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Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).

The vulnerability of deep neural network (DNN)-based systems makes them susceptible to adversarial perturbation and may cause classification task failure. In this work, we propose an adversarial attack model using the Artificial Bee Colony (ABC) algorithm to generate adversarial samples without the need for a further gradient evaluation and training of the substitute model, which can further improve the chance of task failure caused by adversarial perturbation. In untargeted attacks, the proposed method obtained 100%, 98.6%, and 90.00% success rates on the MNIST, CIFAR-10 and ImageNet datasets, respectively. The experimental results show that the proposed ABCAttack can not only obtain a high attack success rate with fewer queries in the black-box setting, but also break some existing defenses to a large extent, and is not limited by model structure or size, which provides further research directions for deep learning evasion attacks and defenses.

Reasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH & DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach.

Machine to machine (M2M) communications have significant application potential in the emerging networks including home network. The advent of M2M communication brings numerous security issues as well, while existing efforts have not fully solved those issues. In this article, we propose a secure lightweight anonymous authentication and key agreement protocol for M2M home network service, which can guarantee to resist various security issues like, forgery attacks, insider attacks, masquerade attacks, resilience to key exposure, etc. Furthermore, the proposed protocol generates reasonable additional computational and communication overhead, which is also verified by performance analysis.

Electronic Patient Health Record (EPHR) systems may facilitate a patient not only to share his/her health records securely with healthcare professional but also to control his/her health privacy, in a convenient and easy way even in case of emergency. In order to fulfill these requirements, it is greatly desirable to have the access control mechanism which can efficiently handle every circumstance without negotiating security. However, the existing access control mechanisms used in healthcare to regulate and restrict the disclosure of patient data are often bypassed in case of emergencies. In this article, we propose a way to securely share EPHR data under any situation including break-the-glass (BtG) without compromising its security. In this regard, we design a reference security model, which consists of a multi-level data flow hierarchy, and an efficient access control framework based on the conventional Role-Based Access Control (RBAC) and Mandatory Access Control (MAC) policies.

With the Internet’s meteoric rise in popularity and usage over the years, there has been a significant increase in the number of web applications. Nearly all organisations use them for various purposes, such as e-commerce, e-banking, e-learning, and social networking. More importantly, web applications have become increasingly vulnerable to malicious attack. To find web vulnerabilities before an attacker, security experts use black-box web application vulnerability scanners to check for security vulnerabilities in web applications. Most studies have evaluated these black-box scanners against various vulnerable web applications. However, most tested applications are traditional (non-dynamic) and do not reflect current web. This study evaluates the detection accuracy of five black-box web application vulnerability scanners against one of the most modern and sophisticated insecure web applications, representing a real-life e-commerce. The tested vulnerabilities are injection vulnerabilities, in particular, structured query language (SQLi) injection, not only SQL (NoSQL), and server-side template injection (SSTI). We also tested the black-box scanners in four modes to identify their limitations. The findings show that the black-box scanners overlook most vulnerabilities in almost all modes and some scanners missed all the vulnerabilities.

In current times, multimedia application includes integrated sensors, mobile networks and Internet-of-Things (IoT) services. In IoT services, if more devices are connected without much constrains, the problem of security, trust and privacy remain a challenge. For multimedia communications through Wireless Sensor Network (WSN), sensor nodes transmit confidential data to the gateway nodes via public channels. In such an environment, the security remains a serious issue from past many years. Only few works are available to support secure multimedia communications performed in IoT-enabled WSNs. Among the few works, Kumari and Om recently proposed an authentication protocol for multimedia communications in IoT-enabled WSNs, which is applicable in coal mine for safety monitoring. The authors claimed in their work that their contributory protocol strongly withstands several security threats such as, user impersonation attack, sensor node impersonation attack, sensor node anonymity issue and others technical design issues. However, this article proved that Kumari and Om’s protocol has some design flaws and is susceptible to various security attacks including, user and sensor node impersonation attacks. As a remedy, a robust authentication protocol using smartcard is constructed to solve the security issues found in Kumari and Om’s protocol. The proof of correctness of mutual authentication is performed using the BAN logic model. In addition, our further security investigation claimed strong protection against known security attacks. Our protocol is analyzed comprehensively and compared against the similar protocols and the results showed that it is efficient and robust than earlier protocols.

