"Advanced Techniques for Cybercrime Analysis: Identifying and Mitigating Emerging Threats"
American Military University
Background: Cybercrime is an issue that is quickly spreading and poses a serious threat to people, companies, and society at large (Casino et al., 2019). Due to the growing usage of technology and the internet, it is difficult for law enforcement and security professionals to keep up with cybercriminals' access to various tools and strategies. In my thesis proposal, I plan to look into the sophisticated strategies and tactics employed by cybercriminals in their criminal activity and the strategies and tactics utilized by law enforcement and security experts to recognize and counter these threats. The numerous forms of cybercrime, including advanced persistent threats, ransomware, phishing, banking trojans, and other sophisticated methods employed by cybercriminals, will be the focus of the research. Additionally, the research will list the current defenses employed by law enforcement and security experts and assess how well they work in identifying and reducing these dangers.
Purpose: This research aims to better understand cutting-edge cybercrime analysis methods and develop countermeasures (Sarker, 2022). To begin, we will undertake a thorough literature analysis to assess what is already known about sophisticated cybercrime methods and defences. Aside from laying the groundwork for the study's questions and goals, the literature evaluation will help reveal any holes in the existing research (Cascavilla et al., 2019). Recommendations for further study and practice, such as the need for additional in-depth examinations of certain approaches and the creation of new tactics for recognizing and reducing cybercrime risks, will be based on the results.
This thesis proposal hopes to add to the present knowledge of cutting-edge cybercrime methods and the steps law enforcement and security experts take to combat them. The findings will help businesses, government agencies, and others fight cybercrime more effectively.
· What are the current and emerging trends in cybercrime? (Nicholls,et al., 2021)
· What are the best methods for studying cybercrime?
· To what extent can organizations and law enforcement authorities successfully counteract new forms of cybercrime?
II. LITERATURE REVIEW
Overview: The term "cyber security" refers to safeguarding digital assets, like trade secrets and customer information, from unauthorized access and use. Cybercrime has been recognized by the United States government as a significant threat to the country's economy and national security, making it a critical management issue. Cybercrime can take several shapes, from direct attacks (such as hacking or DDoS) to indirect ones (such as the disclosure of private information or fraud) (Gyamfi & Jurcut, 2022). Businesses are stepping up their own cybersecurity measures in response to rising instances of cybercrime caused by recent developments. With most businesses now being transacted online, hackers have access to a wealth of valuable information about sales, consumers, markets, and new product development. Supply chains and mobile devices are embedded within the same networks for convenience and efficiency. However, this also makes them very susceptible to attack by hackers.
In addition, malicious actors are growing more sophisticated in their attacks on significant firms. This includes both professional cybercrime organizations and state-sponsored groups, and political hacktivists. Malicious actors are usually ahead of corporate cybersecurity teams in terms of technology and methodology since they can continually produce more complicated malware or advanced targeted attacks, while cybersecurity primarily relies on response, giving it the upper hand. The FBI estimates that in 2019, cybercrime would cost U.S. firms $3.5 billion, and they receive more than 1,300 reports of cybercrime every day (Gyamfi & Jurcut, 2022). Since many companies are reluctant to report ransomware attacks for fear of reprisal, it is estimated that the true annual cost is closer to $9 billion. The average cost of an attack on a small or medium-sized firm is $200,000, with as many as 60% of those enterprises closing their doors permanently due to the attack.
Human weaknesses, rather than technology flaws, are often the target of the most complex cyberattacks. Human behavior is predictable and easily manipulated, in contrast to technology flaws, which are simple to fix and remedy. In so-called "social engineering," criminals study a target's network and social interactions to launch personalized "phishing" campaigns. These are designed to trick workers into doing something irresponsible, like opening a link or downloading a file that introduces malware into the company network or spyware that can recognize login credentials for future exploitation. Applying AI and ML to the problem of spotting and stopping cyberattacks is a primary focus of study in this area. Examples include detecting and classifying network traffic in real-time using AI and ML approaches, which can aid in the early detection and mitigation of cyber assaults (Mliki et al., 2021). Feature selection and feature engineering were also recognized as critical to boosting these methods' effectiveness, which was a major takeaway from the research.
Another area of study is analysing data to spot and counteract cyber dangers. One study (Li, 2021) demonstrated that big data analytics could be utilized to spot telltale signs of a cyber-attack in the midst of regular network traffic. The research also indicated that analysts could benefit from data visualization approaches by gaining a deeper understanding of the data and making more educated decisions about responding to cyber threats.
