Ransomware attacks constitute major security threats to personal and corporate data and information. A successful ransomware attack results in significant security and privacy violations with attendant financial losses and reputational damages to owners of computer-based resources. This makes it imperative for accurate, timely and reliable detection of ransomware. Several techniques have been proposed for ransomware detection and each technique has its strengths and limitations. The aim of this paper is to discuss the current trends and future directions in automated ransomware detection. The paper provides a background discussion on ransomware as well as historical background and chronology of ransomware attacks. It also provides a detailed and critical review of recent approaches to ransomware detection, prevention, mitigation and recovery. A major strength of the paper is the presentation of the chronology of ransomware attacks from its inception in 1989 to the latest attacks occurring in 2021. Another strength of the study is that a large proportion of the studies reviewed were published between 2015 and 2022. This provides readers with an up-to-date knowledge of the state-of-the-art in ransomware detection. It also provides insights into advances in strategies for preventing, mitigating and recovering from ransomware attacks. Overall, this paper presents researchers with open issues and possible research problems in ransomware detection, prevention, mitigation and recovery.

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The protection of sensitive information is among the top priorities of organizations that are involved in manufacturing. Because of this, businesses are afraid of sharing their data with other parties so that they may construct predictive and prognostic models. In such a situation, it is difficult to construct complete models to forecast the breakdown of assets since data from a single firm would not provide the needed range of operating regimes and failure types. Recently, the Internet of Things (IoT) has gotten a lot of interest because of the vast variety of applications it has in numerous fields that communicate across multiple levels of the Internet infrastructure. The Internet of Things is composed of three layers: the physical layer, the network layer, and the application layer. This paper discusses security threats and responses for each layer of the IoT. The research examines different current state-of-the-art IoT security frameworks and suggests a unified IoT network security framework name "A Unified Federated Security Framework." The fuzzy cognitive maps used in the proposed framework are used to represent and evaluate trust connections between entities in federated identity management systems. For Internet of Things networks, the unified federated security architecture suggested in this paper offers comprehensive security characteristics. It also allows for the accurate categorization of all assaults and the capture of the different dangers, which allows for the development and implementation of improved defenses.

Direct storage of biometric templates in databases exposes the authentication system and legitimate users to numerous security and privacy challenges. Biometric cryptosystems or template protection schemes are used to overcome the security and privacy challenges associated with the use of biometrics as a means of authentication. This paper presents a review of previous works in biometric key binding and key generation schemes. The review focuses on key binding techniques such as biometric encryption, fuzzy commitment scheme, fuzzy vault and shielding function. Two categories of key generation schemes considered are private template and quantization schemes. The paper also discusses the modes of operations, strengths and weaknesses of various kinds of key-based template protection schemes. The goal is to provide the reader with a clear understanding of the current and emerging trends in key-based biometric cryptosystems.