HLSC730 Week 7 Tenets of Defensive & Offensive Counterterrorism
Piecing Together Puzzles
Based on your readings thus far from the text and your own research explain
data collection techniques for investigating terrorism pre-and or post event.
What tools are available and should be used to enhance investigations? What
tools and or techniques are available to help in identifying and
understanding missing pieces of the puzzle.
Piecing Together Puzzles
One of the ways to counterterrorism attacks and related activities is through collecting
data and analyzing the same to learn the trends. The data collection techniques I would use
include data mining, which involves using technology to track down on suspicious activities that
are out of the ordinary and may threaten national security (Mo et al., 2017). Data mining focuses
on key sensitive areas, including financial transactions, communications, and movements. As
such, it would be critical to collaborate with other critical entities and agencies in the financial
sector, immigration department, and telecommunication industry to get the records of certain
individuals where needed and upon request. For this reason, some of the tools that would be used
in operation and data mining include technological tools, which will optimize more on the
current technology, which include the Internet of Things (IoT) and machine learning.
IoT and Machine Learning (ML) tools would effectively identify and understand the
missing pieces of puzzles. Through machine learning, data scientists can develop models that
send notifications and alerts in case certain suspicious activities happen in financial transactions
(Labib et al., 2020). On the other hand, IoT is highly effective because of its scope of coverage
through preinstalled security cameras, satellites, and other tools for tracking movements and
communications (Cheng & Zhao, 2018). Similarly, through the optimization of deep learning
and neural networks embedded in the telecommunication systems, the security personnel and
agencies would be able to be notified of the communications that mention certain keywords that
are of interest to terrorism and assist the government in moving fast to ensure any such plans are
pre-empted before the culprits enact them. Therefore, the success of the data mining approach as
a data collection technique for counterterrorism would only work through multi-agency
collaboration.
Recent studies are technology-focused; thus, they provide much information about the
potential of numerous IT tools that intelligence teams currently use to extract intelligence data.
For instance, Shodan is a sophisticated and multipurpose search engine that intelligence teams
can use to detect exposed network systems in the digital space worldwide (Sood & Enbody,
2014). A search query on Shodan can provide information about essential systems, most of
which are core parts of critical infrastructure. Some examples include SCADA systems,
navigation systems, nuclear points control centers, routers, control centers of power grids, and
others.
Maltego is another open-source intelligence (OSINT) tool available for gathering data
because of its ability to perform large-scale, aggressive data mining and analytics. Security
intelligence teams currently collect freely accessible data from public sources, such as the
Internet, which makes Maltego an incredible tool for gathering critical data (Sood & Enbody,
2014). The tool can swiftly query Internet infrastructure systems, web portals, OSNs, and online
groups to filter, mine, and analyze data to identify patterns, correlations, and interactions with
adversaries or targets.
Lastly, common tools that initially provided a wealth of information on targets,
individuals, and other elements of intelligence cannot be left behind. In fact, advanced tech-
based tools only enhance the data gathered from older versions of technologies. Some of these
tools are mobile phones, computers, CCTV cameras, servers, routers, and e-mails. These tools
provide unlimited access to cyber intelligence (CYBINT) and human intelligence (HUMINT),
which are the foundation of all defensive and offensive counterintelligence strategies.
Respectfully,
Al
References
Cheng, C., & Zhao, B. (2018, July). Intelligent counterterrorism equipment status and
development. In International Conference on Applications and Techniques in Cyber
Security and Intelligence (pp. 318-324). Springer, Cham.
Labib, N. M., Rizka, M. A., & Shokry, A. E. M. (2020). Survey of machine learning approaches
of anti-money laundering techniques to counterterrorism finance. In Internet of Things—
Applications and Future (pp. 73-87). Springer, Singapore.
Mo, H., Meng, X., Li, J., & Zhao, S. (2017, March). Terrorist event prediction based on
revealing data. In 2017 IEEE 2nd International Conference on Big Data Analysis
(ICBDA) (pp. 239-244). IEEE. http://dx.doi.org/10.1109/ICBDA.2017.807881
Sood, A., & Enbody, R. (2014). Targeted cyber attacks: Multi-staged attacks driven by exploits
and malware. Syngress Publishing.
