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Vehicular networks are continually gaining popularity, owing to the evolution of the Internet of things, by means of rapid communication in IP-based networks. Vehicles are being fitted with sensory devices to gain inputs from the external environment for information processing. Numerous applications of vehicular networks depend on the use of communication networks. The security and privacy of data communication are significant challenges, particularly in wireless applications, for example, the Internet of connected vehicles. When an official fleet of vehicles (government or military) begins its journey, the vehicles need to communicate with one another to share sensitive information. The security of such a fleet is maintained in various security layers, each of which coordinates and communicates with the others, but avoids disclosing sensitive information of higher security layers to lower security layers. No such access control model exists that can enforce the authority of security officials in layers. We propose a novel security and privacy based access control (SPBAC) model for the Internet of connected vehicles, which allows security officials to access information in combination with permissions and roles, instead of roles only, for officials traveling in vehicles belonging to the same fleet. Vehicle-to-vehicle communication is performed with the aid of onboard unit sensory devices. The vehicles can also communicate through wireless technology, namely WiFi and cellular communications (4G/5G). The SPBAC model provides communication among security layers in a secure, private, and efficient manner compared to other state-of-the-art algorithms. The SPBAC model is demonstrated here using mathematical modeling along with implementation examples.

The main challenge in database-as-a-service is the security and privacy of data because service providers are not usually considered as trustworthy. So, the data must be encrypted before storing into the database. Another challenge arises that the performance is degraded on the deployment of encryption algorithm on runtime. Furthermore, the connectivity through the Internet adds more delay. To tackle this, we have proposed parallel query execution methodology using multithreading technique up to 6 threads. We have conducted experiments up to 1000,000 (1 million) encrypted records. Our results are quite promising. For data encryption/decryption, we have used advance encryption standard with blocking length of 256 bits. We have designed our methodology in the context of parallel computation method proposed in the literature (Ho et al., in: Proceedings of the 2017 international conference on machine learning and soft computing, pp 47–52, 2017). We compared the results with state-of-art algorithms. The state-of-art algorithms execute the experiments on 10,120 encrypted records maximum which took about time of 1000 ms with 2 threads. But the proposed methodology is proved outstanding that executed the experiments which were performed on 100,000 encrypted records. It outperformed with 6 threads which took only 507 ms even with 2 threads, and the proposed methodology is much better which took only 994 ms. So, the efficiency and scalability of the proposed methodology are proved better as compared to state-of-the-art algorithms.

© 2013 IEEE. An insulin pump is a small wearable medical gadget which can mimic the way a healthy pancreas functions by providing continuous subcutaneous insulin infusion for the patient. However, in the current products, the access to the pump is not securely controlled, rendering the pump insecure to harmful or even lethal attacks, such as those that lead to the privacy breach of the patient and the delivery of abnormal dosage of insulin to the patient. In a conventional symmetric key-based security solution, how to distribute and manage the key is quite challenging, since the patient bearing an insulin pump may visit any hospital or clinic to receive treatment from any qualified doctor. In order to prevent malicious access to the pump, in this paper, we propose a Fingerprint-based Insulin Pump security (FIPsec) scheme which requires an entity to first pass the fingerprint authentication process before it is allowed to access the insulin pump. With this scheme, the request from an adversary to access the pump will be blocked thereby protecting the pump from the possibility of being subjected to serious attacks during the post-request period. In the scheme, a cancelable Delaunay triangle-based fingerprint matching algorithm is proposed for the insulin pump, which has capabilities to resist against nonlinear fingerprint image distortion and the influence of missing or spurious minutiae. In order to evaluate the performance of the proposed fingerprint matching algorithm, we perform comprehensive experiments on FVC2002 DB1 and DB2 fingerprint databases. The results show that the FIPsec scheme can achieve a reasonably low equal error rate and thus becomes a viable solution to thwarting unauthorized access to the insulin pump.

