Social Engineering Tactics Research Articles

Beyond Technical Barriers: A Multidimensional Conceptual Framework for Understanding and Countering Cyber Scam Susceptibility

Cyber-attacks have become a serious global cybersecurity issue, affecting sectors such as petroleum, finance, health, and industry. The financial and banking sectors are particularly vulnerable, experiencing disproportionate harm due to the escalating complexity of digital threats. Recent academic efforts have focused on developing countermeasures, but these have primarily concentrated on technical solutions, which alone are insufficient. The persistent success of cyber-attacks highlights the need for a better understanding of human susceptibility to cyber scams. This research addresses the gap by investigating the psychological and social factors that lead individuals to fall for scam emails and links. It also explores how hackers exploit social engineering tactics to target victims and seeks to reverse these tactics to enhance protection. By understanding why people fall for cyber scams, the research aims to develop more effective cybersecurity measures, contributing significantly to the ongoing battle against cyber-attacks.

Non-Deterministic and Risk Based Security Services

This paper presents a comprehensive framework to address these challenges. Understanding various social engineering tactics is crucial for effective prevention and detection. Trust based models in entities enable many business objectives that may include speed to market, scalability, decentralization etc However, they also increase the attack surface due to "loose boundaries" between enforcement points or corresponding resource authorization servers and the service orchestration layer. The control points of enforcement are generally static across a spectrum of threat vectors such as Identity, Fraud, Authentication, Authorization, Cyber security and physical security etc. in this paper we propose a "Trust based security framework aka " Interdiction Services" that fundamentally is non-deterministic and risk based. This paper presents a converged security framework towards a comprehensive prevention and detection controls mechanism. The paper proposes a converged security framework that allows various parties from fraud, cyber, and physical security to collaborate but operate independently through a common framework of Interdiction Services.

An intelligent security mechanism in mobile Ad-Hoc networks using precision probability genetic algorithms (PPGA) and deep learning technique (Stacked LSTM)

The Mobile Ad-hoc Networks (MANETs) have gained a significant attention in the recent years due to their proliferation and huge application purposes. To defend from many types of modern cyber dangers like Distributed Denial of Service (DDoS) attacks, Advanced Persistent Threats (APTs), Insider Threats, Ransomware, Zero-Day Exploits, Social Engineering tactics, and etc is not easy when it comes to keeping MANETs security. These complex assaults focus on network infrastructure, take advantage of weaknesses in communication protocols and control user actions. Although there have been improvements in intrusion detection systems (IDS), it is still difficult to fully safeguard MANETs. The purpose of this research is to create advanced methods that can accurately find and decrease attacks inside MANETs. Applying Sensor-based Feature Extraction (SFE) to extract useful network features such as Received Signal Strength Indication (RSSI) and Time of Travel (TOT) from datasets NSL-KDD and CICIDS-2017. Utilizing the fresh method of Precise Probability Genetic Algorithm (PPGA) optimization for removing unrelated details, which enhances precision in detecting attacks. Predicting normal and attacking labels by applying Stacked Recurrent Long Short Term Memory (SRLSTM) method, fine-tuning classifier's parameters in every layer to improve outcomes. In order to authenticate and compare the suggested methods with current attack detection tactics, this study will make use of various evaluation measurements. The NSL-KDD, which is a benchmark dataset in network intrusion detection research, has a wide variety of network traffic data with instances that are labeled as normal and different attacks. CICIDS-2017 is similar because it contains an extensive dataset too - this includes real-world traces from network traffic where there's both regular activity and harmful actions. The purpose is to enhance the existing status of MANET security so as it can withstand more strongly against cyber dangers. According to the outcomes, it is analyzed that the attack detection accuracy has improved greatly 99 % when compared to other methods, as shown by the detailed assessment measurements. Better handling of big datasets with top detection accuracy reduces the time needed 8.9 s for training and testing models. Decrease in misclassification results and better ability to differentiate normal network actions from harmful intrusions. Improved resistance of MANETs to different cyber dangers, guaranteeing the safety and dependability of network communication in changing and non-centralized settings.

