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  • Writer's pictureKyser Clark

Trends in Penetration Testing


Penetration Testing Trends

In cybersecurity, penetration testing, often called "ethical hacking," is a crucial practice to discover and address vulnerabilities in computer systems, applications, and networks before malicious hackers can exploit them. As technological landscapes continuously evolve, so too does the art and science of penetration testing. Central to this dynamic evolution are three transformative trends: the surge of artificial intelligence and machine learning, the emergence of the Internet of Things, and the rise of cloud computing. These booming developments, while opening new horizons of possibilities, bring forth complex challenges that mandate a new understanding and approach to the industry. The profound interplay of artificial intelligence (AI) and machine learning (ML) within this framework offers a glimpse into the complexities and opportunities these trends herald for the future of penetration testing.


Artificial Intelligence and Machine Learning

The artificial intelligence (AI) surge in modern industries is undeniable, with cybersecurity and penetration testing being no exceptions. The complexities of modern systems, alongside their continuous evolution, pose challenges to traditional, primarily manual, penetration testing methods. As a response, AI, especially its subset machine learning (ML), offers transformative solutions. They automate and streamline laborious tasks, significantly enhancing vulnerability identification and assessment. Specific algorithms, like the Markov Decision Process (MDP) and genetic algorithms, are tailored to refine attack planning. Similarly, the Planning Domain Definition Language (PDDL) facilitates advanced vulnerability assessments, offering a more systematic and structured approach to penetration testing (McKinnel et al., 2019).


The evolution of penetration testing has witnessed a shift from manual to automated practices due to the expanding complexity of modern networks. Reinforcement learning (RL), a facet of ML, has emerged as a vital tool to augment the efficiency and precision of these tests. Viewing penetration testing tasks as a partially observed Markov decision process (POMDP) allows for optimization via various algorithms. The Intelligent Automated Penetration Testing Framework (IAPTF) highlights this shift, employing model-based RL for decision-making. This approach notably excels in larger networks, outperforming existing methodologies and manual human testing. Moreover, with the ability to dissect extensive networks into security clusters, it addresses the scalability concerns that arise when integrating RL into penetration testing tools (Ghanem et al., 2022). At the same time, AI and ML are poised to streamline and enhance penetration testing—IoT devices' sheer volume and variety present new challenges, demanding tailored approaches.


The Internet of Things

The emergence of the Internet of Things (IoT) has brought about substantial transformations in the technological domain, spreading across nearly every aspect of daily living and business operations. The anticipated growth of IoT devices, projected to rise from 35.82 billion in 2021 to 75.44 billion by 2025, underscores the rapid expansion and the associated security implications (Yaacoub et al., 2023). Various cybersecurity challenges have emerged alongside the convenience of smart devices in sectors like healthcare and industrial automation. Due to their vast diversity and often hasty configurations, IoT devices are particularly susceptible to a range of vulnerabilities, including weak or hardcoded passwords, insecure ecosystem interfaces, and outdated security protocols. These vulnerabilities and inadequate security measures tailored for resource-limited IoT devices pave the way for potential cyberattacks that can jeopardize confidentiality, integrity, availability (CIA), and authentication (Yaacoub et al., 2023).


In response to these evolving threats, penetration testers have developed innovative strategies tailored to the IoT landscape. One such methodology is the PETIoT, a dedicated Kill Chain for IoT Vulnerability Assessment and Penetration Testing (VAPT). This approach emphasizes a structured process, encompassing everything from preliminary information gathering to remediation recommendations (Bella et al., 2023). Traditional penetration testing, primarily dealing with standalone systems or software, is now evolving. Modern IoT penetration testing addresses individual devices and critically examines the larger ecosystem they operate within. This encompasses cloud interactions, associated software applications, wireless communications, and underlying hardware—each a potential entry point for cyberattacks. As we increasingly rely on IoT devices, the importance of penetration testing to ensure their security becomes essential. Drawing from the profound implications of IoT's diverse interactions, it is imperative to delve into one of the most influential aspects of this interconnected landscape: cloud computing. Cloud computing has significantly impacted penetration testing methodologies and introduced new challenges for penetration testers.


Cloud Computing

The rise of cloud computing, especially Mobile Cloud Computing (MCC), has transformed application operations by integrating mobile and cloud platforms. MCC offers optimized service offloading, augmented security, and efficient complexity management. However, these benefits come with unique intricacies stemming from the convergence of both domains, demanding robust penetration testing approaches (Al-Ahmad et al., 2019).