Information technology is playing an important role in healthcare systems. Communication technologies enable the user to stay in contact with the physician. The physician is also staying in contact with his patient. Smart wearable sensing devices are easily available in a smart environment. The patient wears these sensing devices and performs daily life routine actions. If there is any disturbance in the patient body than sensing devices sense it and send the message to the doctor about the patient condition. Through this process, the physician can give proper treatment to a patient according to the condition of the patient at that time. Patient life can be saved using these innovations in smart healthcare systems. Breast cancer is a critical healthcare problem. The computer can solve this problem using advanced algorithms of artificial intelligence (AI). Breast cancer is the second leading disease causes woman deaths. According to a report, 2.5 million breast cancer cases identified in the United States in 2017. According to IARC, 8.2 million deaths caused by cancer per year. Deaths rate from cancer is increasing developed countries every year. Cancer is the name of uncontrolled cell growth in the body. Breast cancer occurs due growth of cells in the breast. Group of extra tissues is known as tumor. Cancer is the deadliest disease in the whole world. Cancer has stages. It can be cured in the first stage. After the first stage, it can never be cured. But in the first stage of cancer, it is very difficult to diagnose it. Early stage detection of cancer can save millions of lives. Computer-aided detection is used for early detection of cancer. X-ray image of the breast is known as a mammogram. Breast is compressed between the two plates and the x-ray beam is applied to take mammogram. The technique which is applied before symptoms occur in woman breast is known as screening mammography. Mammography images have noise. Image processing techniques are used to remove noise from a mammogram. In the first-phase preprocessing is done. Filters are applied to remove noise from mammograms. After preprocessing noise-free image is passed through the segmentation phase. In segmentation, the region of interest (ROI) is found. ROI is the region in the image in which a tumor exists. After the segmentation feature is extracted. The whole image’s dataset is passed from above phases. After this, text or comma-separated values (CSV) file is obtained which has the features of mammogram dataset. Dataset has two labels “0” and “1.” Label “1” indicates cancer and label “0” indicates noncancer image. Feature extracted dataset is also labeled using mammogram labeled. Subfield of AI is machine learning (ML). It has two major types that are supervised and unsupervised. Supervised learning has labeled data for training. Unsupervised learning is trained using unlabeled data. Dataset is divided into two parts. One is used for training is known as training dataset. Second is used for testing is known as test data. Training data are used for training and testing data are used for the testing model. In the end, a different measure is calculated using different formulas such as precision, accuracy, and f-score using confusing matrix. If the reasonable accuracy is achieved when the model is considered for prediction. Feature is extracted from new samples and label is predicted for that case. Either case has cancer or not. Our work is beneficial to save human lives. We applied ML algorithms to detect cancer from a mammogram. We achieved reasonable accuracy for the prediction of cancer.

Software Defined Network (SDN) brought revolution in the network field with the partnership of Academia and Industry. SDN bridges the gap to overcome issues of IoT deployment, optimization and better utilization of network resources. The escalation in resource congestion in Wireless Sensor Networks (WSNs) can usually lead to scalability, data computation or storage, and energy efficiency problems with only a single sink node for data acquisition. Internet of Things (IoT) has resource and energy constraints for WSN devices. Low Power and Lossy Networks (LLNs) ought to be optimized for traffic with multiple sinks. RPL routing has constraints to support this approach. However, RPL inherits the ability to offer features like Auto-Configuration, Self-Healing, Loop avoidance, and detection. These features of RPL can be transformed into the improved performance of a WSN by increasing the number of sinks with a linear increase of data transmitting nodes in the network. Further, to mitigate the escalated computing needs, edge computing has emerged as a new paradigm to resolve SDN-enabled IoT and localized computing needs. This study proposes an SDN-based solution to the interconnectivity of resource constraint LLN devices with edge computing routers in mesh and cluster topological scenario using RPL as IoT routing protocol. Performance evaluation concerning different routing metrics and objective functions: Minimum Rank with Hysteresis Function (MRHOF) and Zero (OF0) are analyzed. COOJA simulator is used for emulation of random as well as linear grid topologies for the creation of WSN static nodes. Simulation results confirm that the gradual increase of a number of nodes from 16, 32, 48, 64 and a simultaneous increase in sinks nodes as 1, 2, 3, 4 respectively in LLN network reflects the desired advantages with the stable network.

This research paper presents a validated emotion enabled cognitive driver assistance model (EECDAM) as an accident prevention scheme while keeping in mind different types of driver distractions. It is observed that distracted drivers know that distraction can lead them to a crash but they are not aware of distractions when they take over and they continue to drive. With advancements in autonomous vehicles technologies, it is possible to have an onboard driver assistance systems. However, research is yet to be reported on this issue whether onboard driver assistance program will be effective or not. The Emotion Enabled Cognitive Driver Assistance Model is a system based on an encapsulated Emotion Enabled Cognitive Driver Assistant (EECDA), which computes the effects of external factors at the distraction level of the subject and generates algorithmically generated fear emotion. During experiments, the EECDA intervenes when the fear intensity of the driver crosses a threshold by sending two sound alerts to the driver to take appropriate action. To demonstrate the effectiveness of the proposed approach as a road safety system, a Cognitive Agent-Based Computing (CABC) framework has been utilized to validate the results of the EECDAM. Algorithms are utilized using fuzzy sets to compute distraction of the drivers. We also present an Agent-Based Model (ABM) to validate the implementation of the proposed scheme. Extensive experiments demonstrate the proficiency of the proposed model for robust collision avoidance.

Evaluating cyber security risk is a challenging task regardless of an organisation’s nature of business or size, however, an essential activity. This paper uses the National Institute of Standards and Technology (NIST) cyber security framework (CSF) to assess the cyber security posture of a local government organisation in Western Australia. Our approach enabled the quantification of risks for specific NIST CSF core functions and respective categories and allowed making recommendations to address the gaps discovered to attain the desired level of compliance. This has led the organisation to strategically target areas related to their people, processes, and technologies, thus mitigating current and future threats.

