Due to advancements in the development of wireless medical sensing devices and wireless communication technologies, the wireless body area network (WBAN) has become an eminent part of e-healthcare systems. WBAN uses medical sensors to continuously monitor and collect the physiological parameters of a patient's health and send them to a remote medical server through a portable digital assistance (PDA)/mobile. Due to limitations in communication, such as power, storage, and the computational capabilities of sensors, data aggregation techniques are used to reduce the communication overhead in real-time data transmission in WBAN. However, since the WBAN transmits sensitive health data, data security and data privacy are a major concern. In this paper, we propose a secure privacy-preserving data aggregation (SPPDA) scheme based on bilinear pairing for remote health monitoring systems to improve data aggregation efficiency and data privacy. Our proposed SPPDA scheme utilizes the homomorphic property of the bilinear ElGamal cryptosystem to perform privacy-preserving secure computation and combines it with the aggregate signature scheme, enabling data authenticity/integrity in the WBAN. The proposed SPPDA scheme is proved to be semantically secure under the decisional bilinear Diffie-Hellman assumption. Security analysis demonstrates that our proposed scheme preserves data confidentiality, data authenticity, and data privacy; it also resists passive eavesdropping and replay attacks. A performance evaluation based on simulation results and a comparison of computational cost with related schemes show that data aggregation and batch verification at the PDA significantly reduce communication and transmission overhead and support efficient computation at the remote server.

User authentication is a measurement challenge for handheld devices and online accounts such as bank accounts, social media accounts etc. because illegal access results in money loss and user privacy. individual devices, online financial services, and intelligent spaces are three significant areas of concern for customer authentication procedures. Three ways have been identified for authentication factors: i) knowledge-factor, ii) inherence factor, and iii) possession-factor. This study investigates two-way user authentication through image processing. CNN, RCNN, and Deepface are deep learning algorithms used for image recognition. We used imagechain for image storage and Blockchain for personal information storage (mobile number) to secure the database. The database is stored on an Ethereum-based blockchain. After determining whether the image is fake or real, match the webcam image with the imagechain; if both images match, the one-time password is given to the user's cellphone number for login access. For image processing, Opencv is employed, and the Python library is used to execute machine and deep learning algorithms for user authentication. Test the proposed model on the 10 to 100 users for authentication. Accuracy of this experiment is 75.35, 76.33, 98.18 and cosine similarities of images are much better between images, but in case of fake image identification it achieved 97.35 % accuracy.

The architecture of software-defined networking (SDN) involves the separation of the network control plane from the routing plane. If this initiative turns out well, it has the potential to reduce operating expenses and the duration required to provide new services in comparison to traditional networks. However, this architecture has additional security concerns, including a single point of failure that could potentially provide any user with unrestricted access to the entire network. Nevertheless, it is essential to reduce the probability of security breaches. The development of immediate intrusion detection systems (IDSs) that can quickly spot and stop malicious activities like distributed denial of service (DDoS), DoS, web-attacks, and Bot-NET is an important part of SDN architecture. Several researchers are using cutting-edge methods, such as machine learning, to investigate and elucidate the causes behind the sudden rise in attacks and abnormal behavior, but the majority of these methods are deficient in terms of flexibility and accuracy. This study proposed a lightweight method for detecting different SDN attacks from intrusion-defined networks. The lightweight long short-term memory (LSTM) network has the capability to capture temporal patterns and sequential interactions in the SDN data. It also learned important context that is efficient for feature extraction and then developed supervised random forest (SRF) for the attack prediction. The dataset consists of 207,146 rows and 84 features that were preprocessed, including separate features and target attacks. The experiments show that the proposed method achieved 99.93% accuracy for attack detection and 0.0090 loss, confirming its efficacy. We also tested the proposed method on another SDN dataset and achieved 99.43% accuracy for multi-class attack detection. Furthermore, the use of supervised random forest reduces the model’s complexity, resulting in increased overall efficiency.

Internet of Things (IoT) growth is expected to reach phenomenal levels of diverse adoption and deployment amongst multiple industries. This ranges from financial, communication, transport, health and general consumer sectors. The expected burst of IoT deployment will introduce significant cyber security challenges, specifically in the area of configuration management. Blockchain technology is continuously evolving beyond the use as financial currency, and is proving to be a feasible solution for distributed record keeping and validation. The special characteristics of Blockchain technology introduces a new domain of IoT secure configuration and decentralized management, while taking into consideration the challenging constraints imposed by IoT devices efficient and low-power design. In this paper we try to identify an effective approach to manage and control security IoT device configurations by adopting a Blockchain technology.