A Survey on Security Threats and Countermeasures in Internet of  Medical Things (IoMT)

Παπαϊωάννου, Μαρία; Karageorgou, Marina; Μαντάς, Γεώργιος; Sucasas, Victor; Essop, Ismael; Rodrı́guez, Jonathan; Lymberopoulos, Dimitrios

doi:10.1002/ett.4049

Cited by 125 publications

(88 citation statements)

References 74 publications

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“…The scheme is specified under two back-ends of AVISPA using Security Protocol Animator (SPAN) 70,73 namely OFMC and CL-AtSe which are broadly used in various security schemes. 41,46,56,57,68,70,74 As mentioned above, the specified code in HLPSL is converted into IF to generate the final report. The Output Format (OF) of the generated report is structured as stated below.…”

Section: Simulation Resultsmentioning

confidence: 99%

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An improved privacy preserving remote user authentication scheme for agricultural wireless sensor network

Rangwani

Sadhukhan

Ray

et al. 2021

Trans Emerging Tel Tech

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Agriculture is a fundamental pillar of society, bearing a crucial role in shaping the economy of the country and provides huge financial prospects and the possibility of employment to the large mass of the country. Thus, the incorporation of modern technology would lead to amplification in productivity and economic escalation. In this perspective, Wireless Sensor Network (WSN) can provide a greater service by sensing, monitoring and supplying continuous information about climatic conditions like temperature, light, soil moisture, humidity, and so on. These parameters help in increment of productivity in terms of quantity and quality growth in agriculture. However, WSN brings security threats like masquerading, alteration, interference, interception, and so on, that harmfully affect crop production and other agricultural practices. Thus, privacy preservation and security enhancement for agricultural WSN are the major challenges to be conquered. To meet this purpose, a privacy-preserving efficient and secure remote user authentication scheme is introduced in this research work for the application of agricultural WSN. The proposed scheme is formally evaluated using probabilistic Random-Oracle-Model (ROR) for proving the robustness of the scheme. Further, the scheme is simulated using a well-accepted AVISPA tool to show the security strength of it. The performance analysis of our scheme shows that along with its robustness in terms of security, the proposed scheme is more efficient in terms of computation and communication overheads in contrast to other existing schemes. These aforesaid features make the proposed scheme implementable in a practical environment.

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Section: Simulation Resultsmentioning

confidence: 99%

“…This property is known as confidentiality. 36,57 • Integrity: The messages being transmitted into the open channel could be morphed easily. Hence, the messages must be protected against any type of unwanted modification to preserve the integrity of the messages.…”

Section: Design Goalsmentioning

confidence: 99%

An improved privacy preserving remote user authentication scheme for agricultural wireless sensor network

Rangwani

Sadhukhan

Ray

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“… Signal splitting attacks: It refers to breaking of the communication and getting rid of the signal in a network so that attacks like eavesdropping could happen where an intruder can get access or listen to a conversation secretly or signal can degrade. Spoofing attack: In the existing network, an intruder can intrude with the help of a copy of each packet that affects confidentiality and privacy, which is a must in a healthcare system [ 44 ]. In a sensor spoofing attack, the physical environment is altered in a way that means the medical system cannot work properly [ 45 ].…”

Section: Security Issues In Healthcarementioning

confidence: 99%

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

Anand

Rani

Anand

et al. 2021

Sensors

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The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.

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“…In remote monitoring of patient health, data aggregation is a primary research topic for efficient data transmission. In this scenario, security is becoming a primary concern for continuous observation of patient health conditions in IoMT [17]. Mostly, sensor nodes are placed in a hostile environment along with unsafe communication mediums lead to several security attacks.…”

Section: Introductionmentioning

confidence: 99%

FoG-Oriented Secure and Lightweight Data Aggregation in IoMT

et al. 2021

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Internet of Medical Things (IoMT) is becoming an essential part of remote health monitoring due to the evolution of medical wireless sensors and intelligent communication technologies. IoT-based healthcare applications are employed in the medical centers to provide continuous health monitoring of a patient. However, recent smart medical devices have limited resources to handle the huge amount of healthcare data. IoMT faces several challenging issues, like security, privacy, anonymity, and interoperability. In data aggregation and communication, the privacy and security of medical information is a demanding task. Therefore, we proposed a suitable scheme to overcome the limitations of existing research studies. This paper presents an Efficient and Secure Data Transmission and Aggregation (ESDTA) scheme to enhance aggregation efficiency and data security. Our proposed work provides secure data aggregation and data forwarding of healthcare parameter values by employing the Secure Message Aggregation (SMA) algorithm and Secure Message Decryption (SMD) algorithm at the Mobile Node (MN) and Fog Node FN, respectively. From a security perspective, the proposed scheme preserves the data integrity and also protect against several security threats like data fabrication and replay attack. The proposed scenario is simulated through simulation tool NS 2.35. The simulation results prove that aggregation at the MN effectively reduces transmission and communication costs. Furthermore, the effective computation at the fog node minimizes the storage and computational cost at the cloud server. Thus, the analysis of the proposed scheme shows the supremacy of our proposed work. We compare our scheme with other related secure data aggregation-based schemes in terms of communication cost, energy consumption, resilience, storage and computational cost.

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A Survey on Security Threats and Countermeasures in Internet of Medical Things (IoMT)

Cited by 125 publications

References 74 publications

An improved privacy preserving remote user authentication scheme for agricultural wireless sensor network

An improved privacy preserving remote user authentication scheme for agricultural wireless sensor network

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

FoG-Oriented Secure and Lightweight Data Aggregation in IoMT

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