Asymmetric Warfare Technology
Tags: Law Enforcement, Analytics, Military, Education, Data Mining

Asymmetric Warfare Technology

Introduction

The principles of asymmetric warfare have not changed much since Sun Tzu scrolled the Art of War in 700 BC. Emerging technologies are the next evolution in asymmetric warfare. Emerging intelligence methods in the modern era have been completely revolutionized. The Internet has given everyone a level playing field. Well into the 21st-century, the clever computers are winning the asymmetric cyber warfare games. However, the dinosaur governments are slowly realizing the power of the Internet and how to harness the power for the light side. This brief essay will cover a few emerging technologies employed by the US government.

Data mining

The first emerging technology used by the US government in the Internet age is data mining and analytics. Data mining uses supercomputers to compile and sort extraordinary amounts of data. Before the data mining software was developed, intelligence analysts would take months to years to scour the vast amount of information searching for a needle in trenches worth of needles. Thanks to data mining and supercomputers, the needles can be discovered in hours to weeks depending on the complexity of the request for information (RFI). The National Security Agency (NSA) is chiefly responsible for emerging technologies and intelligence methods.

ILP

The NSA has a vast repertoire of emerging analytic techniques for aid the military, the intelligence community (IC), and law enforcement (LE) in intelligence-led policing (ILP). ILP is a proactive intelligence collection and analysis. ILP's primary function is forecasting extremist and or criminal events before the events occur, a daunting task. The NSA's job like the Central Intelligence Agency (CIA) on a much larger scale is to detect, search, and find national security threats to the US government, multi-national corporations (MNCs), and any other activities that could be evidenced malicious to the US interests. Thanks to the globalization of education, our adversaries may read from the same learning materials as ICers. Thus, the NSA needs to lead the way in emerging technical analytics for the US national economic interests to remain viable.

NSA analytics

The NSA uses a variety of ILP analytics. The first analytic method the NSA uses is by Clearview AI. Clearview AI illegally copies every social media face then aggregates this information into a database searchable against every known and or a suspected person of interest. Another analytic technique first employed by casinos to odd check is called the Hidden Markov Model (HMM). For the casinos, the HMM predicted every possible outcome. The NSA incorporates the same algo along with Clearview AI also to predict adverse to US empire interests outcomes in Southwest Asia and Northern Africa (Hossain, 2018, November 28). Finally, a team out of China uses a novel data analytics approach to forecast terrorism events called wavelet transform-based pattern recognition, high-level maths (Ze, Duoyong, Bo, Zhanfeng, & Aobo, 2018, January 28). This university provides constant entertainment in emerging analytics from the early 21st century, a dinosaur relic in data analytics.

Conclusion

The principles of asymmetric warfare remain nearly the same since Sun Tzu scrolled the Art of War in 700 BC. Emerging technologies are the next frontier in asymmetric warfare. Emerging intelligence methods in the modern era have revolutionized intelligence collection and analysis. The dark side Internet computers dove straightaway into the weeds to figure ways to exploit the new toys. As usual, the dinosaur governments are playing catch up. The dinosaur government created a variety of software tools to help intelligence analysts. This brief essay discussed a few emerging technologies employed by the US government.

References

Hossain, T. (2018, November 28). Forecasting violent events in the Middle East and North Africa using the Hidden Markov Model and regularized autoregressive models. Retrieved September 24, 2020, from https://journals.sagepub.com/doi/full/10.1177/1548512918814698

Ze, L., Duoyong, S., Bo, L., Zhanfeng, L., & Aobo, L. (2018, January 28). Terrorist group behavior prediction by wavelet transform-based pattern recognition. Retrieved September 24, 2020, from https://www.hindawi.com/journals/ddns/2018/5676712/