In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunch and is always hard to access due to privacy regulations like the General Data Protection Regulation (GDPR). A potential solution is to release a synthetic dataset with a similar distribution to that of the private dataset. Nevertheless, in some scenarios, it has been found that the attributes needed to train an AI model belong to different parties, and they cannot share the raw data for synthetic data publication due to privacy regulations. In PETS 2023, Xue et al. proposed the first generative adversary network-based model, VertiGAN, for vertically partitioned data publication. However, after thoroughly investigating, we found that VertiGAN is less effective in preserving the correlation among the attributes of different parties. This article proposes a Vertical Federated Learning-based Generative Adversarial Network, VFLGAN, for vertically partitioned data publication to address the above issues. Our experimental results show that compared with VertiGAN, VFLGAN significantly improves the quality of synthetic data. Taking the MNIST dataset as an example, the quality of the synthetic dataset generated by VFLGAN is 3.2 times better than that generated by VertiGAN w.r.t. the Fréchet Distance. We also designed a more efficient and effective Gaussian mechanism for the proposed VFLGAN to provide the synthetic dataset with a differential privacy guarantee. On the other hand, differential privacy only gives the upper bound of the worst-case privacy guarantee. This article also proposes a practical auditing scheme that applies membership inference attacks to estimate privacy leakage through the synthetic dataset.

Consumers seeking a new mobile plan have many choices in the present mobile landscape. The Mobile Virtual Network Operator (MVNO) has recently gained considerable attention among these options. MVNOs offer various benefits, making them an appealing choice for a majority of consumers. These advantages encompass flexibility, access to cutting-edge technologies, enhanced coverage, superior customer service, and substantial cost savings. Even though MVNO offers several advantages, it also creates some security and privacy concerns for the customer simultaneously. For instance, in the existing solution, MVNO needs to hand over all the sensitive details, including the users' identities and master secret keys of their customers, to a mobile operator (MNO) to validate the customers while offering any services. This allows MNOs to have unrestricted access to the MVNO subscribers' location and mobile data, including voice calls, SMS, and Internet, which the MNOs frequently sell to third parties (e.g., advertisement companies and surveillance agencies) for more profit. Although critical for mass users, such privacy loss has been historically ignored due to the lack of practical and privacy-preserving solutions for registration and handover procedures in cellular networks. In this paper, we propose a universally composable authentication and handover scheme with strong user privacy support, where each MVNO user can validate a mobile operator (MNO) and vice-versa without compromising user anonymity and unlinkability support. Here, we anticipate that our proposed solution will most likely be deployed by the MVNO(s) to ensure enhanced privacy support to their customer(s).

Introducing Small Cell Networks (SCN) has significantly improved wireless link quality, spectrum efficiency and network capacity, which has been viewed as one of the key technologies in the fifth-generation (5G) mobile network. However, this technology increases the frequency of handover (HO) procedures caused by the dense deployment of cells in the network with reduced cell coverage, bringing new security and privacy issues. The current 5G-AKA and HO protocols are vulnerable to security weaknesses, such as the lack of forward secrecy and identity confusion attacks. The high HO frequency of HOs might magnify these security and privacy concerns in the 5G mobile network. This work addresses these issues by proposing a secure privacy-preserving universal HO scheme (\\(\\UniHand\\)) for SCNs in 5G mobile communication. \\(\\UniHand\\) can achieve mutual authentication, strong anonymity, perfect forward secrecy, key-escrow-free and key compromise impersonation (KCI) resilience. To the best of our knowledge, this is the \\textit{first} scheme to achieve secure, privacy-preserving universal HO with \\textit{KCI} resilience for roaming users in 5G environment. We demonstrate that our proposed scheme is resilient against all the essential security threats by performing a comprehensive formal security analysis and conducting relevant experiments to show the cost-effectiveness of the proposed scheme.