Human Activity Recognition is a key component of ambient assisted living and one of the most active areas of research in the Internet of Things. The use of wearable and embedded sensors in human activity recognition overcomes the limitations of conventional approaches relying on machine vision and environmental sensors. We offer a novel, lightweight Convolutional Neural Network – Bidirectional Gated Recurrent Unit (CNN-BiGRU) model that classifies human activities using the inertial sensor data collected with body-mounted smartwatches and smartphones. Unlike the traditional approaches, the presented model is trained on the magnitude of the 3D acceleration (mag _a ), which significantly minimizes the input space 1D. The deep learner has been validated using two different publicly available datasets from the Wireless Sensor Data Mining (WISDM) lab and different evaluation parameters such as Recall/Sensitivity, Precision, accuracy, and F1-scores are computed. A comparison with the existing studies reveals that our proposed learner surpasses the existing methodologies. Using magnitude of 3D acceleration (mag _a ^w , 1D input signal), we have achieved 97.29% accuracy for all 6 activities of WISDM 2011 dataset and 98.81% accuracy for 3D acceleration (a _x ^w ,a _y ^w ,a _z ^w ). The Precision, Recall and F1-score remained at 97% for the 1D case and 99% for the 3D case. When evaluated on the data of all 18 smartwatch-based activities in WISDM 2019 dataset, we have achieved 97.5% accuracy with the magnitude of 3D acceleration (mag _a ^w ) and 98.4% accuracy for 6D acceleration and angular velocities (a _x ^w ,a _y ^w ,a _z ^w ,ω _x ^w ,ω _y ^w ,ω _z ^w ). The Precision, Recall and F1-score remained at 98% in both cases.

Intrusion Prevention Systems (IPS), capable of preventing the organizational network from a cyber-attack in addition to detecting it, are widely adopted by organizations to protect their networks from unauthorized access, attacks, and malicious activities. Similarly, Snort an open-source IPS is extensively used for effective network security monitoring and analysis. When functioning as an IPS, Snort can be deployed in inline mode within an organizational network, so that all the organizational network traffic travels through it, hence actively blocking or preventing malicious traffic in real-time. This requires Snort to process the network traffic fast enough to match the network traffic line rate. But the Snort IPS default data acquisition module i.e. advanced packet filtering (AF_PACKET) cannot process network traffic at the line rate that causes packet loss and network services disturbance. This research work discusses the technologies available to make Snort IPS process network traffic at line rate. Packet filtering framework (PF_RING) and data plane development kit (DPDK) are the most effective and widely used software technologies, whereas the Napatech smart network interface card (smartNIC) is a very efficient hardware technology for achieving line rate traffic processing. A throughput comparison shows that PF_RING and DPDK achieve a throughput close to 1G with 100% CPU utilization whereas Napatech smartNIC achieves full 1G throughput with CPU utilization of less than 5%. Furthermore, the integration of Snort IPS with the security information and event management (SIEM) system has been discussed for better attack detection in an organizational network.

Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands.

The Internet of Vehicles (IoV) is an incipient topic within the wider domain of the Internet of Things (IoT). Using this technology intelligent transportation systems (ITS) can be developed, whose main purpose is to ensure more safety, faster travel, reduced energy consumption and improved vehicle upkeep. Devices connected within the IoV transmit a vast amount of data, which leads to additional costs on the communication network along with data security concerns. This research considers the potential uses of blockchain technology for improving communication between independent vehicles. In this paper, a decentralized system is proposed to enhance the security and performance of financial transactions in the network of autonomous cars based on the Ethereum blockchain. Our system is divided into two modules: car registration and message alert generating. New vehicles are allowed on the network using the smart contract that has been written and published on the Ethereum Remix IDE. This is the contract code attached to the Metamask wallet. Another module, the message alert-generating module, allows an administrator to send alert messages to all registered cars via a web-based system to their wallet for an Ethereum gas cost. It ensures the reliability of data transferred between the self-driving cars through the use of blockchain-based systems. The data is, therefore, confirmed and vetted using the proof of work and stake consensus algorithm to bring out the truth. The transaction being made, the system will see to it that the transaction made is of integrity and trustable. By synching all these technologies and means of reaching an agreement, the blockchain secures not only the means of keeping and retaining the information of the kept within self-driving vehicles but also establishes a strong foundation of highest trust and transparency in the interaction between the vehicles on the network. Accordingly, a well-defined and reliable conceptual design for the communication systems of autonomous vehicles has been proposed. Future work and the unresolved issues of using blockchain for autonomous vehicles have also been deliberated over in detail.

Healthcare is a multi-actor environment that requires independent actors to have a different view of the same data, hence leading to different access rights. Ciphertext Policy-Attribute-based Encryption (CP-ABE) provides a one-to-many access control mechanism by defining an attribute’s policy over ciphertext. Although, all users satisfying the policy are given access to the same data, this limits its usage in the provision of hierarchical access control and in situations where different users/actors need to have granular access of the data. Moreover, most of the existing CP-ABE schemes either provide static access control or in certain cases the policy update is computationally intensive involving all non-revoked users to actively participate. Aiming to tackle both the challenges, this paper proposes a patient-centric multi message CP-ABE scheme with efficient policy update. Firstly, a general overview of the system architecture implementing the proposed access control mechanism is presented. Thereafter, for enforcing access control a concrete cryptographic construction is proposed and implemented/tested over the physiological data gathered from a healthcare sensor: shimmer sensor. The experiment results reveal that the proposed construction has constant computational cost in both encryption and decryption operations and generates constant size ciphertext for both the original policy and its update parameters. Moreover, the scheme is proven to be selectively secure in the random oracle model under the q-Bilinear Diffie Hellman Exponent (q-BDHE) assumption. Performance analysis of the scheme depicts promising results for practical real-world healthcare applications.