BANKING SECURITY AND ATMs

As the fiscal sector relies on automated teller machines (ATMs) for client deals, the security of these bias has come consummate. This exploration paper explores contemporary challenges and advancements in ATM security, fastening on vulnerabilities that hang banking systems and strategies to alleviate these pitfalls. We examine colorful attack vectors, including physical tampering, cyber-attacks, and social engineering tactics, and their counteraccusations for fiscal institutions and consumers. This paper also reviews current security measures, similar as encryption, biometrics, and machine literacy- grounded fraud discovery, assessing their effectiveness in precluding unauthorized access and icing sale integrity. By assaying recent case studies and security breaches, we give perceptivity into arising trends and recommend stylish practices for enhancing ATM security. Our findings emphasize the need for amulti-layered approach to securing ATMs, integrating technological inventions with robust functional protocols to cover against evolving pitfalls.

Development of a Phishing Detection System Using Support Vector Machine

Phishing represents a significant and escalating threat within the cyber domain, inflicting substantial financial losses on internet users annually. This illicit practice leverages both social engineering tactics and technological means to unlawfully obtain sensitive information from individuals online. Despite numerous studies and publications exploring various methodologies to combat phishing, the number of victims continues to surge due to the inefficiencies of current security measures. The inherently anonymous and unregulated nature of the internet further compounds its susceptibility to phishing attacks. While it's commonly believed that successful phishing endeavours involve the creation of replica messages or websites to deceive users, this notion has not undergone systematic examination to identify potential vulnerabilities. This paper endeavours to fill this gap by conducting a comprehensive evaluation of phishing, synthesizing diverse research perspectives and methodologies. It introduces an innovative classification method utilizing Support Vector Machine (SVM), achieving an impressive accuracy rate of 96.4% in detecting phishing attempts. By implementing this model to distinguish between phishing and legitimate URLs, the proposed solution offers a valuable tool for individuals and organizations to promptly identify and mitigate phishing threats. The findings of this study hold significant implications for bolstering internet security measures and enhancing user awareness in navigating potentially malicious online content.

Impact Of Web (URL) Phishing and Its Detection

Abstract: Web phishing is a persuasive and evolving cyber threat that poses significant risks to individuals, businesses, and organizations in the modern digital age. This paper aims to provide an overview of web phishing, focusing on its methods, detection techniques, and prevention. Phishing attacks occur when malicious actors use deceptive practices to trick people into divulging sensitive or classified information such as passwords, credit card details, or personal data. These attacks primarily manifest through emails, websites, or social engineering tactics. Phishing emails often impersonate trusted entities and lure recipients into clicking on malicious links or downloading harmful attachments. Web phishing involves using fraudulent websites that mimic legitimate ones to steal user information or deliver malware. Detecting web phishing attacks is an ongoing challenge due to the sophistication of attackers. Several detection techniques have been developed, including heuristic analysis, machine learning algorithms, and real-time URL analysis. These methods analyze various attributes of websites and emails to identify suspicious patterns or behaviors. Detecting web phishing is equally essential. Effective prevention strategies include user education and awareness programs, using two-factor authentication, regular software updates, and deploying advanced email filtering and anti-phishing tools. User training is crucial in helping individuals recognize phishing attempts and avoid falling victim to them.

Transformation of Crimes (Cybercrimes) in Digital Age

The cybercrime thrives, understanding its evolution is critical for security and policy interventions. This research profiles cyber threat trends since the 1950s alongside legal developments. Background examines early hacking cultures, antivirus innovations, and seminal cases that set precedents. The objectives encompass analyzing major technical and legal inflection points over recent decades regarding cybercrimes. The study's aim is validating escalating cyber risks that demand urgent attention. Doctrinal methodology leverages scholarly journals, case law, and industry data. Key findings reveal sophisticated social engineering tactics, gaps in legal deterrence frameworks, and infrastructure vulnerabilities that underscore needs for global cooperation. Recommendations center on proactive reforms to cybersecurity statutes, public awareness, and cross-border collaboration to mitigate emerging threats. Tracing cybercrime's timeline provides vital context to catalyze preemptive actions against potential attacks in an increasingly interconnected online ecosystem.

Social automation and APT attributions in national cybersecurity

ABSTRACT Advanced Persistent Threats (APTs) are a prime concern in the formulation and implementation of national cybersecurity policies. These threats often also involve complex social engineering tactics which are undergoing a quantitative and qualitative revolution with burgeoning AI capabilities. However, the attribution of these APT activities can be mired with technical and political considerations. We analysed 142 APT groups’ attributions along with their use of social interaction vectors to ascertain the nature of the risk environment and the operational threat landscape of AI and social automation. We discover that close to 80% of APT activities could be chalked up to merely 20% of key nation-state threat actors competing with each other. We further discuss the implications of this political threat distribution for national cybersecurity environments.