In this paper, we have presented a comprehensive study on designing an aware energy architecture of Wireless Sensor Networks. A summary and modelling of various major techniques in designing the constituents of clustered wireless sensor network architecture are given in detail. In continuation of it, we have also analysed our proposed scheme, Extended-Multilayer Cluster Designing Algorithm (E-MCDA) in a large network. Among its components, algorithms for time slot allocation, minimising the CH competition candidates, and cluster member selection to CH play underpinning roles to achieve the target. These incorporations in MCDA result in minimising transmissions, suppressing the unneeded response of transmissions and near equal size and equal load clusters. We have done extensive simulations in NS2 and evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, the number of designed clusters, the number of nodes per cluster and un-clustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCDA and EADUC concerning parameters above.

Most existing biometric systems designed for healthcare applications only use biometrics for authentication/access control without considering its other function-data encryption. In this paper, we propose a cancelable finger-vein-based bio-cryptosystem, which not only can provide authentication but also can encrypt sensitive healthcare data through a biometric cryptographic technique, fuzzy commitment scheme (FCS). The proposed bio-cryptosystem stores both the encrypted version of healthcare data and the biometric template on a smart card for the reason that it is safer if the biometric data never leaves the card. The employment of the cancelable biometrics further enhances the system security. The experimental results and security analysis show the validity of the proposed scheme.

Anomalies in cardiac functionality can be fatal. Early detection of these anomalies, and in many cases their precursors, can save lives. The probability of the occurrence of these anomalies is extremely among people with a pre-diagnosed heart condition. In this research, we discovered that much remote Electrocardiography (ECG) monitoring systems do not convey “enough” information to the diagnosing doctor or the nominated caregiver. A few examples of this information can be the type of cardiac abnormality, the exact waveform of the ECG signal, time and frequency of the occurrence of the anomaly, machine-understandable part so that medical SCADA be alerted about the case, and immediate preventative urgent steps correlated to that emergency. It is also important to delivery surrounding context to the Health Information System so that the medical expert make his/her diagnosis with ample support data. The most important component in this communication is the security of contents from cybercrimes. We propose a cost-efficient and non-invasive health monitoring system that is secure and quickly deployable. The presented system embeds an intelligent wearable data acquisition system with unique identification algorithms requiring very little computational time and simple threshold-based classification. Scopus

While data from Supervisory Control And Data Acquisition (SCADA) systems is sent upstream, it is both the length of pulses as well as their frequency present an excellent opportunity to incorporate statistical fingerprinting. This is so, because datagrams in SCADA traffic follow a poison distribution. Although wrapping the SCADA traffic in a protective IPsec stream is an obvious choice, thin clients and unreliable communication channels make is less than ideal to use cryptographic solutions for security SCADA traffic. In this paper, we propose a smart alternative of data obfuscation in the form of Impulsive Statistical Fingerprinting (ISF). We provide important insights into our research in healthcare SCADA data security and the use of ISF. We substantiate the conversion of sensor data through the ISF into HL7 format and define policies of a seamless switch to a non HL7-based non-secure HIS to a secure HIS. - 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Scopus

In above article [1] , reference [11] is incorrect as the conference article had not been published in full until 2019. The correct reference is cited here as [2] . Therefore, the sentences in the article [1] with reference [11] should be cited [2] in this article as “When the data are stored in a centralized location and are transmitted by applying symmetric data encryption techniques, the deployment and maintenance costs will go beyond cost tolerance thresholds [2] ,” “Since cloud computing signifies a comparatively novel computing representation at every level, like applications, hosts, network, and data, that in turn raises the issue of the application safety to shift toward cloud computing [2] and [3] ,” and “However, this definition is not accurate in the sense that, if the normal operation is susceptible to eavesdropping, in this case, the normal operation of the organization must be restricted [2] .”

A healthcare network as a new context-aware cyber-physical system (CPS) includes embedded sensors, Internet of Things technology, and cloud computing. With the intellectualization of mobile terminal devices, mobile healthcare networks are automatically generated and can provide convenient healthcare services and real-time communication. However, two problems in the CPS need to resolved. A solution to the first problem is to mitigate the computational and storage cost of a mobile terminal. A solution to the second problem is to provide the cyber-physical privacy and security for healthcare data. Ciphertext-policy attribute-based encryption (CP-ABE) can provide data privacy and secure sharing in an open environment. In this article, a fast CP-ABE scheme is provided in order to satisfy the use of mobile terminal devices with low computational and storage power. In the proposed scheme, the work of a system can deliver expensive computational tasks to semitrusted third parties, ensuring a constant number of simple local computations. The scheme can also verify the correctness of the decryption result by using a Boneh–Lynn–Shacham short signature scheme. The CP-ABE scheme not only ensures the cyber-physical security of healthcare data, but also reduces the local load from the efficiency analysis and experimental results.