Simplistic Approach to Detect Cybercrimes and Deter Cyber Criminals

Ahmed, Abu Shakil; Deb, Sudip; Habib, Al-Zadid Sultan Bin; Mollah, Md. Nurunnabi; Ahmad, Abu Saleh

doi:10.1109/ic4me2.2018.8465618

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…However, in the cybersecurity domain, the dynamic nature of cybercrimes makes it difficult and challenging to detect and predict the threat probabilities and the cascading impact of cybercrimes in an evolving organization landscape (Morgan, 2020). Further, cybercrime happens through advanced communication devices utilizing Internet connections, which is challenging to detect the crime and identify the offenders (Ahmed et al, 2018). According to the 2021 Data Breach Investigation Report, ransomware played a significantly increased role in Malware associated breaches of about 61.2% concerning previous years (Verizon, 2021).…”

Section: Impart Cybercrime On Organizationsmentioning

confidence: 99%

“…Their work focused on subjects either part of victimization by cybercrimes (Sattar et al, 2018). Ahmed et al (2018) proposed a framework for automatic and manual techniques to detect cybercrime and charge the offender with proof (Ahmed et al, 2018). Furthermore, Bissell et al (2022) explored what ultimately sets cybersecurity leaders apart in aiding and combating cybercrimes.…”

Section: Related Workmentioning

confidence: 99%

“…Their work focused on subjects either part of victimization by cybercrimes (Sattar et al ., 2018). Ahmed et al . (2018) proposed a framework for automatic and manual techniques to detect cybercrime and charge the offender with proof (Ahmed et al ., 2018).…”

Section: Related Workmentioning

confidence: 99%

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Mitigating cybercrimes in an evolving organizational landscape

Yeboah-Ofori

Opoku-Boateng

2023

CRR

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PurposeVarious organizational landscapes have evolved to improve their business processes, increase production speed and reduce the cost of distribution and have integrated their Internet with small and medium scale enterprises (SMEs) and third-party vendors to improve business growth and increase global market share, including changing organizational requirements and business process collaborations. Benefits include a reduction in the cost of production, online services, online payments, product distribution channels and delivery in a supply chain environment. However, the integration has led to an exponential increase in cybercrimes, with adversaries using various attack methods to penetrate and exploit the organizational network. Thus, identifying the attack vectors in the event of cyberattacks is very important in mitigating cybercrimes effectively and has become inevitable. However, the invincibility nature of cybercrimes makes it challenging to detect and predict the threat probabilities and the cascading impact in an evolving organization landscape leading to malware, ransomware, data theft and denial of service attacks, among others. The paper explores the cybercrime threat landscape, considers the impact of the attacks and identifies mitigating circumstances to improve security controls in an evolving organizational landscape.Design/methodology/approachThe approach follows two main cybercrime framework design principles that focus on existing attack detection phases and proposes a cybercrime mitigation framework (CCMF) that uses detect, assess, analyze, evaluate and respond phases and subphases to reduce the attack surface. The methods and implementation processes were derived by identifying an organizational goal, attack vectors, threat landscape, identification of attacks and models and validation of framework standards to improve security. The novelty contribution of this paper is threefold: first, the authors explore the existing threat landscapes, various cybercrimes, models and the methods that adversaries are deploying on organizations. Second, the authors propose a threat model required for mitigating the risk factors. Finally, the authors recommend control mechanisms in line with security standards to improve security.FindingsThe results show that cybercrimes can be mitigated using a CCMF to detect, assess, analyze, evaluate and respond to cybercrimes to improve security in an evolving organizational threat landscape.Research limitations/implicationsThe paper does not consider the organizational size between large organizations and SMEs. The challenges facing the evolving organizational threat landscape include vulnerabilities brought about by the integrations of various network nodes. Factor influencing these vulnerabilities includes inadequate threat intelligence gathering, a lack of third-party auditing and inadequate control mechanisms leading to various manipulations, exploitations, exfiltration and obfuscations.Practical implicationsAttack methods are applied to a case study for the implementation to evaluate the model based on the design principles. Inadequate cyber threat intelligence (CTI) gathering, inadequate attack modeling and security misconfigurations are some of the key factors leading to practical implications in mitigating cybercrimes.Social implicationsThere are no social implications; however, cybercrimes have severe consequences for organizations and third-party vendors that integrate their network systems, leading to legal and reputational damage.Originality/valueThe paper’s originality considers mitigating cybercrimes in an evolving organization landscape that requires strategic, tactical and operational management imperative using the proposed framework phases, including detect, assess, analyze, evaluate and respond phases and subphases to reduce the attack surface, which is currently inadequate.