With the exponential growth of virtualization-based technologies in cloud computing, there is a critical necessity for heightened security in these virtualized environments. One of the significant concerns arises from vulnerabilities in virtual machines (VMs). These vulnerabilities pose potential threats to the system and can serve as vectors for broader network attacks. Traditional methods of VM vulnerability scanning can be cumbersome, as they often require user-maintained scanners. To address this issue, recent research has proposed an automated vulnerability assessment and patching framework, which identifies and promptly patches critical vulnerabilities in VMs. This framework highlights the importance of early vulnerability scanning for new VMs, prioritizing addressing severe vulnerabilities first and emphasizing the necessity for comprehensive risk assessments in cloud infrastructures (Patil & Modi, 2018). The ever-evolving nature of cloud computing and its intricacies demands specialized offensive security strategies tailored to the specific cloud environment. This transformation reveals challenges in penetration testing within Continuous Integration/Continuous Deployment pipelines unique to cloud environments, emphasizing the need for specialized cloud training and concerns for legal boundaries.


Important Emerging Issues

Cloud computing has introduced new challenges, particularly in penetration testing. Continuous Integration/Continuous Deployment (CI/CD) represents a crucial area of focus, given its transformative impact on software development processes within the cloud. Simultaneously, there is a notable lack of cloud penetration testing training, leading to potential vulnerabilities as organizations move their operations to the cloud. Furthermore, legal boundaries have emerged as a significant concern, given the intricacies of cloud environments and the inherent legal implications of penetration testing. Organizations must grasp these challenges' implications to remain resilient in this rapidly changing landscape. To understand the gravity of these issues, it is beneficial to begin by examining the complexities and nuances introduced by CI/CD in the cloud.


Continuous Integration/Continuous Deployment

In today's dynamic technology ecosystem, Continuous Integration and Continuous Deployment (CI/CD) are pivotal elements for automating software development processes. They enable frequent and swift software releases, bridging the communication gaps between development and operations teams (Lee & Liu, 2023). However, with the accelerating pace of software development and cloud computing, traditional security measures need help. The ever-changing nature of cloud environments, characterized by constant provisioning and decommissioning of resources, makes the challenge even more pronounced. Penetration testing, traditionally reliable, offers only a snapshot in time of the continuously evolving landscape of combined software and cloud environments.


The evident solution to address the rapid changes in software and cloud environments is to advocate for more frequent penetration tests. However, the challenge lies in executing these tests in a manner that is less disruptive to operations and cost-effective. The transformative nature of cloud computing, with its virtualized resources and on-demand scalability, opens avenues for flexible testing environments that might not disrupt main operational workflows. There is an increasing need to innovate in penetration testing methodologies. Incorporating security into all stages of development, whether on-premises or in the cloud, emphasizes the need to prioritize regular testing (Lee & Liu, 2023). As the technology community continues to devise platforms and strategies for automating security testing and optimizing costs, the focal point for organizations is clear: How can we intensify the frequency of penetration tests to safeguard against vulnerabilities while minimizing disruptions and remaining cost-effective?


Lack of Cloud Penetration Testing Training

In cybersecurity, penetration testing has consistently evolved to meet the challenges posed by emerging technologies. However, a noticeable discrepancy exists regarding cloud platforms' vast and intricate landscape. Many existing penetration testing methodologies and courses predominantly concentrate on on-premises infrastructures. This focus has inadvertently created a knowledge gap where professionals might be adept at identifying vulnerabilities in traditional systems but remain relatively unversed in cloud-specific threats (Zhao et al., 2021). The intricacies of cloud platforms, including multi-tenancy, shared resources, and API-centric designs, demand a tailored approach to penetration testing, one that many current training programs do not sufficiently address.


There is a pressing need for training providers to recalibrate their curricula. Introducing hands-on cloud penetration testing training and certifications should be a priority, not an afterthought. Such programs must transcend mere theory, immersing participants in real-world cloud attack scenarios. By doing so, they will equip professionals with the requisite skills to decipher and rectify vulnerabilities unique to cloud architectures (Zhao et al., 2021). Furthermore, training modules should also incorporate the rapidly changing regulatory landscape, ensuring that professionals are not just technically adept but also compliant with industry standards and best practices. By taking these steps, training institutions can play a decisive role in bolstering cloud security, benefiting businesses that rely on these platforms for their daily operations.


