Hamid Barati

Lung cancer is the second-leading cause of death worldwide. One method of cancer prediction involves identifying genes that are effective in cancer. The selection of efficient genes is one area of interest for specialists in diagnosing lung cancer. This research presents a method based on mathematical modelling that considers genes with high dimensions. The output of solving the mathematical model is to determine efficient genes for lung cancer diagnosis. We first remove ineffective genes in lung cancer diagnosis using the proposed method. Then, for the remaining genes, we obtain the dependency matrix between them. In the next step, we get the sorted solution space for the World Competitive Contests algorithm (WCC) using the futuristic greedy index and the dependency matrix. The research's main innovation is identifying this ordered solution space within the larger solution space. The ordered solution space is a space of candidate solutions not randomly selected by the WCC algorithm. Instead, these candidate solutions are regularly used by the WCC algorithm to find proper approximate solutions. This method reduces the number of iterations in the WCC algorithm and finds appropriate approximate solutions in less execution time. The experimental results showed that in the first dataset, the proposed method achieved a 17.63% improvement in execution time compared to the fast WCC algorithm. Additionally, with the increase in data volume in the large dataset, the execution time of the proposed algorithm shows an improvement of approximately 41.6% compared to the WCC algorithm. Experiments confirm that the proposed method's Accuracy error rate is approximately 1%, and that the error rate varies significantly as the data sets change and grow, because the WCC algorithm prevents random choices in the solution space.

Medical Internet of Things (IoMT) is the next frontier in the digital revolution and is utilized in healthcare. In this context, IoT enables individuals to remotely manage their essential activities with minimal interaction. However, the limitations of network resources and the challenges of establishing a secure channel, as well as sharing and collecting sensitive information through an insecure public channel, pose security challenges for the medical IoT. This paper presents a lightweight multi-factor authentication and anonymous user authentication scheme to access real-time data in a blockchain-based environment. The scheme utilizes an insecure channel called L2AI. L2AI ensures security and efficiency while enhancing user anonymity through the use of pseudo-identity and dynamic indexing. The proposed method supports highly scalable systems with an efficient user registration process, allowing authenticated users to access both existing and newly added system entities without additional processes. Although the scheme is primarily designed for large systems, such as health infrastructure, it is also suitable for resource-constrained devices. The scheme relies on one-way cryptographic hashing functions and bitwise XOR operations. Additionally, a fuzzy mining algorithm is employed on the user side to verify the user's biometric information. L2AI adopts the "Real-Or-Random (ROR)" model for security proof and employs BAN logic for proof of authenticity. Formal security verification is conducted using the "Automatic Validation of Internet Security Protocols and Programs" (Proverif) tool, complemented by informal security analysis demonstrating the proper functionality of L2AI.

Summary: The internet of things (IoT) refers to the interconnection of various devices through the internet. With the help of IoT, different applications and devices can interact with each other and even with humans through the internet. However, the widespread use of IoT in homes, offices, and private companies has raised concerns about privacy and security. The theft of user information by hackers is a major challenge that has hindered the adoption of IoT. This paper proposes a method for improving security on two levels: intra‐cluster and inter‐cluster. In the intra‐cluster phase, the cluster head uses fuzzy logic to measure the trust level of nodes. If the node is trusted, the information is received and sent to the destination. In the inter‐cluster phase, data between cluster heads is encrypted using column transposition encryption. The encryption is hop by hop, and the encryption key is changed at each hop. The proposed method is simulated using NS‐2 software. A comparison of the proposed method's performance with conventional methods shows that the proposed method results in better packet delivery rate, throughput, and prevention of link spoofing attacks.

Internet of Things (IoT) means connecting different devices through the Internet. The Internet of things enables humans to remotely manage and control the objects they use with the Internet infrastructure. After the advent of the Internet of Things in homes, organizations, and private companies, privacy and information security are the biggest concern. This issue has challenged the spread of the Internet of things as news of the user’s theft of information by hackers intensified. The proposed method in this paper consists of three phases. In the first phase, a star structure is constructed within each cluster, and a unique key is shared between each child and parent to encrypt and secure subsequent communications. The second phase is for intra-cluster communications, in which members of the cluster send their data to the cluster head in a multi-hop manner. Also, in this phase, the data is encrypted with different keys in each hop, and at the end of each connection, the keys are updated to ensure data security. The third phase is to improve the security of inter-cluster communications using an authentication protocol. In this way, the cluster heads are authenticated before sending information to prevent malicious nodes in the network. The proposed method is also simulated using NS2 software. The results showed that the proposed method has improved in terms of energy consumption, end-to-end delay, flexibility, packet delivery rate, and the number of alive nodes compared to other methods.