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Section: Impart Cybercrime On Organizationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Mitigating cybercrimes in an evolving organizational landscape

Yeboah-Ofori

Opoku-Boateng

2023

CRR

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“…Then, a visual similarity-based technique was used to identify the legitimacy of URL via finding the most similar scores either it is legitimate or suspicious URLs. Another researchers used biometric techniques to defend cyber crimes such as Ahmed et al in [103] proposed an approach to be applied in Bangladesh to detect cybercrimes over the Internet. The new framework requires each Internet's user to register a national ID and password to gain access to the Internet, and foreigners can gain access using their visa's number.…”

Section: G Cybercrime Detection Using Other Techniquesmentioning

confidence: 99%

Comprehensive Review of Cybercrime Detection Techniques

et al. 2020

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Cybercrimes describe cases of indictable offences and misdemeanours in which computer or any communication tools are involved as targets, commission instruments, incidental to, or that cases are associated with the prevalence of computer technology. Common forms of cybercrimes could be child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing and other cyber hacking. These kinds of cybercrimes are mostly leading to breaching users' privacy, security violation, business loss, financial fraud, or damage in public as well as government properties. Hence, this paper intensively reviews cybercrime detection and prevention methods. It first explores the different types of cybercrimes then discusses their threats against privacy and security in computer systems. It also describes the strategies that cybercriminals might utilize in committing these crimes against individuals, organizations, and societies. The paper then reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyses the vulnerabilities of each technique. As a future study, the paper provides recommendations for the development of cybercrime detection model in which it is capable to effectively detect cybercrime in comparison to the existing techniques.

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“…Although technologies and the Internet make life easier, they have been used for harmful purposes. Cybersecurity crimes impact society [2] since these crimes occur through modern communication devices using internet connections. The actors who cause a cybercrime are called attackers, and they are different kinds and have multiple goals; one of those kinds is APT Attacks.…”

Section: Introductionmentioning

confidence: 99%

Advanced Persistent Threat Attack Detection using Clustering Algorithms

Alsanad¹,

Altuwaijri²

2022

IJACSA

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Advanced Persistent Threat (APT) attack has become one of the most complex attacks. It targets sensitive information. Many cybersecurity systems have been developed to detect the APT attack from network data traffic and request. However, they still need to be improved to identify this attack effectively due to its complexity and slow move. It gets access to the organizations either from an active directory or by gaining remote access, or even by targeting the Domain Name Server (DNS). Nowadays, many machine learning (ML) techniques have been implemented to detect APT attack by using the tools in the market. However, still, there are some limitations in terms of accuracy, efficiency, and effectiveness, especially the lack of labeled data to train ML methods. This paper proposes a framework to detect APT attacks using the most applicable clustering algorithms, such as the APRIORI, K-means, and Hunt's algorithm. To evaluate and compare the performance of the proposed framework, several experiments are conducted on a public dataset. The experimental results showed that the Support Vector Machine with Radial Basis Function (SVM-RBF) achieves the highest accuracy rate, reaching about 99.2%. This accurate result confirms the effectiveness of the developed framework for detecting attacks from network data traffic.

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Simplistic Approach to Detect Cybercrimes and Deter Cyber Criminals

Cited by 8 publications

References 8 publications

Mitigating cybercrimes in an evolving organizational landscape

Mitigating cybercrimes in an evolving organizational landscape

Comprehensive Review of Cybercrime Detection Techniques

Advanced Persistent Threat Attack Detection using Clustering Algorithms

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