Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network

The ubiquity of Internet of Things (IoT) devices has completely changed the healthcare industry by presenting previously unheard-of potential for remote patient monitoring and individualized care. In this regard, we suggest a unique method that makes use of Secure Convolutional Neural Networks (SCNNs) to improve security in Internet-of-Healthcare (IoH) applications. IoT-enabled healthcare has advanced as a result of the integration of IoT technologies, giving it impressive data processing powers and large data storage capacity. This synergy has led to the development of an intelligent healthcare system that is intended to remotely monitor a patient's medical well-being via a wearable device as a result of the ongoing advancement of the Industrial Internet of Things (IIoT). This paper focuses on safeguarding user privacy and easing data analysis. Sensitive data is carefully separated from user-generated data before being gathered. Convolutional neural network (CNN) technology is used to analyse health-related data thoroughly in the cloud while scrupulously protecting the privacy of the consumers.The paper provide a secure access control module that functions using user attributes within the IoT-Healthcare system to strengthen security. This module strengthens the system's overall security and privacy by ensuring that only authorised personnel may access and interact with the sensitive health data. The IoT-enabled healthcare system gets the capacity to offer seamless remote monitoring while ensuring the confidentiality and integrity of user information thanks to this integrated architecture

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment

[EN] Due to large volume and high variability of editing tools, protecting multimedia contents, and ensuring their privacy and authenticity has become an increasingly important issue in cyber-physical security of industrial environments, especially industrial surveillance. The approaches authenticating images using their principle content emerge as popular authentication techniques in industrial video surveillance applications. But maintaining a good tradeoff between perceptual robustness and discriminations is the key research challenge in image hashing approaches. In this paper, a robust image hashing method is proposed for efficient authentication of keyframes extracted from surveillance video data. A novel feature extraction strategy is employed in the proposed image hashing approach for authentication by extracting two important features: the positions of rich and nonzero low edge blocks and the dominant discrete cosine transform (DCT) coefficients of the corresponding rich edge blocks, keeping the computational cost at minimum. Extensive experiments conducted from different perspectives suggest that the proposed approach provides a trustworthy and secure way of multimedia data transmission over surveillance networks. Further, the results vindicate the suitability of our proposal for real-time authentication and embedded security in smart industrial applications compared to state-of-the-art methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and sponsored by Qing Lan Project of Jiangsu Province, China.Sajjad, M.; Ul Haq, I.; Lloret, J.; Ding, W.; Muhammad, K. (2019). Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment. IEEE Transactions on Industrial Informatics. 15(12):6541-6550. https://doi.org/10.1109/TII.2019.2921652S65416550151

Joint block and stream cipher based on a modified skew tent map

Image encryption is very different from that of texts due to the bulk data capacity and the high redundancy of images. Thus, traditional methods are difficult to use for image encryption as their pseudo-random sequences have small space. Chaotic cryptography use chaos theory in specific systems working such as computing algorithms to accomplish dissimilar cryptographic tasks in a cryptosystem with a fast throughput. For higher security, encryption is the approach to guard information and prevent its leakage. In this paper, a hybrid encryption scheme that combines both stream and block ciphering algorithms is proposed in order to achieve the required level of security with the minimum encryption time. This scheme is based on an improved mathematical model to cover the defects in the previous discredited model proposed by Masuda. The proposed chaos-based cryptosystem uses the improved Skew Tent Map (STM) RQ-FSTM as a substitution layer. This map is based on a lookup table to overcome various problems, such as the fixed point, the key space restrictions, and the limitation of mapping between plain text and cipher text. It uses the same map as a generator to change the byte position to achieve the required confusion and diffusion effects. This modification improves the security level of the original STM. The robustness of the proposed cryptosystem is proven by the performance and the security analysis, as well as the high encryption speed. Depending on the results of the security analysis the proposed system has a better dynamic key space than previous ones using STM, a double encryption quality and a better security analysis than others in the literature with speed convenience to real-time applications