Effective anomaly detection is vital to minimize the economic impacts of security issues that Internet of Things (IoT) and Industrial Internet of Things (IIoT) face more frequently in recent years. Conventional anomaly detection methods have difficulties in finding new and complex anomalies. machine learning (ML) algorithms have proven to be excellent in anomaly detection with the development of ML research. However, labeled data is hard to get in real situations, so we need unsupervised learning methods. We suggest a new unsupervised learning method for detecting anomalies in multivariate time series, named Bi-Dual-GM GRU-AE. It has two Gate Mechanisms to better pick the combination part from different aspects of input features. These features are derived from different hidden sizes of GRU encoders that take both forward and backward time windows as inputs. We evaluated the proposed method on real-world IoT and IIoT datasets and showed that it outperforms the latest methods in the multivariate time series anomaly detection task. The proposed approach shows superior detection performance particularly in scenario where anomalies are relatively rare, offering a better detection solution to the modern challenges in IoT and IIoT security.

Yang and Chang (2009) proposed a three-party authenticated key exchange protocol for securing communications in mobile-commerce environments. Their protocol reduces computation and communication costs by employing elliptic curve cryptosystems. However, Tan (2010) pointed out that Yang and Chang (2009)’s protocol cannot withstand impersonation and parallel attacks, and further proposed an enhanced protocol to resist these attacks. This paper demonstrates that Tan (2010)’s approach still suffers from impersonation attacks, and presents an efficient and secure three-party authenticated key exchange protocol to overcome shown weaknesses.

Numerous smart card based authentication protocols have been proposed to provide strong system security and robust individual privacy for communication between parties these days. Nevertheless, most of them do not provide formal analysis proof, and the security robustness is doubtful. Chang and Cheng (2011) proposed an efficient remote authentication protocol with smart cards and claimed that their proposed protocol could support secure communication in a multi-server environment. Unfortunately, there are opportunities for security enhancement in current schemes. In this paper, we identify the major weakness, i.e., session key disclosure, of a recently published protocol. We consequently propose a novel authentication scheme for a multi-server environment and give formal analysis proofs for security guarantees.

The digital signature is a very important subject for network security. Considering multiple signers and multiple verifiers, Xie and Yu (2004) pointed out that the multisignature scheme of Laih and Yen (1996) is vulnerable to a harmful attack. An attack can occur when a specified group of verifiers cooperate to forge a multisignature by secret key substitution following the leak of a secret key or by group public key adjustment during the process of renewing membership. Xie and Yu proposed an improvement of Laih and Yen’s multisignature scheme. In this paper, we show that Xie and Yu’s scheme is vulnerable to clerk and rogue-key attacks. We propose an improved multisignature scheme to resist such attacks. In the proposed scheme, multiple signers can generate a multisignature for the message with the signers’ secret keys, and the specified group of verifiers can cooperate to verify the validity of the multisignature with the signers’ public keys and the verifiers’ secret key. The proposed scheme for a special verifier group not only has the advantages of Xie and Yu’s scheme, but also is secure against clerk and rogue-key attacks.