Relevant Theories and Models: The usage of intrusion detection systems is a significant theory and paradigm in cybercrime (IDS) study. IDSs monitor network traffic for anomalies to identify and categorize cyber assaults. To detect attacks, machine learning algorithms are applied to the data collected from the network's packets. To identify both common and uncommon attacks, studies suggested an intrusion detection system (IDS) that employs a mix of supervised and unsupervised learning methods (Gyamfi & Jurcut, 2022). The IDS was validated on a sizable sample of network traffic, demonstrating a low false positive rate and great accuracy. This supports the idea that incorporating machine learning techniques into intrusion detection systems can be a valuable tool in the fight against cybercrime. The usage of honeypots is another popular approach and idea in cybercrime analysis. Decoy systems, known as honeypots, are used to lure and trap hackers. Cybersecurity assaults can be identified and countered in real-time with the help of such technologies. In order to identify and counteract cyberattacks, Sibi Chakkaravarthy et al. (2020) developed a honeypot-based system powered by ML algorithms. The system was found to be effective in detecting and responding to a wide range of cyber threats during testing in a simulated network environment. Honeypots and intrusion detection systems are just two examples of the ideas and models now in use in the field of cybercrime investigation. These hypotheses and models show the promise of employing cutting-edge strategies like machine learning and artificial intelligence to identify and counteract cyberattacks. It is worth noting that neither the theories nor the models are infallible and that cybercriminals are always developing novel evasion methods. That's why businesses must keep up with evolving cybersecurity threats and refine their own defences accordingly.
Gaps in the Literature: There has been substantial progress in creating cutting-edge methods for cybercrime analysis, however, there are still some knowledge gaps that require filling. More study is needed to determine how successful these methods are against various cyber threats. There is a need for more significant research on the efficiency of AI and ML techniques in detecting application-based assaults, for instance, even though many studies have demonstrated that these methods are effective in detecting network-based attacks (Gyamfi & Jurcut, 2022). More investigation is required into the scalability and resilience of these methods for big and complex systems.
Research Design: In the study's methodology, the research design is a mixed-methods investigation that combines qualitative and quantitative strategies. This approach allows for a more comprehensive understanding of the studied topic as it combines different perspectives and data sources.
Data Collection: Primary and secondary sources, including in-depth interviews with subject matter experts and surveys of cybercrime-affected businesses, will be used to compile the gathered information. The primary data collection method used in this study is in-depth interviews with 10 IT managers who have experience in dealing with cybercrime issues. These interviews were conducted to gather insights and perspectives from the IT managers on the advanced techniques used to identify and mitigate emerging cyber threats (Gyamfi & Jurcut, 2022). This data collection method allows for a deeper understanding of the subject matter and allows the researcher to explore and clarify issues in more detail. Secondary data collection methods were also used, such as surveys of businesses that have been affected by cybercrime. These surveys were used to gather information on the impact of cybercrime on businesses and the techniques they use to address these issues. This data collection method allows the researcher to gather a large amount of data from a broad sample of participants in a relatively short time.
Data Analysis: The data collected from both primary and secondary sources will then be analyzed using different techniques such as network analysis, statistical analysis, and content analysis (Gyamfi & Jurcut, 2022). These techniques will be used to examine and interpret the data to identify patterns, trends, and relationships.
Ethical Considerations: The study also considers ethical considerations, such as informed consent, confidentiality, and respect for participants. This means that the participants were fully informed about the study and voluntarily agreed to participate. The information collected from the participants will be kept confidential, and their identities will be protected. And the study will be conducted in a manner that respects the participants' rights and well-being.
IV. RESULTS AND DISCUSSION
Presentation of Findings: In the results and discussion section, one of the key findings is that the IT managers interviewed agreed that a lack of knowledge and training about cyber threats and cybersecurity increases cyber-attack cases. The quantitative results demonstrated that 80% of the participants, 8 IT managers, accepted that employees contribute much to cyber-attacks and should be prioritized. The other three (30%) were committed to more effort being directed toward technology improvement, including regular patching, AI and ML (Mliki et al., 2021). This finding aligns with the literature review, highlighting the importance of staying informed and educated about the latest cyber threats and trends to effectively prevent and respond to cyber-attacks.
