data. After quantifying the data, a total of 2,200 respondents data were analyzed through appropriate statistical techniques.Results: The study results indicate that CP was found significantly and negatively related to PH (β = −0.157, t = 9.444, p < 0.001). A significant relationship was found between CP and PsH (β = 0.779, t = 45.013, p < 0.001). The third prediction revealed a significant negative relationship between the CP and OW (β = −0.080, t = 5.261, p < 0.001). The CP and PA had a significant negative relationship (β = −0.047, t = 3.351, p < 0.001).Conclusion: The PH, PsH, and OW of the Chinese people were affected due to the CP and lockdown measures. It is suggested that ER intervention reduces the negative psychological impacts for improving quality of life. ER can function one's sentiments in their social environment effectively for quality of life.

Unmanned Aerial Systems (UAVs, Drones), initially known only for their military applications, are getting increasingly popular in the civil sector as well. Over the military canvas, drones have already proven themselves as a potent force multiplier through unmanned, round-the-clock, long-range and high-endurance missions for surveillance, reconnaissance, search and rescue, and even armed combat applications. With the emergence of the Internet of Things (IoT), commercial deployments of drones are also growing exponentially, ranging from cargo and taxi services to agriculture, disaster relief, risk assessment and monitoring of critical infrastructures. Irrespective of the deployment sector, drones are often entrusted to conduct safety, time and liability critical tasks, thus requiring secure, robust and trustworthy operations. In contrast, the rise in UAVs’ demand, coupled with market pressure to reduce size, weight, power and cost (SwaP-C) parameters, has caused vendors to often ignore security aspects, thus inducing serious safety and security threats. As UAVs rely on Global Positioning System (GPS) for positioning and navigation, they can fall prey to GPS jamming and spoofing attacks. The vulnerability of GPS to spoofing has serious implications for UAVs, as victim drones using civil GPS can be misdirected or even completely hijacked for malicious intents, as already demonstrated in several academic research efforts using commercially available GPS spoofing hardware. Beside UAVs, GPS spoofing attacks are equally applicable to other GPS-dependent platforms, including manned aircraft, ground vehicles, and cellular systems. This paper conducts a comprehensive review of GPS spoofing threats, with a special focus on their applicability over UAVs and other GPS-dependent mobile platforms. It presents a novel taxonomy of GPS spoofing attacks and critically analyzes different spoofing techniques based upon placement of spoofing device, attack stealthiness, attack methodologies, and objectives of the attacker. We also discuss some of the recent experiments from open literature which utilized commercially available hardware for successfully conducting spoofing attacks.

BackgroundCOVID-19 break out has created panic and fear in society. A strict kind of lockdown was imposed in Wuhan, Hubei province of China. During home confinement due to lockdown, people face multidimensional issues. The present study explored the psychological impacts of COVID-19 home confinement during the lockdown period and Wuhan’s residents’ attitude toward physical activity.MethodsA cross-sectional online survey was conducted to collect the primary data according to the study objectives. The population was Wuhan residents (+ 18 years) who were in home confinement. A total of 2280 participant’s reply to the online questionnaire survey and collected data after quantifying the data, about 2200 (96.49%); about (49.8%) female, about (49.4%) male, others about (0.2%), and about (0.6%) were not disclosed their gender participants responses were used for analysis. The collected data were analyzed through appropriate statistical techniques.ResultsAccording to results, H1 is supported with β = −40.793, t = 57.835, p = 0.000, which claimed a negative association between COVID-19 lockdown policy and behavior and attitude. Results for H2 reveals that the COVID-19 lockdown policy have negative influence on emotional control with β = −0.769, t = 46.766, p = 0.000 and it is supported. H3 documented a significant positive relationship between COVID-19 lockdown policy and lockdown period psychological impact, which means lockdown policy, is the main reason to increase the lockdown psychological impact. Further, COVID-19 lockdown policy have negative influence on physical activity (H4) and self-belief (H5) with β = −0.657, t = 32.766, p = 0.000 and β = −0.620, t = −6.766, p = 0.000 respectively. H6 stated that there is a positive impact of behavior and attitude toward physical activity. The results for H6, behavior, and attitude affecting the physical activity with β = 0.401, t = 10, p = 0.000, which is supported.ConclusionCOVID-19 home confinement created various psychological impacts, negatively affecting the emotional state due to depression and anxiety. Physical activity is the best strategy to manage human nature’s psychological issues, and people’s attitudes were positive toward physical activity during home confinement. However, the lockdown policy also affects physical activity participation negatively, and a sedentary lifestyle prevailed during home confinement.

