Internet of Things (IoT) comprises of large number of smart devices that can exchange sensed data by availing online services. Smart devices can directly interact with the human beings for sensing the healthcare parameters and transmitting to a central repository. During the data exchange, it is mandatory to secure the messages between sender and receiver to handle the malicious human based attacks. For providing secure communication, a number of signature-based schemes are explored in literature but smart devices need more lightweight operations by ensuring desired security strengths. The main problem during signature based approaches is the computational overhead due to large real numbers required for signature and verification processes. This paper presents a light weight shortened complex digital signature algorithm for providing secure communication between smart devices in human centered IoT. We have used less extensive operations to achieve signature and verification processes like human beings do signatures on legal documents and verify later as per witness. We have also presented a multi-option parameter selection to use a signature-verification pair of expressions at particular index by adopting the common practice of human beings for using different signatures as per document criticality level. It enhances the security strength to guard against traffic analysis attacks. We have developed an experimental setup where a number of cell phones and smart devices are considered for secure communication using existing and proposed digital signature schemes. Results demonstrate the supremacy of our scheme as compared to preliminaries.

The exponential growth of the Internet of Things (IoT) has led to the rapid expansion of interconnected systems, which has also increased the vulnerability of IoT devices to security threats such as distributed denial-of-service (DDoS) attacks. In this paper, we propose a machine learning pipeline that specifically addresses the issue of DDoS attack detection in IoT networks. Our approach comprises of (i) a processing module to prepare the data for further analysis, (ii) a dynamic attribute selection module that selects the most adaptive and productive features and reduces the training time, and (iii) a classification module to detect DDoS attacks. We evaluate the effectiveness of our approach using the CICI-IDS-2018 dataset and five powerful yet simple machine learning classifiers—Decision Tree (DT), Gaussian Naive Bayes, Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). Our results demonstrate that DT outperforms its counterparts and achieves up to 99.98% accuracy in just 0.18 s of CPU time. Our approach is simple, lightweight, and accurate for detecting DDoS attacks in IoT networks.

Internet-of-Things (IoT) include a large number of devices that can communicate across different networks. Cyber-Physical Systems (CPS) also includes a number of devices connected to the internet where wearable devices are also included. Both systems enable researchers to develop healthcare systems with additional intelligence as well as prediction capabilities both for lifestyle and in hospitals. It offers as much persistence as a platform to ubiquitous healthcare by using wearable sensors to transfer the information over servers, smartphones, and other smart devices in the Telecare Medical Information System (TMIS). Security is a challenging issue in TMIS, and resourceful access to health care services requires user verification and confidentiality. Existing schemes lack in ensuring reliable prescription safety along with authentication. This research presents a Secure Authentication and Prescription Safety (SAPS) protocol to ensure secure communication between the patient, doctor/nurse, and the trusted server. The proposed procedure relies upon the efficient elliptic curve cryptosystem which can generate a symmetric secure key to ensure secure data exchange between patients and physicians after successful authentication of participants individually. A trusted server is involved for mutual authentication between parties and then generates a common key after completing the validation process. Moreover, the scheme is verified by doing formal modeling using Rubin Logic and validated using simulations in NS-2.35. We have analyzed the SAPS against security attacks, and then performance analysis is elucidated. Results prove the dominance of SAPS over preliminaries regarding mutual authentication, message integrity, freshness, and session key management and attack prevention.

Wireless Sensor Networks (WSNs) consist of lightweight devices to measure sensitive data that are highly vulnerable to security attacks due to their constrained resources. In a similar manner, the internet-based lightweight devices used in the Internet of Things (IoT) are facing severe security and privacy issues because of the direct accessibility of devices due to their connection to the internet. Complex and resource-intensive security schemes are infeasible and reduce the network lifetime. In this regard, we have explored the polynomial distribution-based key establishment schemes and identified an issue that the resultant polynomial value is either storage intensive or infeasible when large values are multiplied. It becomes more costly when these polynomials are regenerated dynamically after each node join or leave operation and whenever key is refreshed. To reduce the computation, we have proposed an Efficient Key Management (EKM) scheme for multiparty communication-based scenarios. The proposed session key management protocol is established by applying a symmetric polynomial for group members, and the group head acts as a responsible node. The polynomial generation method uses security credentials and secure hash function. Symmetric cryptographic parameters are efficient in computation, communication, and the storage required. The security justification of the proposed scheme has been completed by using Rubin logic, which guarantees that the protocol attains mutual validation and session key agreement property strongly among the participating entities. Simulation scenarios are performed using NS 2.35 to validate the results for storage, communication, latency, energy, and polynomial calculation costs during authentication, session key generation, node migration, secure joining, and leaving phases. EKM is efficient regarding storage, computation, and communication overhead and can protect WSN-based IoT infrastructure.

Cloud computing has become a significant computing model in the IT industry. In this emerging model, computing resources such as software, hardware, networking, and storage can be accessed anywhere in the world on a pay-per-use basis. However, storing sensitive data on un-trusted servers is a challenging issue for this model. To guarantee confidentiality and proper access control of outsourced sensitive data, classical encryption techniques are used. However, such access control schemes are not feasible in cloud computing because of their lack of flexibility, scalability, and fine-grained access control. Instead, Attribute-Based Encryption (ABE) techniques are used in the cloud. This paper extensively surveys all ABE schemes and creates a comparison table for the key criteria for these schemes in cloud applications.

The advancement and innovations in wireless communication technologies including the Internet of Things have massively changed the paradigms of health-based services. In particular, during the COVID-19 pandemic, the trends of working from home have been promoted. Wireless body area network technology frameworks help sufferers in remotely obtaining scientific remedies from physicians through the Internet without paying a visit to the clinics. IoT sensor nodes are incorporated into the clinical device to allow health workers to consult the patients’ fitness conditions in real time. Insecure wireless communication channels make unauthorized access to fitness-related records and manipulation of IoT sensor nodes attached to the patient’s bodies possible, as a result of security flaws. As a result, IoT-enabled devices are threatened by a number of well-known attacks, including impersonation, replay, man-in-the-middle, and denial-of-service assaults. Modern authentication schemes do solve these issues, but they frequently involve challenging mathematical concepts that raise processing and transmission costs. In this paper, we propose a lightweight, secure, and efficient symmetric key exchange algorithm and remote user authentication scheme. Our research proposal presents a successful privacy-protecting method for remote users and provides protection against known attacks. When compared to conventional options, this technique significantly reduces calculation costs by up to 37.68% and transmission costs by up to 32.55%.