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With the deployment of the 5G cellular system, the upsurge of diverse mobile applications and devices has increased the potential challenges and threats posed to users. Industry and academia have attempted to address cyber security challenges by implementing automated malware detection and machine learning algorithms. This study expands on previous research on machine learning-based mobile malware detection. We critically evaluate 154 selected articles and highlight their strengths and weaknesses as well as potential improvements. We explore the mobile malware detection techniques used in recent studies based on attack intentions, such as server, network, client software, client hardware, and user. In contrast to other SLR studies, our study classified the means of attack as supervised and unsupervised learning. Therefore, this article aims at providing researchers with in-depth knowledge in the field and identifying potential future research and a framework for a thorough evaluation. Furthermore, we review and summarize security challenges related to cybersecurity that can lead to more effective and practical research.

In the past, phishing techniques were a common means of attack carried out by individuals or small groups via spam mail on a randomly selected target. However, in recent years, phishing techniques have been adopted by advanced persistent threat (APT) groups to attack organizations such as the Sony Pictures Enterprise and Korea Hydro & Nuclear Power. As such, our study aims to analyze the past campaigns conducted by the APT groups. We aim to propose a countermeasure that corresponds to the phishing campaign by collecting datasets pertaining to the phishing techniques. Based on our past study, we collected private and public data from 16 different cases that utilize a phishing attack. Our research adopted MITRE’s ATT&CK framework and tactic, techniques, and procedures (TTPs) to extract and examine the various campaigns. The framework proposed in this study makes considerable contributions to both the private and public sectors, as the framework aids the organizations in counteracting the malicious threats performed by the APT groups.

Despite the extensive ramifications of illegal online gambling (IOG) services, actions taken by government authorities have had little effect in halting these operations. In order to reduce the prevalence of IOG, the ability to detect malicious uniform resource locators (URLs) is crucial. Text mining and binary classification have been widely adopted to detect and prevent spam short message services (SMSs), but government authorities and various task forces that monitor and regulate gambling also rely on the analysis of malicious URLs. This study proposes a novel system to analyse the characteristics of spam URLs, offering a method that can assist government agencies combatting mobile IOG sites.

With the development of IoT devices, wearable devices are being used to record various types of information. Wearable IoT devices are attached to the user and can collect and transmit user data at all times along with a smartphone. In particular, sensitive information such as location information has an essential value in terms of privacy, and therefore some IoT devices implement data protection by introducing methods such as masking. However, masking can only protect privacy to a certain extent in logs having large numbers of recorded data. However, the effectiveness may decrease if we are linked with other information collected from within the device. Herein, a scenario-based case study on deanonymizing anonymized location information based on logs stored in wearable devices is described. As a result, we combined contextual and direct evidence from the collected information. It was possible to obtain the result in which the user could effectively identify the actual location. Through this study, not only can a deanonymized user location be identified but we can also confirm that cross-validation is possible even when dealing with modified GPS coordinates.

The development of information and communication technologies extended the application of digitalized industrial control systems (ICSs) to critical infrastructure. With this circumstance, emerging sophisticated cyberattacks by adversaries, including nation-backed terrorists, target ICSs due to their strategic value that critical infrastructure can cause severe consequences to equipment, people, and the environment due to the cyberattacks on ICSs. Therefore, critical infrastructure owners should provide high assurance to those involved, such as neighboring residents and governments, that the facility is adequately protected against cyberattacks. The risk assessment that identifies, estimates, and prioritizes risks is vital to provide high assurance. This study proposes a framework for evaluating risks by quantifying the likelihood of cyber exploitation and the consequences of cyberattacks. The quantification of the likelihood of cyber exploitation is inspired by research on Bayesian attack graphs (BAGs), allowing probability evaluation that considers the causal relationship between ICSs and multistage attacks. For the cyberattack consequences quantification, we propose a methodology to evaluate how far an impact will spread and thus how many functions will be influenced when an ICS is exploited. The methodology is conducted by ICS experts identifying and listing functional dependencies and essential function goals among ICSs that they are already familiar with and do not require in-depth cybersecurity knowledge. Through experiments, we demonstrated how to apply our framework to assess the risks of the plant protection system, which is a safety-grade digital system used in nuclear power plants. The result shows that risk can be multidimensionally assessed than previous literature, such as discovering that components that were not considered important have high risk due to their functional connectivity.