Interpretation of Results: The IT administrators also admitted that weak technology and unprotected systems are factors in the prevalence of cyber-attacks. This result accords with the literature review's discussion of the difficulties in keeping up with the rapid development of technology and the necessity for businesses to upgrade and patch their systems to close security holes frequently. The study also indicated that IT managers understand the need to invest in cutting-edge methods like artificial intelligence and machine learning, data analytics, and network forensics to detect and counteract new forms of cybercrime (Mliki et al., 2021). This is in keeping with the findings of the literature evaluation, which highlighted the potential of such methods to enhance the efficiency and effectiveness of cybercrime analysis. The literature review has mentioned that human weaknesses rather than technology flaws are often the target of the most complex cyberattacks, which is consistent with the findings of the study that IT managers believe that human behavior is a major factor in cybercrime and that social engineering is one of the most common ways to infiltrate company's network.
Implications for Future Research and Practice: In the results and discussion section, one of the key findings is that the IT managers interviewed agreed that a lack of enough knowledge and training about cyber threats and cybersecurity increases the cases of cyber-attacks. This finding aligns with the literature review, highlighting the importance of staying informed and educated about the latest cyber threats and trends to effectively prevent and respond to cyber-attacks (Nicholls et al., 2021). The IT managers also acknowledged that technology and system vulnerability could contribute to the occurrence of cyber-attacks. This finding is consistent with the literature review, which discussed the challenges of keeping up with the constant evolution of technology and the need for organizations to regularly update and patch their systems to prevent vulnerabilities from being exploited by cybercriminals.
The study also found that IT managers know the importance of investing in advanced techniques such as AI and ML, data analytics, and network forensics for identifying and mitigating emerging cyber threats. This aligns with the literature review, which discussed the potential benefits of these techniques for improving the efficiency and effectiveness of cybercrime analysis.
Furthermore, the study found that IT managers believe that human behavior is a significant factor in cybercrime and that social engineering is one of the most common ways to infiltrate a company's network, this is also in line with the literature review that has mentioned that human weaknesses rather than technology flaws are often the target of the most complex cyberattacks.
Summary of Main Findings: The study's results show that IT managers blame both a lack of knowledge and training and technological and system vulnerabilities for the prevalence of cyber-attacks. The research also backs up the conclusions drawn from the literature review, which state that cutting-edge methods like AI and ML, data analytics, and network forensics can help spot and counteract new forms of cybercrime (Mliki et al., 2021). Moreover, IT directors recognize that user behavior plays a significant role in cybercrime and that social engineering is a common means by which hackers gain access to a business's computer system.
Recommendations for Future Research and Practice: Additional study of cybercrime analysis with AI and ML: IT managers think these tools could make cybercrime investigations more accurate and efficient. To fully understand the capabilities and limitations of these technologies and to create techniques for their practical application in identifying and mitigating emerging cyber risks, further study is required. Spend money on methods that focus on people: The research showed that social engineering is a typical tactic used by cybercriminals to break into a company's network because it takes advantage of people's natural tendencies to trust others and share information. Company leadership should allocate resources to training programs that inform workers of the risks posed by social engineering and how to avoid falling prey to it.
Cascavilla, G., Tamburri, D. A., & Van Den Heuvel, W. J. (2021). Cybercrime threat intelligence: A systematic multi-vocal literature review. Computers & Security, 105, 102258.
Casino, F., Politou, E., Alepis, E., & Patsakis, C. (2019). Immutability and decentralized storage: An analysis of emerging threats. IEEE Access, 8, 4737-4744.
Gyamfi, E., & Jurcut, A. (2022). Intrusion detection in internet of things systems: A review on Design Approaches Leveraging Multi-Access Edge Computing, machine learning, and datasets. Sensors, 22(10), 3744. https://doi.org/10.3390/s22103744
Li, S. (2021). Development trend of computer network security technology based on the Big Data Era. Journal of Physics: Conference Series, 1744(4), 042223. https://doi.org/10.1088/1742-6596/1744/4/042223
Mliki, H., Kaceam, A., & Chaari, L. (2021). A comprehensive survey on intrusion detection based machine learning for IOT Networks. ICST Transactions on Security and Safety, 8(29), 171246. https://doi.org/10.4108/eai.6-10-2021.171246
Nicholls, J., Kuppa, A., & Le-Khac, N. A. (2021). Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape. IEEE Access.
Sarker, M. G. R. (2022). An Interlinked Relationship between Cybercrime & Digital Media. IJFMR-International Journal For Multidisciplinary Research, 4(6).
Sibi Chakkaravarthy, S., Sangeetha, D., Cruz, M. V., Vaidehi, V., & Raman, B. (2020). Design of intrusion detection honeypot using social leopard algorithm to detect IOT ransomware attacks. IEEE Access, 8, 169944–169956. https://doi.org/10.1109/access.2020.3023764