Cybersecurity in the face of information warfare and cyberattacks

Cybersecurity in the face of information warfare and cyberattacks in the modern world is one of the most critical issues of our time. Driven by the rapid development of technologies and digital transformation, threats to information security are becoming even more severe. Nowadays, almost every aspect of our lives, from personal data to critical infrastructure systems, is affected by cyberattacks. Information warfare has become a complex struggle where digital battles are integral to modern conflict. Countries, corporations, and even criminals fight for control over data and influence over critical systems. All this creates a situation where a defense against cyber threats is a top priority. The article aims to provide a detailed analysis and understanding of cyber threats' dynamics in the context of information warfare. It also highlights effective strategies and measures to ensure cybersecurity in such a challenging environment. The research methodology is based on comparing and analyzing information on real cyberattacks, studying the vulnerabilities of information systems, and developing strategies and technologies to counter cyber threats. The study employs both quantitative and qualitative methods, including statistical analysis and case studies. The results of the study revealed the main features of information warfare and their impact on cybersecurity globally and within specific organizations and states. Conflicting parties can use digital means to attack opponents, destroy their critical infrastructure, obtain secret information, or disrupt their operations. Information operations may use phishing and social engineering tactics to access essential resources or information. The organizations and states that find themselves at the epicenter of information warfare must carefully ensure the cybersecurity of their information resources.

Social Engineering in Social Media Networks and Its Impact on Digital and Societal Security in the Sultanate of Oman: An Exploratory Study

Social engineering within social media networks presents a growing concern for public safety in the Sultanate of Oman. The risks associated with such attacks primarily arise from human vulnerabilities rather than technical failures. Consequently, this research conducted an in-depth examination of the ramifications of social engineering and the responses of Omani society to these threats, revealing their significant implications at the national level. Social engineering poses a substantial risk to public security by exploiting users into disclosing sensitive information, thereby necessitating enhanced security protocols to protect personal identities and financial assets. Utilizing a descriptive analytical approach, the study administered a questionnaire to a sampled population, comprising five sections related to demographic profiles, awareness of social engineering, its social and psychological impacts, proficiency in countering such schemes, and the various forms of social engineering attacks. The findings highlighted disparities in Omani societal attitudes towards social engineering, evident across gender, age, educational attainment, and occupational categories. This underscores the need to develop tailored security strategies that account for these variations to enhance protective measures. The study advocates for increasing digital literacy among Omani citizens and developing skills to combat social engineering tactics. These recommendations should form essential components of a cohesive national strategy aimed at strengthening digital security. Moreover, the research emphasizes the importance of exploring technological solutions to address social engineering challenges and enhance digital literacy. It stresses the necessity of collaborative efforts across sectors to cultivate a secure digital environment in the Sultanate of Oman, while highlighting the importance of continuous improvement and the implementation of comprehensive strategies to effectively counter social engineering threats.

Leveraging NLP and Deep Learning for Phishing Detection and Anti-Phishing Training in Nigeria: A Focus on Localized Tactics and Cultural Factors

Phishing attacks pose a significant cybersecurity threat globally, with developing nations like Nigeria facing unique challenges due to localized tactics and cultural factors. This paper presents a novel approach to phishing mitigation in Nigeria, leveraging Natural Language Processing (NLP) and Deep Learning techniques to enhance both automated detection and user training. We analyze a corpus of Nigeria-specific phishing attempts, identifying linguistic patterns and cultural references commonly exploited by attackers. Using this data, we train a deep learning model capable of detecting localized phishing content with high accuracy. Building on this technical foundation, we design a dynamic anti-phishing training program that adapts to individual user behavior and local phishing trends. A Hybrid Deep learning modelsrecurrent neural networks (RNNs) and transformer-based models (BERT), was trained on large datasets of phishing and legitimate samples to learn discriminate features and classify new instances. Our results demonstrate significant improvements in both automated phishing detection rates and user resilience to social engineering tactics. The model achieved high precision (0.89), recall (0.94), and F1-scores (0.92, 1.00).This research contributes to the field by showcasing the potential of combining advanced AI techniques with culturally informed strategies to create more effective, localized cybersecurity solutions.

