The Internet of Things (IoT) has the potential to improve the complementary of communication, control, and information processing within the public transportation system. The IoT-enabled Intelligent Transportation System (ITS) ensures that automated transportation is networked and operated collaboratively. The IoT-enabled ITS has revolutionized the transportation industry by enabling the seamless integration of a wide range of devices and systems. It makes the strategic use of networked devices, sensors, and data analytics to improve transportation network efficiency, safety, and environmental friendliness. The usage of the IoT in the ITS has grown in popularity due to its capacity to improve traffic control, reduce congestion, facilitate live monitoring, and optimize transportation operations. The IoT-enabled ITS systems and devices must be protected from cyber-attacks for various reasons, including preserving sensitive data, guaranteeing privacy, preventing unauthorized access, and protecting against the risk of interruptions or manipulations. Malware attacks affect the working and performance of the deployed smart IoT devices. We propose a secure deep learning-enabled malware attack detection for IoT-enabled ITS (in short, SDLMA-IITS). The approach of explainable artificial intelligence (XAI) has been utilized for the effective detection of malware. A deep security analysis of the proposed SDLMA-IITS is presented to prove its security against various potential attacks. The comparative performance analysis of SDLMA-IITS is given with the other similar existing schemes. Finally, a practical implementation of SDLMA-IITS is provided to measure its impact on the security of the IoT-enabled ITS systems and devices.

The increasing prevalence of malicious activities in Internet of Things (IoT)-enabled healthcare and Internet of Medical Things (IoMT) systems necessitates robust intrusion detection mechanisms. This article introduces a novel approach combining meta-heuristic optimization and machine learning techniques to analyze network traffic for enhanced detection accuracy. Our proposed method utilizes 11 chaotic maps and the K-nearest neighbor (KNN) algorithm to identify malicious activity in IoMT and IoT network systems. Recognizing the significance of feature selection in network traffic intrusion detection, we employ the chaotic grey wolf optimizer (CGWO) to select the most relevant and impactful features for learning strategically. Our approach demonstrates superior performance through comprehensive experiments compared to well-known meta-heuristic algorithms and prior art methods, as evidenced by various evaluation metrics. This research contributes to advancing intrusion detection systems in healthcare IoMT and IoT, offering a reliable and efficient solution to safeguard against evolving cyber threats.

Smishing is a type of cyberattack that includes delivering a fake messages to normal users in order to get their personal information. Cybersecurity experts are now concerned about this sort of assault because, both the individuals and companies are loosing their money by this attack. This article presents a machine learning base comparison approach using different text analysis features (i.e., Discourse, Linguistic, Psycholinguistic, Persuasion Principle and Statistics based features). Here, different Machine Learning classifiers (i.e., Logistic Regression, Naive Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machine and XGBoost) have been used for smishing detection. The evaluation was performed on a prepared dataset. The dataset includes both smishing and normal messages. According to the comparative experimental results from the implemented classification algorithms, Random Forest classifier gives the best performance with the 99% accuracy.

Security on sensitive data of the patients is essential in e-medical system where, sensors and the servers use the insecure channels like ZigBee and Internet to exchange their information. An adversary may hamper the communications by mounting various attacks as well as he/she may try to get the sensitive data. Therefore, security system is needed so that it can protect the security attacks. This paper provides a new security protocol, referred as SecHealth when the healthcare sensors transmit their data to a local server known as fog server. In this case, after verifying each other (i.e., sensor and fog server), then only the sensors are able to transmit their data to the local fog server. However, security analysis of the proposed protocol states that it can overcome all the possible known attacks.

The concept of two-factor multiserver authentication protocol was developed to avoid multiple number of registrations using multiple smart-cards and passwords. Recently, a variety of two-factor multiserver authentication protocols have been developed. It is observed that the existing RSA-based multiserver authentication protocols are not suitable in terms of computation complexities and security attacks. To provide lower complexities and security resilience against known attacks, this article proposes a two-factor (password and smart-card) user authentication protocol with the RSA cryptosystem for multiserver environments. The comprehensive security discussion proved that the known security attacks are eliminated in our protocol. Besides, our protocol supports session key agreement and mutual authentication between the application server and the user. We analyze the proof of correctness of the mutual authentication and freshness of session key using the BAN logic model. The experimental outcomes obtained through simulation of the Automated Validation of Internet Security Protocols and Applications (AVISPA) S/W show that our protocol is secured. We consider the computation, communication, and storage costs and the comparative explanations show that our protocol is flexible and efficient compared with protocols. In addition, our protocol offers security resilience against known attacks and provides lower computation complexities than existing protocols. Additionally, the protocol offers password change facility to the authorized user.

Abstract Now‐a‐days, the low‐power handheld mobile devices make our life more comfortable. With the fast advancement of mobile communication technologies and Internet, mobile users are accessing remote services at home over the Internet. Recently, Tsai and Lo put forwarded a user authentication scheme for distributing mobile cloud environments. Unfortunately, we observed that Tsai and Lo's scheme suffers from user impersonation attack and known session‐specific temporary information attack. Besides, the scheme does not support the wrong password and fingerprint detection in the authentication phase. The scheme also violates the user anonymity property. Moreover, the password update functionality is absent in Tsai and Lo's scheme. In order to provide more securities and functionalities, this article put forwarded an enhanced scheme for distributing mobile cloud environments. The simulation on automated validation of Internet security protocols and applications tool ensures that our scheme is secure against the active and passive attacks. Our cryptanalysis gives surety that the scheme can defend related security attacks. We also compare our scheme with the previous schemes with respect to computation cost and security aspects. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract At the present time, application of online communication systems are rapidly increasing and most of the clients depend on a set of servers to fulfill their daily needs. In order to access these servers, a client (user) needs to register to each server with different login credentials. To circumvent this situation, the concept of multi‐server authentication has been adopted, where a user can access all the servers using a single login credential. In this paper, a two‐factor multi‐server authentication protocol, which is proposed by Leu and Hsieh, is analyzed and observed that the forgery attack and the off‐line password‐guessing attack can be made on it. Further, the off‐line password‐guessing attack and other security threats are found in similar kind of multi‐server authentication protocol, which is designed by Li et al. This paper mainly focuses on enhancing the securities of the previously mentioned protocols and thus proposed a new protocol. We have employed formal and informal security analysis to analyze the proposed protocol. The performance of our protocol is also compared with the related protocols. It can also be noted that the designed protocol accomplishes mutual authentication, session key verification, and identity and password change phases. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Client‐server‐based communications provide a facility by which users can get several services from home via the Internet. As the Internet is an insecure channel, it is needed to protect information of communicators. An authentication scheme can fulfill the aforementioned requirements. Recently, Huang et al. presented an elliptic curve cryptosystem‐based password authentication scheme. This work has demonstrated that the scheme of Huang et al. has security weakness against the forgery attack. In addition, this paper also presented that the scheme of Huang et al. has some design drawbacks. Therefore, this paper has focused on excluding the security vulnerabilities of the scheme of Huang et al. by proposing an elliptic curve cryptosystem‐based password authentication scheme using smart card. The security of our scheme is based on the hardness assumption of the one‐way hash functions and elliptic curve discrete logarithm problem. Furthermore, we have demonstrated that our scheme is secured against known attacks. The performance of our scheme is also nearly equal when compared to related competing schemes. Copyright © 2016 John Wiley & Sons, Ltd.