An Investigation And Insight Into Terrorism In Nigeria
- URL: http://arxiv.org/abs/2109.11023v2
- Date: Wed, 5 Jan 2022 16:23:09 GMT
- Title: An Investigation And Insight Into Terrorism In Nigeria
- Authors: Aamo Iorliam, Raymond U. Dugeri, Beatrice O. Akumba, Samera Otor, and
Yahaya I. Shehu
- Abstract summary: This paper studies the terrorist activities in Nigeria from 1970 to 2019.
Insights are made on the occurrences of terrorist attacks, the localities of the target, and the successful and unsuccessful rates of such attacks.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Terrorism is one of the most life-challenging threats facing humanity
worldwide. The activities of terrorist organizations threaten peace, disrupts
progress, and halt the development of any nation. Terrorist activities in
Nigeria in the last decades have negatively affected economic growth and have
drastically reduced the possibilities of foreign investments in Nigeria. In
this paper, statistical and inferential insights are applied to the terrorist
activities in Nigeria from 1970 to 2019. Using the Global Terrorism Database
(GTD), insights are made on the occurrences of terrorist attacks, the
localities of the target, and the successful and unsuccessful rates of such
attacks. The Apriori algorithm is also used in this paper to draw hidden
patterns from the GTD to aid in generating strong rules through database
mining, resulting in relevant insights. This understanding of terrorist
activities will provide security agencies with the needed information to be one
step ahead of terrorists in making the right decisions targeted at curbing
terrorism in Nigeria.
Related papers
- Defining and Evaluating Physical Safety for Large Language Models [62.4971588282174]
Large Language Models (LLMs) are increasingly used to control robotic systems such as drones.
Their risks of causing physical threats and harm in real-world applications remain unexplored.
We classify the physical safety risks of drones into four categories: (1) human-targeted threats, (2) object-targeted threats, (3) infrastructure attacks, and (4) regulatory violations.
arXiv Detail & Related papers (2024-11-04T17:41:25Z) - Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks [39.54354926067617]
Graph neural networks are a type of deep learning model that can utilize the interactive relationships within graph structures.
fraudulent activities only account for a very small part of transaction transfers.
fraudsters often disguise their behavior, which can have a negative impact on the final prediction results.
arXiv Detail & Related papers (2024-09-15T23:08:31Z) - Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attacks [47.9744734181236]
"steal now, later" attacks can be employed to exploit potential vulnerabilities in the AI service.
The average cost of each attack is less than $ 1 dollars, posing a significant threat to AI security.
arXiv Detail & Related papers (2024-04-24T13:51:56Z) - Towards more Practical Threat Models in Artificial Intelligence Security [66.67624011455423]
Recent works have identified a gap between research and practice in artificial intelligence security.
We revisit the threat models of the six most studied attacks in AI security research and match them to AI usage in practice.
arXiv Detail & Related papers (2023-11-16T16:09:44Z) - Privacy in Large Language Models: Attacks, Defenses and Future Directions [84.73301039987128]
We analyze the current privacy attacks targeting large language models (LLMs) and categorize them according to the adversary's assumed capabilities.
We present a detailed overview of prominent defense strategies that have been developed to counter these privacy attacks.
arXiv Detail & Related papers (2023-10-16T13:23:54Z) - High Accuracy Location Information Extraction from Social Network Texts
Using Natural Language Processing [0.0]
This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction.
We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions.
The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves.
arXiv Detail & Related papers (2023-08-31T10:21:24Z) - Prediction of terrorism pattern accompanied by cyber-terrorism and the
development direction of corresponding legal systems [0.0]
As terrorist groups' access to cyber-attack assets improves, the traditional form of terrorism is also expected to change to a form combined with cyber-terrorism.
From a national security point of view, Korea lacks a legal system to prepare for and respond to cyber terrorism.
arXiv Detail & Related papers (2022-03-05T00:21:15Z) - Fighting Money Laundering with Statistics and Machine Learning [95.42181254494287]
There is little scientific literature on statistical and machine learning methods for anti-money laundering.
We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging.
arXiv Detail & Related papers (2022-01-11T21:31:18Z) - Framework for Managing Cybercrime Risks in Nigerian Universities [0.0]
The study is based on literature review and propose how an actionable framework that Nigerian Universities can adopt to setoff cybersecurity programs can be developed.
We conclude that the framework provides a lucrative starting point for Nigerian universities to setoff efficient and effective cyber security program.
arXiv Detail & Related papers (2021-08-22T15:24:32Z) - Learning future terrorist targets through temporal meta-graphs [8.813290741555994]
We propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets.
We derive 2-day-based time series that measure the centrality of each feature within each dimension over time.
Formulating the problem in the context of the strategic behavior of terrorist actors, these multivariate temporal sequences are then utilized to learn what target types are at the highest risk of being chosen.
arXiv Detail & Related papers (2021-04-21T08:09:57Z) - A Bayesian decision support system for counteracting activities of
terrorist groups [0.0]
Terrorist groups present a serious threat to the security and well-being of the general public.
Such observable behaviour and communications data can be utilised by the authorities to estimate the threat posed by a terrorist group.
Here we develop a Bayesian integrating decision support system that can bring together information relating to each of the members of a terrorist group as well as the combined activities of the group.
arXiv Detail & Related papers (2020-07-08T20:23:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.