A Survey of Advanced Border Gateway Protocol Attack Detection Techniques.

The Internet's default inter-domain routing system, the Border Gateway Protocol (BGP), remains insecure. Detection techniques are dominated by approaches that involve large numbers of features, parameters, domain-specific tuning, and training, often contributing to an unacceptable computational cost. Efforts to detect anomalous activity in the BGP have been almost exclusively focused on single observable monitoring points and Autonomous Systems (ASs). BGP attacks can exploit and evade these limitations. In this paper, we review and evaluate categories of BGP attacks based on their complexity. Previously identified next-generation BGP detection techniques remain incapable of detecting advanced attacks that exploit single observable detection approaches and those designed to evade public routing monitor infrastructures. Advanced BGP attack detection requires lightweight, rapid capabilities with the capacity to quantify group-level multi-viewpoint interactions, dynamics, and information. We term this approach advanced BGP anomaly detection. This survey evaluates 178 anomaly detection techniques and identifies which are candidates for advanced attack anomaly detection. Preliminary findings from an exploratory investigation of advanced BGP attack candidates are also reported.

An Experimental Detection of Distributed Denial of Service Attack in CDX 3 Platform Based on Snort.

Distributed Denial of Service (DDoS) attacks pose a significant threat to internet and cloud security. Our study utilizes a Poisson distribution model to efficiently detect DDoS attacks with a computational complexity of O(n). Unlike Machine Learning (ML)-based algorithms, our method only needs to set up one or more Poisson models for legitimate traffic based on the granularity of the time periods during preprocessing, thus eliminating the need for training time. We validate this approach with four virtual machines on the CDX 3.0 platform, each simulating different aspects of DDoS attacks for offensive, monitoring, and defense evaluation purposes. The study further analyzes seven diverse DDoS attack methods. When compared with existing methods, our approach demonstrates superior performance, highlighting its potential effectiveness in real-world DDoS attack detection.

How education level influences internet security knowledge, behaviour, and attitude: a comparison among undergraduates, postgraduates and working graduates.

During the pandemic, the prevailing online learning has brought tremendous benefits to the education field. However, it has also become a target for cybercriminals. Cybersecurity awareness (CSA) or Internet security awareness in the education sector turns out to be critical to mitigating cybersecurity risks. However, previous research indicated that using education level alone to judge CSA level received inconsistent results. This study postulated Social Educational Level (SEL) as a moderator with an extended Knowledge-Attitude-Behaviour model, used students' year level as a proxy for the impact of education level, and used work exposure for the influence of social education level, to compare CSA among undergraduates, postgraduates and working graduates. The participants in the study were divided into six groups, namely year 1 university students, year 2-3university students, final-year students, postgraduate students, young working graduates, and experienced working graduates. The Human Aspects of Information Security Questionnaire was used to conduct a large-scale survey. The multivariate regression model analysis showed significant differences among the knowledge, attitude and behaviour dimensions across groups with different conditions of year-level and work exposure. However, it was found that SEL played a more significant role than an individual's education level. The study suggested that a greater endeavour be committed to educating the public at large together with individuals, institutes, corporate and governments to improve the national CSA level.

The influence of social education level on cybersecurity awareness and behaviour: a comparative study of university students and working graduates.

A multitude of studies have suggested potential factors that influence internet security awareness (ISA). Some, for example, used GDP and nationality to explain different ISA levels in other countries but yielded inconsistent results. This study proposed an extended knowledge-attitude-behaviour (KAB) model, which postulates an influence of the education level of society at large is a moderator to the relationship between knowledge and attitude. Using exposure to a full-time working environment as a proxy for the influence, it was hypothesized that significant differences would be found in the attitude and behaviour dimensions across groups with different conditions of exposure and that exposure to full-time work plays a moderating role in KAB. To test the hypotheses, a large-scale survey adopting the Human Aspects of Information Security Questionnaire (HAIS-Q) was conducted with three groups of participants, namely 852 Year 1-3 students, 325 final-year students (age = 18-25) and 475 full-time employees (age = 18-50) in two cities of China. MANOVA and subsequent PROCESS regression analyses found a significant negative moderating effect of work exposure, which confirmed the proposed model. However, the effect was more pervasive than expected and moderation was found in the interaction between work exposure and all three ISA dimensions. The social influence does not only reshape the cybersecurity attitude of the highly educated, but also knowledge and behaviour. Findings contribute theoretically, methodologically and practically, offering novel perspectives on ISA research and prompting new strategies to respond to human factors.

Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning.

Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website's content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies' detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model.

Indicators to assess preservice teachers' digital competence in security: A systematic review.

The goal of this review is to analyse the state of inquiry in the field of digital competence in security in initial teacher education, via indicators to assess preservice teachers' digital competence in security, in order to help find opportunities to improve their competence level. Following the parameters defined in the PRISMA declaration, the review uses a bibliographic research methodology to explore the WoS, Scopus and ERIC databases. After a search identifying a sample of 31 scholarly articles published between 2010 and 2021, we analyse the information obtained using descriptive statistics and content analysis. The results show a predominance of empirical research in the European context. These studies are quantitative and tend to use questionnaires. Our conclusion proposes the need to train preservice teachers in data protection and privacy, searching for and using Internet images with authorship screening, use of open software programs, and respect for online communication norms, as well as ethical and responsible technology use. All of these issues are implicitly and transversally linked to the area of digital competence in security.