Biometric devices play an integral role in consumer’s daily life, providing a seamless environment. However, it is essential to measure the usability of biometrics, owing to the elements of biometrics satisfying both usability and security. This study redefines the elements of biometrics pertaining to usability determined in previous studies and adds elements of psychological relevance, such as privacy concerns. To organize the interrelated usability structure systemically, this paper applies the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) to derive the usability structure. Thereupon, the established structure is applied in the clustered weighted Analytical Network Processes (ANP) to generate the proposed usability evaluation model. By these methods, the pertinent relationships between the factors are clarified and the weight of each element is determined. In the empirical study, 106 students measured usability of the fingerprint recognition system, iris recognition system, and facial recognition system employing our usability evaluation model. The results of this model generate the quantitative score of usability for biometric systems and suggest strategies to increase the score. The proposed usability evaluation model can comprehensively assist usability practitioners to evaluate biometric systems.

COVID-19 has prompted a rise in telecommuting practices in most companies worldwide. Meanwhile, companies are struggling to cope with the new and evolving security threats in telecommuting using old control methods. Specifically, there is an increased danger of hacking attacks in telecommuting environments. Furthermore, corporate concerns regarding telecommuting security have led to a questioning of existing control methods that no longer seem adequate. Significant research has been conducted on the factors that improve the effectiveness of corporate security policies, such as formal control, informal control, and extrarole behaviors. However, these studies did not consider telecommuting environments, which surged after the COVID-19 outbreak. Telecommuting loosens the physical control over employees and eliminates the collegial environment in which employees encourage each other to protect system information. This study determined how the factors that influence the effectiveness of existing information security policies behave in a telecommuting environment. Our study shows that specification and mandatoriness are the most important factors for an effective telecommuting security policy. We conclude that this sudden change in the working environment has rendered existing security controls obsolete, and specification and mandatoriness are likely to receive increasingly more attention in the growing field of telecommuting security policy.

Financial fraud under IoT environment refers to the unauthorized use of mobile transaction using mobile platform through identity theft or credit card stealing to obtain money fraudulently. Financial fraud under IoT environment is the fast-growing issue through the emergence of smartphone and online transition services. In the real world, a highly accurate process of financial fraud detection under IoT environment is needed since financial fraud causes financial loss. Therefore, we have surveyed financial fraud methods using machine learning and deep learning methodology, mainly from 2016 to 2018, and proposed a process for accurate fraud detection based on the advantages and limitations of each research. Moreover, our approach proposed the overall process of detecting financial fraud based on machine learning and compared with artificial neural networks approach to detect fraud and process large amounts of financial data. To detect financial fraud and process large amounts of financial data, our proposed process includes feature selection, sampling, and applying supervised and unsupervised algorithms. The final model was validated by the actual financial transaction data occurring in Korea, 2015.

In the era of Internet of Things (IoT), impact of social media is increasing gradually. With the huge progress in the IoT device, insider threat is becoming much more dangerous. Trying to find what kind of people are in high risk for the organization, about one million of tweets were analyzed by sentiment analysis methodology. Dataset made by the web service “Sentiment140” was used to find possible malicious insider. Based on the analysis of the sentiment level, users with negative sentiments were classified by the criteria and then selected as possible malicious insiders according to the threat level. Machine learning algorithms in the open-sourced machine learning software “Weka (Waikato Environment for Knowledge Analysis)” were used to find the possible malicious insider. Decision Tree had the highest accuracy among supervised learning algorithms and K-Means had the highest accuracy among unsupervised learning. In addition, we extract the frequently used words from the topic modeling technique and then verified the analysis results by matching them to the information security compliance elements. These findings can contribute to achieve higher detection accuracy by combining individual’s characteristics to the previous studies such as analyzing system behavior.