Encountering social engineering activities with a novel honeypot mechanism

<span lang="EN-US">Communication and conducting businesses have eventually transformed to be performed through information and communication technology (ICT). While computer network security challenges have become increasingly significant, the world is facing a new era of crimes that can be conducted easily, quickly, and, on top of all, anonymously. Because system penetration is primarily dependent on human psychology and awareness, 80% of network cyberattacks use some form of social engineering tactics to deceive the target, exposing systems at risk, regardless of the security system's robustness. This study highlights the significance of technological solutions in making users more safe and secure. Throughout this paper, a novel approach to detecting and preventing social engineering attacks will be proposed, combining multiple security systems, and utilizing the concept of Honeypots to provide an automated prevention mechanism employing artificial intelligence (AI). This study aims to merge AI and honeypot with intrusion prevention system (IPS) to detect social engineering attacks, threaten the attacker, and restrict his session to keep users away from these manipulation tactics.</span>

A Fraud Prevention and Secure Cognitive SIM Card Registration Model

Background: Most subscriber identification module (SIM) which usually finds their way to mobile phone users are primarily unregistered or pre-registered. Criminals buy these SIM cards, which have fake personal information, activate them and then use them as a channel of attacking vulnerable mobile phone users. Objective: to investigate the existing standards of the registration process, the weakness and how fraudsters leverage the shortcomings of the existing registration to attack unsuspecting subscribers. Methods: The study also proposed an automated theoretical model as an augmented model to ensure the SIM registration process and implementation become secure. Results: In our investigation, we identified that there had been a rise in fraudulent activities in Ghana, and the criminals have adapted to the new trend of committing a crime using mobile phones. The research presented a proposed conceptual model and algorithm for the new SIM registration. The study further conducted a comparative analysis of the principal component adopted to measure the robustness of the registration platform. The criminals mostly use social engineering tactics to trick their victims into disclosing sensitive information or sending money for services yet to be rendered. MNOs request an ID card before registering and activating SIMs, yet criminals can outwit the registration processes and get SIM cards registered through unapproved channels. Conclusion: We found out that the robustness of our model shall prevent SIM pre-registration and unapproved SIM activation due to verification mechanisms in the proposed model. A cognitive learningsystem has automated the registration process that can identify multiple registrations and prevent unapproved activation. Keywords: SIM Fraud; SIM card; Cognitive System; Mobile Network Operators; Intelligent decisionmaking; SIM Registration

Cybercrime in Ghana and Victims Accounts

The main thrust of the study was to examine how cybercrime victims are being lured into virtual space by scammers and later swindled. Qualitative research method was advanced to explore the dynamics of cybercrime activities in Ghana. Secondary data were sourced from the Criminal Investigation Department of the Ghana Police Service. Purposive sampling technique was outlined to engage with the respondents. Structured interview guide was considered as a data collection instrument. In the data analysis, each of the in-depth interviews was transcribed as soon as the information was gathered and developed into codes. Six (6) internet victims took part in the study. The investigation revealed that people become internet victims as a result of unrealistic profit ventures, online romance, raffle schemes, America green card lottery and rent apartment deals. Out of the six cases, only one culprit was imprisoned and even the approach was conventional policing strategy considering the volatile nature of the cybercrime related offences. It was concluded from the discussions that the National Commission for Civic Education (NCCE) should create public awareness about the social and economic consequences of cybercrime activities in the country. The education should further emphasize on the social engineering tactics employed by the conmen to dupe their client on the internet.
 
 Received: 8 March 2022 / Accepted: 25 April 2022 / Published: 5 May 2022

Detection and Handling of Threats in Pre-Established Networks Through a Junior Perspective in Internship Scenario

In this paper, there are two questions that we seek to answer, what are the tools used in the industry nowadays regarding its cybersecurity? How is cybersecurity being approached and dealt with by the companies and their greatest difficulties? Along the way it’s observed how the entire interaction with clients is made as also how the investigation on possible threats is conducted and handled, passing through how to detect, analyze and interact with the client team in the mitigation of it, this taking into consideration their infrastructure and capabilities. This paper is taken from the point of view of a junior utilizing free tools to analyze threats and dealing with attempts of infiltrating the network utilizing social engineering tactics as well as more technical skills.

