Cybersecurity in 2021-2022: Cybersecurity Advancements

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Table of Contents

Introduction
AI, Machine Learning, and Deep Learning
Behavioral Analytics
Blockchain and IoT Security
Embedded Hardware Authentication
Zero Trust Model
Conclusion
Works Cited

Introduction

The current digital revolution, commonly known as the fourth industrial revolution, has brought about new technological innovations that change the world in every aspect. COVID-19 saw organizations shift to working from home to cope with government restrictions to curb its effects. However, the ongoing digitization of society and the increasingly online nature of life have created opportunities for phishers, hackers, extortionists, and scammers. Hence, organizations are researching new technological innovations to address the growing online threat and protect company and customer data. This essay will investigate emerging cyber security advancements such as Blockchain, artificial intelligence (AI), machine learning and deep learning, behavioral analytics, IoT protection, embedded hardware authentication, and the zero-trust model.

AI, Machine Learning, and Deep Learning

Today, AI and deep learning are gaining traction in areas of cybersecurity. AI is now used in a similar way to how it is used in financial systems to detect fraud through the identification of unusual behavior patterns. As the scale and diversity of cyberattacks increase, artificial intelligence is assisting inadequately funded security operations specialists in staying ahead of the curve (Bonfanti). Unlike traditional software-driven methodologies, enterprises use AI and machine learning to automate risk detection and efficiently respond to threats.

According to Bonfanti, AI and machine learning in cybersecurity can be used both for offensive and defensive purposes. The characteristics that make AI and machine learning suitable for cybersecurity can also be applied to cyber offenses. Based on this view, we could see AI and machine learning adopted on a large scale for both offensive and defensive purposes in the near future. On the offensive side, we may see AI and AI tools utilized by cybercriminals to compromise targets’ security systems. Organizations are adopting AI-driven solutions to mitigate both AI and human-driven attacks.

Deep learning is one area of AI and machine learning gaining traction in recent years. Deep learning is a machine learning specialty in which machine algorithms learn independently, unsupervised, through their parameters to conclude, unlike machine learning inside intelligence, which is reliant on supervision for the machine to learn based on statistical models (Chen et al). These algorithms rely on neural networks and layers, which act as mini-brains (Bradley) Owing to the fast-evolving and increasingly innovative developments available to cyber-criminals, the need for deep learning in cybersecurity has become essential. Today, traditional cyber threat intelligence is not enough to handle these threats. Hence, organizations rely on the deep learning strength of behavioral biometrics to make it possible to learn from its dynamism and develop new classification criteria without human intervention.

Behavioral Analytics

Behavioral analytics leverages machine learning, artificial intelligence, big data, and analytics to identify dangerous behavior by studying normal, daily behavior variances. This strategy is widely used to target a specific population for social media and digital marketing, but it’s also being used to develop better cyber security technologies (Wells). Cyber threats are always present and can come from outside the organization or within. Without competent security or analytical solution, an organization may not be able to detect a breach at the appropriate time. These technologies monitor and identify malware and hackers, but they also track user activities. Emerging technologies such as AI combined with machine learning algorithms and statistical analysis work together to discover abnormalities, irregularities, and out-of-pattern behaviors.

Blockchain and IoT Security

Internet of Things (IoT) is a physical object embedded with sensors and other technologies to exchange data with other devices on a network. IoT devices are gaining momentum, however, they face security challenges. As such, Blockchain, as one of the newest technologies, is gaining popularity and interest in cyber security. A Blockchain is a peer-to-peer network that allows two transacting parties to verify their identities during a transaction. Once it was recognized as the underpinning of Bitcoin, Blockchain became linked with cryptocurrencies (Wylde). Other technologies, like Ethereum, have embraced it over time for various applications, including smart contracts, decentralized software, and decentralized banking, to name a few. Because of its decentralized, consensus-driven, and trust-free nature, Blockchain is inherently resistant to exploitation (Kamal et al.). Thus, to corrupt a ledger transaction, Blockchain systems that use proof of work validation techniques (bitcoins) need hackers to take control of most nodes, which is a costly procedure by design. This computational cost may be used for different security activities, obviating the necessity for a trusted central authority.

Blockchain’s decentralized nature is fundamental to IoT device security. For cybercriminals, the IoT has been and continues to be a key target. IoT devices’ growing popularity and minimal security features make them an attractive target for hackers, who need to purchase a botnet kit from the dark web to gain access. Concerns have been expressed about the Internet of Things’ ability to safeguard billions of linked devices due to vulnerabilities. To solve this issue, Blockchain promises to close the security vulnerabilities by lowering the chance of IoT devices being breached by a central authority and increasing the scalability of IoT deployments (Li et al.). In theory, it would allow IoT networks to be protected in various ways, including by creating a group agreement on suspicious network behavior and isolating any nodes operating unusually. Organizations like Touted, which is regarded as the first of its type, are already offering Blockchain-enabled IoT to stakeholders in charge of the consensus and enhancing system redundancy.

Embedded Hardware Authentication

Perhaps one of today’s numerous embedded design issues is guaranteeing that a device is legitimate without resorting to an unnecessarily complicated and costly system. Original equipment manufacturers (OEMs) face increasing challenges in safeguarding electronic systems from counterfeiting. With the shift to outsourced manufacturing for household electronics and computer accessories, protecting and preventing the unlawful creation of products bearing an OEM mark is becoming increasingly complex (Greenfield.). As such, embedded hardware authentication offers a solution to this problem.

Zero Trust Model

The model is based on the assumption that a network has already been compromised. According to this paradigm, a company cannot be trusted since security risks might come from inside and outside the company, necessitating increased security measures. When security risks are constantly growing in today’s digital transition, the framework is excellent for safeguarding organizations (Li et al.). This strategy is unique since it is based on contemporary business concerns such as working from home, ransomware attacks, hybrid-cloud environments, and other modern security threats.

Conclusion

The growing threat to organization network security is evolving at an unprecedented rate, necessitating the development of new methods to protect these systems. The area of AI has brought new techniques to keep networks safe from attacks. Coupled with behavioral analytics through deep learning, network security is evolving. In IoT, Blockchain is being utilized to protect connected devices through decentralization and encryption, adding to the evolving nature of system security. The growing threat of counterfeiting continues to threaten organizations. Hence, the utilization of embedded security adds a much-needed solution arising from third-party manufacturers. The ever-changing nature of network threats has resulted in a zero-tolerance model in which organizations assume the network is compromised because threats can be internal or external.

Works Cited

Bonfanti, Matteo E. “Artificial Intelligence and the Offence-Defence Balance in Cyber Security.” Cyber Security: Socio-Technological Uncertainty and Political Fragmentation. London: Routledge, 2022, Web.

Bradley, Tony. “Applying The Power Of Deep Learning To Cybersecurity.” 2021, Web.

Chen, Hua-Min, et al. “Deep Q-Learning for Intelligent Band Coordination in 5G Heterogeneous Network Supporting V2X Communication.”2022, Web.

Greenfield, David. “The Embedded Cybersecurity Trend | Automation World.”Automation World, 2018, Web.