The ever-growing ecosystem of the Internet of Things (IoT) integrating with the ever-evolving wireless communication technology paves the way for adopting new applications in a smart society. The core concept of smart society emphasizes utilizing information and communication technology (ICT) infrastructure to improve every aspect of life. Among the variety of smart services, eHealth is at the forefront of these promises. eHealth is rapidly gaining popularity to overcome the insufficient healthcare services and provide patient-centric treatment for the rising aging population with chronic diseases. Keeping in view the sensitivity of medical data, this interfacing between healthcare and technology has raised many security concerns. Among the many contemporary solutions, attribute-based encryption (ABE) is the dominant technology because of its inherent support for one-to-many transfer and fine-grained access control mechanisms to confidential medical data. ABE uses costly bilinear pairing operations, which are too heavy for eHealth’s tiny wireless body area network (WBAN) devices despite its proper functionality. We present an efficient and secure ABE architecture with outsourcing intense encryption and decryption operations in this work. For practical realization, our scheme uses elliptic curve scalar point multiplication as the underlying technology of ABE instead of costly pairing operations. In addition, it provides support for attribute/users revocation and verifiability of outsourced medical data. Using the selective-set security model, the proposed scheme is secure under the elliptic curve decisional Diffie–Hellman (ECDDH) assumption. The performance assessment and top-ranked value via the help of fuzzy logic’s evaluation based on distance from average solution (EDAS) method show that the proposed scheme is efficient and suitable for access control in eHealth smart societies.

Objective: To identify various difficulties in clinical practice faced by young physical medicine and rehabilitation (PMR) fellows. Study Design: Qualitative study. Place and Duration of Study: The study was carried out at Armed Forces Institute of Rehabilitation Medicine (AFIRM) from Aug 2017 to Sep 2017. Material and Methods: The study was carried out on ten physical medicine and rehabilitation fellows, who have cleared their fellowship exams in last 5 years. The study utilized online questionnaire followed by in-depth; telephonic interviews until the point of data saturation. The data was analyzed through thematic analysis. The data was transcribed in six stages to observe defining meaningful patterns. It was analyzed manually to draw conclusions. Results: The most common problems observed by young fellows were difficulty in performing specialized electrodiagnostic procedures, interpretation of urodynamics results and performing certain pain procedures including supra-scapular nerve block, platelet-rich plasma (PRP) and meralgiaparesthetica block. It was followed by managing specialized population including geriatrics, cerebral palsy and amputees and counseling skills. Conclusion: The skills component and specialized population management were deficient, though, sufficiently covered in the curriculum. So training institutes must consider certain changes in the teaching and learning environment to improve the identified areas.

