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Recently Internet of Things (IoT) attains tremendous popularity, although this promising technology leads to a variety of security obstacles. The conventional solutions do not suit the new dilemmas brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provides an opportunity to improve IoT security. In this work, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts Dendritic Cell Algorithm (DCA) and Self Normalizing Neural Network (SNN). The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset, performs signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, we compared these results with State-of-the-art techniques, which showed that our model is capable of performing better classification tasks than SVM, NB, KNN, and MLP. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection.

Medicine is the main means to reduce cancer mortality. However, some medicines face various risks during transportation and storage due to the particularity of medicines, which must be kept at a low temperature to ensure their quality. In this regard, it is of great significance to evaluate and select drug cold chain logistics suppliers from different perspectives to ensure the quality of medicines and reduce the risks of transportation and storage. To solve such a multiple criteria decision-making (MCDM) problem, this paper proposes an integrated model based on the combination of the SWARA (stepwise weight assessment ratio analysis) and CoCoSo (combined compromise solution) methods under the probabilistic linguistic environment. An adjustment coefficient is introduced to the SWARA method to derive criteria weights, and an improved CoCoSo method is proposed to determine the ranking of alternatives. The two methods are extended to the probabilistic linguistic environment to enhance the applicability of the two methods. A case study on the selection of drug cold chain logistics suppliers is presented to demonstrate the applicability of the proposed integrated MCDM model. The advantages of the proposed methods are highlighted through comparative analyses.

Two party authentication schemes can be good candidates for deployment in Internet of Things (IoT)-based systems, especially in systems involving fast moving vehicles. Internet of Vehicles (IoV) requires fast and secure device-to-device communication without interference of any third party during communication, and this task can be carried out after registration of vehicles with a trusted certificate issuing party. Recently, several authentication protocols were proposed to enable key agreement in two party settings. In this study, we analyze two recent protocols and show that both protocols are insecure against key compromise impersonation attack (KCIA) as well as both lack of user anonymity. Therefore, this paper proposes an improved protocol that does not only resist KCIA and related attacks, but also offers comparable computation and communication. The security of proposed protocol is tested under formal model as well as using well known Burrows–Abadi–Needham (BAN) logic along with a discussion on security features. While resisting the KCIA and related attacks, proposed protocol also provides comparable trade-of between security features and efficiency and completes a round of key agreement in just 13.42 ms, which makes it a promising candidate to be deployed in IoT environments.

Hospitality brand management is a primary concern in the hotel industry and the evaluation of brands can be considered as a decision-making problem with multiple criteria. The evaluation information of brands may be uncertain sometimes. The q-rung orthopair fuzzy set (q-R.O.F.S.), which represents the preference degree of a person from the positive and negative aspects, has turned out to be an efficient tool in depicting uncertainty and vagueness in the decision-making process. This article dedicates to presenting an integrated multiple criteria decision-making method with q-R.O.F.S.. Firstly, a score function of the q-R.O.F.S. is proposed to solve the deficiencies of two existing score functions. Then, a weight-determining method based on the additive consistency of the preference relation is developed. A decision-making method integrating the score function, the best worst method and the VIsekriterijumska optimizacija I KOmpromisno Resenje (V.I.K.O.R.) which means multiple criteria compromise optimisation in English) method is further proposed. Finally, a case study regarding the hospitality brand management is provided to show the applicability and validity of the proposed method.

The roaming service enables a remote user to get desired services, while roaming in a foreign network through the help of his home network. The authentication is a pre-requisite for secure communication between a foreign network and the roaming user, which enables the user to share a secret key with foreign network for subsequent private communication of data. Sharing a secret key is a tedious task due to underneath open and insecure channel. Recently, a number of such schemes have been proposed to provide authentication between roaming user and the foreign networks. Very recently, Lu et al. claimed that the seminal Gopi-Hwang scheme fails to resist a session-specific temporary information leakage attack. Lu et al. then proposed an improved scheme based on Elliptic Curve Cryptography (ECC) for roaming user. However, contrary to their claim, the paper provides an in-depth cryptanalysis of Lu et al.’s scheme to show the weaknesses of their scheme against Stolen Verifier and Traceability attacks. Moreover, the analysis also affirms that the scheme of Lu et al. entails incorrect login and authentication phases and is prone to scalability issues. An improved scheme is then proposed. The scheme not only overcomes the weaknesses Lu et al.’s scheme but also incurs low computation time. The security of the scheme is analyzed through formal and informal methods; moreover, the automated tool ProVerif also verifies the security features claimed by the proposed scheme.

In supply chain finance, the multiple criteria decision making (MCDM) problem of selecting a suitable third-party logistics (3PL) service supplier is of great significance to financial institutions. A suitable 3PL service supplier can not only help financial institutions carry out the supply chain finance business, but also can replace financial institutions to supervise the operation of a target financing supply chain, thus reducing the operational risks of financial institutions. As a useful MCDM method, the combined compromise solution (CoCoSo) method mainly combines a compromise decision algorithm with an aggregation strategy to obtain a compromise solution. This study extends the CoCoSo method to hesitant fuzzy linguistic environment to solve the multi-expert MCDM problem of the 3PL service supplier selection. Target criteria whose values are in linguistic forms are considered in the process of normalizing the decision matrix. A new integration approach with respect to subordinate compromise scores is introduced, and the subjective and objective weights of criteria are considered simultaneously in this extended process to avoid one-sidedness of criterion weights. A case study about the 3PL service supplier selection is given, in which the sensitivity analysis and comparative analysis are provided to highlight the advantages of the proposed method.

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