Legal Boundaries

Organizations increasingly lean towards public cloud providers and shared cloud environments, leading to a complex landscape where the ownership of resources becomes more ambiguous. Within this ecosystem, the very act of penetration testing faces numerous legal challenges. Even if an organization greenlights a Red Team operation, which is more extensive and unpredictable than conventional penetration tests, there are still potential legal repercussions (DeMarco, 2018). These exercises delve into a corporation's vulnerabilities, often employing methods used by criminal hackers without the knowledge of most company employees. The nuances introduced by multi-tenancy, ambiguous ownership of resources, and the shared responsibility model greatly influence the permissions and prohibitions regarding these tests. Notably, access to sensitive information during such operations can inadvertently expose data governed by various legal frameworks, triggering potential legal actions and mandates for breach notifications (DeMarco, 2018). Therefore, even if an organization attempts to protect its assets, it must proceed cautiously to avoid breaching standards or compromising its reputation.


Obtaining the necessary permissions and legal authorization for penetration tests in public or shared cloud environments further compounds these challenges. As cloud infrastructures are characterized by their rapid elasticity and changeability, there is a legitimate concern that the environment may have evolved when authorization has finally been granted. Maintaining clear documentation, ensuring legal oversight, and frequently updating the scope of work is crucial to aligning with the evolving environment. Moreover, Red Team operations necessitate a careful balance between rigorous testing and ensuring no additional risk is posed to systems or users (DeMarco, 2018). The interplay of legal constraints, dynamic cloud environments, and the overarching objective of cyber-resilience requires organizations to be more vigilant and adaptive than ever in their approach to cloud penetration testing.


Conclusion

The integration of AI, the widespread use of IoT devices, and an increasing reliance on cloud computing marks the rapid evolution of the technological landscape. AI and ML introduce efficiency and redefine traditional penetration testing methods. With the emergence of IoT devices, there are vast opportunities. However, they come with multifaceted challenges, necessitating robust frameworks to address device vulnerabilities and the intricate ecosystems in which they operate. While heralding flexibility, cloud computing also unveils a shifting virtual environment and distinct legal challenges specific to its penetration testing.


Legal complexities surrounding cloud penetration testing emphasize the imperative of strategic planning. What was once a straightforward task in penetration testing has expanded into a domain teeming with legal and ethical dilemmas, especially in shared or public cloud spaces. There is a noticeable absence of specialized cloud testing training, highlighting a considerable skills gap and necessitating a rethinking of current educational approaches. As organizations delve deeper into penetration testing, balancing security measures and adherence to evolving legal and ethical standards becomes critical. While this paper reveals pivotal trends, the dynamic nature of this field dictates continuous research and agility to stay ahead of looming vulnerabilities and threats across cyberspace.



References


Al-Ahmad, A. S., Kahtan, H., Hujainah, F., & Jalab, H. A. (2019). Systematic literature review on penetration testing for mobile cloud computing applications. IEEE Access, 7, 173524-173540. https://doi.org/10.1109/access.2019.2956770


Bella, G., Biondi, P., Bognanni, S., & Esposito, S. (2023). PETIoT: PEnetration testing the Internet of things. Internet of Things, 22, 100707. https://doi.org/10.1016/j.iot.2023.100707


DeMarco, J. V. (2018). An approach to minimizing legal and reputational risk in red team hacking exercises. Computer Law & Security Review, 34(4), 908-911. https://doi.org/10.1016/j.clsr.2018.05.033


Ghanem, M. C., Chen, T., & Nepomuceno, E. (2022). Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks. https://doi.org/10.21203/rs.3.rs-1686285/v1


Lee, W., & Liu, Z. (2023). Microservices-based DevSecOps Platform using Pipeline and Open Source Software. Journal of Information Science & Engineering, 39(5), 1117-11128. https://doi.org/10.6688/JISE.202309_39(5).0007


McKinnel, D. R., Dargahi, T., Dehghantanha, A., & Choo, K. R. (2019). A systematic literature review and meta-analysis on artificial intelligence in penetration testing and vulnerability assessment. Computers & Electrical Engineering, 75, 175-188. https://doi.org/10.1016/j.compeleceng.2019.02.022


Patil, R., & Modi, C. (2018). Designing an efficient framework for vulnerability assessment and patching (VAP) in virtual environment of cloud computing. The Journal of Supercomputing, 75(5), 2862-2889. https://doi.org/10.1007/s11227-018-2698-6


Yaacoub, J. A., Noura, H. N., Salman, O., & Chehab, A. (2023). Ethical hacking for IoT: Security issues, challenges, solutions and recommendations. Internet of Things and Cyber-Physical Systems, 3, 280-308. https://doi.org/10.1016/j.iotcps.2023.04.002


Zhao, T., Gasiba, T., Lechner, U., & Pinto-Albuquerque, M. (2021). Raising awareness about cloud security in industry through a board game. Information, 12(11), 482. https://doi.org/10.3390/info12110482

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