Ensuring Smartphone Security Through Real-Time Log Analysis

Smartphones have become an essential part of one’s routine life. The use of smartphones has increased over the decade due to ever-growing functionalities, high-bandwidth internet, data storage and the social need to get connected with others. Smartphones are the preferred devices for accessing email, chatting, browsing, financial transactions etc. thus making them a pocket-sized computer with tons of valuable features. However, smartphones are designed by keeping functionality in mind rather than security. All these functionalities have enhanced convenience for the user but these same technologies have also opened up new doors for cyber threats. A threat actor uses social engineering tactics to trick users into providing sensitive information. Today, smartphones are associated with a number of risks since connections such as Wi-Fi networks and Bluetooth can act as vulnerable points for data exfiltration. The use of third-party applications may end-up launching the malware and revealing our private information. Thus, ensuring mobile security, integrity and privacy is a fundamental requirement. This paper proposes a methodology that would help the analyst to tackle mobile threats by collecting logs using MTD (Mobile Threat Defense) and analyze those logs using Splunk SIEM (Security Information and Event Management) Solution. The MTD solution continuously scans the mobile for any suspicious activities and provides real-time logs. The SIEM solution provides a robust search system and thus can monitor and correlate logs coming from various MTD agents. Rather than using the MTD console for analysis, this paper emphasizes the use of the SIEM solution console for better results. This proposed implementation of integrating MTD with SIEM is found to be effective in real-time handling of cyber-attacks at the device, network, and application levels.

"So-called privacy breeds evil"

A growing body of research suggests that intimate partner abusers use digital technologies to surveil their partners, including by installing spyware apps, compromising devices and online accounts, and employing social engineering tactics. However, to date, this form of privacy violation, called intimate partner surveillance (IPS), has primarily been studied from the perspective of victim-survivors. We present a qualitative study of how potential perpetrators of IPS harness the emotive power of sharing personal narratives to validate and legitimise their abusive behaviours. We analysed 556 stories of IPS posted on publicly accessible online forums dedicated to the discussion of sexual infidelity. We found that many users share narrative posts describing IPS as they boast about their actions, advise others on how to perform IPS without detection, and seek suggestions for next steps to take. We identify a set of common thematic story structures, justifications for abuse, and outcomes within the stories that provide a window into how these individuals believe their behaviour to be justified. Using these stories, we develop a four-stage framework that captures the change in a potential perpetrator's approach to IPS. We use our findings and framework to guide a discussion of efforts to combat abuse, including how we can identify crucial moments where interventions might be safely applied to prevent or deescalate IPS.

Fraud and its PREY: Conceptualising social engineering tactics and its impact on financial literacy outcomes

Financial literacy may not be as effective as previously thought in protecting against fraud victimisation. It does not inoculate investors from persuasion or social engineering tactics used by offenders to secure investment in fraudulent schemes. In fact, recent research indicates that overconfidence in investment knowledge may make individuals more susceptible to fraud. Using boiler room fraud as a case study, this article introduces the PREY (Profiled, Relational, Exploitable and Yielding) model to capture the psychological tactics used by fraud perpetrators to influence the thoughts and decision-making processes of individuals. The PREY model operationalizes the tenets of social engineering and demonstrates how such tactics could be re-engineered to increase the effectiveness of fraud prevention within the financial literacy context.

Modern web attacks

There are many factors that can dictate the success of a piece of malware. These include how and to whom it is delivered, how it is executed, how rapidly it propagates and how successfully it evades detection. The first two of these describe the process of threat delivery and execution, which are perhaps the most influential factors in the success of a threat. Traditionally cybercriminals have used email as their preferred vector of attack, employing various social engineering tactics in order to entice the recipient into executing the malicious attachment. As companies have become more aggressive in blocking email content, criminals have shifted their attentions, and are now firmly focused on the web.

Cyber terrorism: fact or fiction

Asian hackers are using Trojans to gather information from workers in the UK Government and the national infrastructure. The Trojans come as attachments or through links to specially crafted websites and use social engineering tactics that come from spoofed senders. Around 300 UK Government departments and critical infrastructure businesses have been attacked warns the UK National Infrastructure Security Coordination Centre (NISCC). The Security centre said that the attackers are gathering commercial or economically valuable information. The agency also warned that anti-virus software and firewalls do not give complete protection. It has released detection and protection advice against the Trojans. We have heard the term cyber terrorism used since the mid 1990s, when the concept of using computers and other types of high technology as weapons was first openly discussed in the form of Information Warfare. Since then we have seen the development of information warfare capability in at least two countries (the USA, China), but the events in the USA of 9/11/2001 and the subsequent response have elevated the fear of terrorism and, as a result, cyber terrorism to a new plane. There has been a strong and understandable reaction to the new terrorist threat and the concept of high tech attacks that they might launch seems to have become wrapped up in the whole atmosphere of fear.