Secure Communications for Resource-Constrained IoT Devices.

The importance of securing communications on the Internet of Things (IoT) cannot be overstated. This is especially the case in light of the increasing proliferation of IoT devices and instances, as well as the growing dependence on their usage. Meanwhile, there have recently been mounting concerns over a wide array of vulnerabilities in IoT communications. The objective of this work is to address constraints in IoT devices that are "resource-constrained", which are devices that are limited in terms of computing, energy, communication, or range capabilities, whether in terms of nominal or temporal limitations. Specifically, we propose a framework for resource-aiding constrained devices to facilitate secure communication. Without loss of generalization, the framework's viability is illustrated by focusing on a group of security functions that utilize message authentication codes, which is a strongly representative example of resource-intensive security functions. Aspects of the framework are further demonstrated in processing cores commonly used in commercial IoT devices.

An Efficient, Anonymous and Robust Authentication Scheme for Smart Home Environments.

In recent years, the Internet of Things (IoT) has exploded in popularity. The smart home, as an important facet of IoT, has gained its focus for smart intelligent systems. As users communicate with smart devices over an insecure communication medium, the sensitive information exchanged among them becomes vulnerable to an adversary. Thus, there is a great thrust in developing an anonymous authentication scheme to provide secure communication for smart home environments. Most recently, an anonymous authentication scheme for smart home environments with provable security has been proposed in the literature. In this paper, we analyze the recent scheme to highlight its several vulnerabilities. We then address the security drawbacks and present a more secure and robust authentication scheme that overcomes the drawbacks found in the analyzed scheme, while incorporating its advantages too. Finally, through a detailed comparative study, we demonstrate that the proposed scheme provides significantly better security and more functionality features with comparable communication and computational overheads with similar schemes.

Combating Barriers to the Development of a Patient-Oriented Frailty Website.

Unlabelled: This viewpoint article, which represents the opinions of the authors, discusses the barriers to developing a patient-oriented frailty website and potential solutions. A patient-oriented frailty website is a health resource where community-dwelling older adults can navigate to and answer a series of health-related questions to receive a frailty score and health summary. This information could then be shared with health care professionals to help with the understanding of health status prior to acute illness, as well as to screen and identify older adult individuals for frailty. Our viewpoints were drawn from 2 discussion sessions that included caregivers and care providers, as well as community-dwelling older adults. We found that barriers to a patient-oriented frailty website include, but are not limited to, its inherent restrictiveness to frail persons, concerns over data privacy, time commitment worries, and the need for health and lifestyle resources in addition to an assessment summary. For each barrier, we discuss potential solutions and caveats to those solutions, including assistance from caregivers, hosting the website on a trusted source, reducing the number of health questions that need to be answered, and providing resources tailored to each users' responses, respectively. In addition to screening and identifying frail older adults, a patient-oriented frailty website will help promote healthy aging in nonfrail adults, encourage aging in place, support real-time monitoring, and enable personalized and preventative care.

A deeper look into cybersecurity issues in the wake of Covid-19: A survey.

This study analyzed the Coronavirus (COVID-19) crisis from the angle of cyber-crime, highlighting the wide spectrum of cyberattacks that occurred around the world. The modus operandi of cyberattack campaigns was revealed by analyzing and considering cyberattacks in the context of major world events. Following what appeared to be substantial gaps between the initial breakout of the virus and the first COVID-19-related cyber-attack, the investigation indicates how attacks became significantly more frequent over time, to the point where three or four different cyber-attacks were reported on certain days. This study contributes in the direction of fifteen types of cyber-attacks which were identified as the most common pattern and its ensuing devastating events during the global COVID-19 crisis. The paper is unique because it covered the main types of cyber-attacks that most organizations are currently facing and how to address them. An intense look into the recent advances that cybercriminals leverage, the dynamism, calculated measures to tackle it, and never-explored perspectives are some of the integral parts which make this review different from other present reviewed papers on the COVID-19 pandemic. A qualitative methodology was used to provide a robust response to the objective used for the study. Using a multi-criteria decision-making problem-solving technique, many facets of cybersecurity that have been affected during the pandemic were then quantitatively ranked in ascending order of severity. The data was generated between March 2020 and December 2021, from a global survey through online contact and responses, especially from different organizations and business executives. The result show differences in cyber-attack techniques; as hacking attacks was the most frequent with a record of 330 out of 895 attacks, accounting for 37%. Next was Spam emails attack with 13%; emails with 13%; followed by malicious domains with 9%. Mobile apps followed with 8%, Phishing was 7%, Malware 7%, Browsing apps with 6%, DDoS has 6%, Website apps with 6%, and MSMM with 6%. BEC frequency was 4%, Ransomware with 2%, Botnet scored 2% and APT recorded 1%. The study recommends that it will continue to be necessary for governments and organizations to be resilient and innovative in cybersecurity decisions to overcome the current and future effects of the pandemic or similar crisis, which could be long-lasting. Hence, this study's findings will guide the creation, development, and implementation of more secure systems to safeguard people from cyber-attacks.

A comprehensive survey of AI-enabled phishing attacks detection techniques.

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client's sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.