Melanoma identification and classification model based on fine-tuned convolutional neural network.

Background: Breakthroughs in skin cancer diagnostics have resulted from recent image recognition and Artificial Intelligence (AI) technology advancements. There has been growing recognition that skin cancer can be lethal to humans. For instance, melanoma is the most unpredictable and terrible form of skin cancer. Materials and Methodology: This paper aims to support Internet of Medical Things (IoMT) applications by developing a robust image classification model for the early detection of melanoma, a deadly skin cancer. It presents a novel approach to melanoma detection using a Convolutional Neural Network (CNN)-based method that employs image classification techniques based on Deep Learning (DL). We analyze dermatoscopic images from publicly available datasets, including DermIS, DermQuest, DermIS&Quest, and ISIC2019. Our model applies convolutional and pooling layers to extract meaningful features, followed by fully connected layers for classification. Results: The proposed CNN model achieves high accuracy demonstrates the model's effectiveness in distinguishing between malignant and benign skin lesions. We developed deep features and used transfer learning to improve the categorization accuracy of medical images. Soft-max classification layer and support vector machine have been used to assess the classification performance of deep features. The proposed model's efficacy is rigorously evaluated using benchmark datasets: DermIS, DermQuest, and ISIC2019, having 621, 1233, and 25000 images, respectively. Its performance is compared to current best practices showing an average of 5% improved detection accuracy in DermIS, 6% improvement in DermQuest, and 0.81% in ISIC2019 datasets. Conclusion: Our study showcases the potential of CNN in melanoma detection, contributing to early diagnosis and improved patient outcomes. The developed model proves its capability to aid dermatologists in accurate decision-making, paving the way for enhanced skin cancer diagnosis.

Securing Smart Healthcare Cyber-Physical Systems against Blackhole and Greyhole Attacks Using a Blockchain-Enabled Gini Index Framework.

The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhole and greyhole attacks pose significant risks to the availability and integrity of CPSs. The current detection and mitigation approaches often struggle to accurately differentiate between legitimate and malicious behavior, leading to ineffective protection. This paper introduces Gini-index and blockchain-based Blackhole/Greyhole RPL (GBG-RPL), a novel technique designed for efficient detection and mitigation of blackhole and greyhole attacks in smart health monitoring CPSs. GBG-RPL leverages the analytical prowess of the Gini index and the security advantages of blockchain technology to protect these systems against sophisticated threats. This research not only focuses on identifying anomalous activities but also proposes a resilient framework that ensures the integrity and reliability of the monitored data. GBG-RPL achieves notable improvements as compared to another state-of-the-art technique referred to as BCPS-RPL, including a 7.18% reduction in packet loss ratio, an 11.97% enhancement in residual energy utilization, and a 19.27% decrease in energy consumption. Its security features are also very effective, boasting a 10.65% improvement in attack-detection rate and an 18.88% faster average attack-detection time. GBG-RPL optimizes network management by exhibiting a 21.65% reduction in message overhead and a 28.34% decrease in end-to-end delay, thus showing its potential for enhanced reliability, efficiency, and security.

BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things.

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature.

Hand-based multibiometric systems: state-of-the-art and future challenges.

The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things.

A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime.

Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks.

In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness.

Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security.

The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

An Efficient Routing Protocol Based on Stretched Holding Time Difference for Underwater Wireless Sensor Networks.

Underwater Wireless Sensors Networks (UWSNs) use acoustic waves as a communication medium because of the high attenuation to radio and optical waves underwater. However, acoustic signals lack propagation speed as compared to radio or optical waves. In addition, the UWSNs also pose various intrinsic challenges, i.e., frequent node mobility with water currents, high error rate, low bandwidth, long delays, and energy scarcity. Various UWSN routing protocols have been proposed to overcome the above-mentioned challenges. Vector-based routing protocols confine the communication within a virtual pipeline for the sake of directionality and define a fixed pipeline radius between the source node and the centerline station. Energy-Scaled and Expanded Vector-Based Forwarding (ESEVBF) protocol limits the number of duplicate packets by expanding the holding time according to the propagation delay, and thus reduces the energy consumption via the remaining energy of Potential Forwarding Nodes (PFNs) at the first hop. The holding time mechanism of ESEVBF is restricted only to the first-hop PFNs of the source node. The protocol fails when there is a void or energy hole at the second hop, affecting the reliability of the system. Our proposed protocol, Extended Energy-Scaled and Expanded Vector-Based Forwarding Protocol (EESEVBF), exploits the holding time mechanism to suppress duplicate packets. Moreover, the proposed protocol tackles the hidden terminal problem due to which a reasonable reduction in duplicate packets initiated by the reproducing nodes occurs. The holding time is calculated based on the following four parameters: (i) the distance from the boundary of the transmission area relative to the PFNs' inverse energy at the 1st and 2nd hop, (ii) distance from the virtual pipeline, (iii) distance from the source to the PFN at the second hop, and (iv) distance from the first-hop PFN to its destination. Therefore, the proposed protocol stretches the holding time difference based on two hops, resulting in lower energy consumption, decreased end-to-end delay, and increased packet delivery ratio. The simulation results demonstrate that compared to ESEVBF, our proposed protocol EESEVBF experiences 20.2 % lesser delay, approximately 6.66 % more energy efficiency, and a further 11.26 % reduction in generating redundant packets.

Key Agreement Schemes in Wireless Body Area Networks: Taxonomy and State-of-the-Art.

Advances in wearable and implantable biosensors have enabled the applicability and usability of wireless body area networks (WBANs). A WBAN allows biosensors to collect and communicate human physiological data using wireless communication. The communication security of the collected data in WBAN is a major concern. Because of the dependability of cryptographic schemes for key management, these have become an important aspect of this security. However, the extremely constrained nature of biosensors has made designing key management schemes a challenging task. For this reason, many lightweight key management schemes have been proposed to overcome these constraints. In this article, we present a review of the state of the art of these solutions. We classify the WBAN schemes into three classes and evaluate them based on adequate metrics for key management in WBAN.

A broadcast-based key agreement scheme using set reconciliation for wireless body area networks.

Information and communication technologies have thrived over the last few years. Healthcare systems have also benefited from this progression. A wireless body area network (WBAN) consists of small, low-power sensors used to monitor human physiological values remotely, which enables physicians to remotely monitor the health of patients. Communication security in WBANs is essential because it involves human physiological data. Key agreement and authentication are the primary issues in the security of WBANs. To agree upon a common key, the nodes exchange information with each other using wireless communication. This information exchange process must be secure enough or the information exchange should be minimized to a certain level so that if information leak occurs, it does not affect the overall system. Most of the existing solutions for this problem exchange too much information for the sake of key agreement; getting this information is sufficient for an attacker to reproduce the key. Set reconciliation is a technique used to reconcile two similar sets held by two different hosts with minimal communication complexity. This paper presents a broadcast-based key agreement scheme using set reconciliation for secure communication in WBANs. The proposed scheme allows the neighboring nodes to agree upon a common key with the personal server (PS), generated from the electrocardiogram (EKG) feature set of the host body. Minimal information is exchanged in a broadcast manner, and even if every node is missing a different subset, by reconciling these feature sets, the whole network will still agree upon a single common key. Because of the limited information exchange, if an attacker gets the information in any way, he/she will not be able to reproduce the key. The proposed scheme mitigates replay, selective forwarding, and denial of service attacks using a challenge-response authentication mechanism. The simulation results show that the proposed scheme has a great deal of adoptability in terms of security, communication overhead, and running time complexity, as compared to the existing EKG-based key agreement scheme.