e-Healthcare promises to be the next big wave in healthcare. It offers all the advantages and benefits imaginable by both the patient and the user. However, current e-Healthcare systems are not yet fully developed and mature, and thus lack the degree of confidentiality, integrity, privacy, and user trust necessary to be widely implemented. Two primary aspects of any operational healthcare enterprise are the quality of healthcare services and patient trust over the healthcare enterprise. Trust is intertwined with issues like confidentiality, integrity, accountability, authenticity, identity, and data management, to name a few. Privacy remains one of the biggest obstacles to ensuring the success of e-Healthcare solutions in winning patient trust as it indirectly covers most security concerns. Addressing privacy concerns requires addressing security issues like access control, authentication, non-repudiation, and accountability, without which end-to-end privacy cannot be ensured. Achieving privacy from the point of data collection in wireless sensor networks, to incorporating the Internet of Things, to communication links, and to data storage and access, is a huge undertaking and requires extensive work. Privacy requirements are further compounded by the fact that the data handled in an enterprise are of an extremely personal and private nature, and its mismanagement, either intentionally or unintentionally, could seriously hurt both the patient and future prospects of an e-Healthcare enterprise. Research carried out in order to address privacy concerns is not homogenous in nature. It focuses on the failure of certain parts of the e-Healthcare enterprise to fully address all aspects of privacy. In the middle of this ongoing research and implementation, a gradual shift has occurred, moving e-Healthcare enterprise controls away from an organizational level toward the level of patients. This is intended to give patients more control and authority over decision making regarding their protected health information/electronic health record. A lot of works and efforts are necessary in order to better assess the feasibility of this major shift in e-Healthcare enterprises. Existing research can be naturally divided on the basis of techniques used. These include data anonymization/pseudonymization and access control mechanisms primarily for stored data privacy. This, however, results in giving a back seat to certain privacy requirements (accountability, integrity, non-repudiation, and identity management). This paper reviews research carried out in this regard and explores whether this research offers any possible solutions to either patient privacy requirements for e-Healthcare or possibilities for addressing the (technical as well as psychological) privacy concerns of the users.

Smart societies have an increasing demand for quality-oriented services and infrastructure in an industrial Internet of Things (IIoT) paradigm. Smart urbanization faces numerous challenges. Among them, secured energy demand-side management (DSM) is of particular concern. The IIoT renders the industrial systems to malware, cyberattacks, and other security risks. The IIoT with the amalgamation of big data analytics can provide efficient solutions to such challenges. This paper proposes a secured and trusted multilayered DSM engine for a smart social society using IIoT-based big data analytics. The major objective is to provide a generic secured solution for smart societies in IIoT environment. The proposed engine uses a centralized approach to achieve optimum DSM over a home area network. To enhance the security of this engine, a payload-based authentication scheme is utilized that relies on a lightweight handshake mechanism. Our proposed method utilizes the lightweight features of the constrained application protocol to facilitate the clients in monitoring various resources residing over the server in an energy-efficient manner. In addition, data streams are processed using big data analytics with MapReduce parallel processing. The proposed authentication approach is evaluated using NetDuino Plus 2 boards that yield a lower connection overhead, memory consumption, response time, and a robust defense against various malicious attacks. On the other hand, our data processing approach is tested on reliable datasets using Apache Hadoop with Apache Spark to verify the proposed DMS engine. The test results reveal that the proposed architecture offers valuable insights into the smart social societies in the context of IIoT.

The inevitable revolution of the Internet of Things (IoT) and its benefits can be witnessed everywhere. Two major issues related to IoT are the interoperability and the identification of trustworthy things. The proposed Context-Aware Trustworthy Social Web of Things System (CATSWoTS) addresses the interoperability issue by incorporating web technologies including Service Oriented Architecture where each thing plays the role of a service provider as well as a role of service consumer. The aspect of social web helps in getting recommendations from social relations. It was identified that the context dependency of trust along with Quality of Service (QoS) criteria, for identifying and recommending trustworthy Web of Things (WoT), require more attention. For this purpose, the parameters of context awareness and the constraints of QoS are considered. The research focuses on the idea of a user-centric system where the profiles of each thing (level of trustworthiness) are being maintained at a centralized level and at a distributed level as well. The CATSWoTS evaluates service providers based on the mentioned parameters and the constraints and then identifies a suitable service provider. For this, a rule-based collaborative filtering approach is used. The efficacy of CATSWoTS is evaluated with a specifically designed environment using a real QoS data set. The results showed that the proposed novel technique fills the gap present in the state of the art. It performed well by dynamically identifying and recommending trustworthy services as per the requirements of a service seeker.