5,178 research outputs found
Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques
Cybercrime is a growing threat to organizations and individuals worldwide,
with criminals using increasingly sophisticated techniques to breach security
systems and steal sensitive data. In recent years, machine learning, deep
learning, and transfer learning techniques have emerged as promising tools for
predicting cybercrime and preventing it before it occurs. This paper aims to
provide a comprehensive survey of the latest advancements in cybercrime
prediction using above mentioned techniques, highlighting the latest research
related to each approach. For this purpose, we reviewed more than 150 research
articles and discussed around 50 most recent and relevant research articles. We
start the review by discussing some common methods used by cyber criminals and
then focus on the latest machine learning techniques and deep learning
techniques, such as recurrent and convolutional neural networks, which were
effective in detecting anomalous behavior and identifying potential threats. We
also discuss transfer learning, which allows models trained on one dataset to
be adapted for use on another dataset, and then focus on active and
reinforcement Learning as part of early-stage algorithmic research in
cybercrime prediction. Finally, we discuss critical innovations, research gaps,
and future research opportunities in Cybercrime prediction. Overall, this paper
presents a holistic view of cutting-edge developments in cybercrime prediction,
shedding light on the strengths and limitations of each method and equipping
researchers and practitioners with essential insights, publicly available
datasets, and resources necessary to develop efficient cybercrime prediction
systems.Comment: 27 Pages, 6 Figures, 4 Table
Secure Software Development: Issues and Challenges
In recent years, technology has advanced considerably with the introduction
of many systems including advanced robotics, big data analytics, cloud
computing, machine learning and many more. The opportunities to exploit the yet
to come security that comes with these systems are going toe to toe with new
releases of security protocols to combat this exploitation to provide a secure
system. The digitization of our lives proves to solve our human problems as
well as improve quality of life but because it is digitalized, information and
technology could be misused for other malicious gains. Hackers aim to steal the
data of innocent people to use it for other causes such as identity fraud,
scams and many more. This issue can be corrected during the software
development life cycle, integrating security across the development phases, and
testing of the software is done early to reduce the number of vulnerabilities
that might or might not heavily impact an organisation depending on the range
of the attack. The goal of a secured system software is to prevent such
exploitations from ever happening by conducting a system life cycle where
through planning and testing is done to maximise security while maintaining
functionality of the system. In this paper, we are going to discuss the recent
trends in security for system development as well as our predictions and
suggestions to improve the current security practices in this industry.Comment: 20 Pages, 4 Figure
Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence
The evolution of cybersecurity has spurred the emergence of autonomous threat
hunting as a pivotal paradigm in the realm of AI-driven threat intelligence.
This review navigates through the intricate landscape of autonomous threat
hunting, exploring its significance and pivotal role in fortifying cyber
defense mechanisms. Delving into the amalgamation of artificial intelligence
(AI) and traditional threat intelligence methodologies, this paper delineates
the necessity and evolution of autonomous approaches in combating contemporary
cyber threats. Through a comprehensive exploration of foundational AI-driven
threat intelligence, the review accentuates the transformative influence of AI
and machine learning on conventional threat intelligence practices. It
elucidates the conceptual framework underpinning autonomous threat hunting,
spotlighting its components, and the seamless integration of AI algorithms
within threat hunting processes.. Insightful discussions on challenges
encompassing scalability, interpretability, and ethical considerations in
AI-driven models enrich the discourse. Moreover, through illuminating case
studies and evaluations, this paper showcases real-world implementations,
underscoring success stories and lessons learned by organizations adopting
AI-driven threat intelligence. In conclusion, this review consolidates key
insights, emphasizing the substantial implications of autonomous threat hunting
for the future of cybersecurity. It underscores the significance of continual
research and collaborative efforts in harnessing the potential of AI-driven
approaches to fortify cyber defenses against evolving threats
AI Approaches to Predictive Justice: A Critical Assessment
This paper addresses the domain of predictive justice, exploring the intersection of artificial intelligence (AI) and judicial decision-making. We will first introduce the concept of predictive justice, referring to the ongoing debate surrounding the potential automation of judicial decisions through AI systems. Then, we will examine the current landscape of AI approaches employed in predictive justice applications, providing a comprehensive
overview of methodologies and technological advancements. Then, we delve into the phenomenology of predictive justice, highlighting the diverse spectrum of legal predictions achievable with contemporary AI systems. We also assess the extent to which these predictive AI systems are presently integrated into real-world judicial practices. Finally, the paper critically addresses recurrent fears and critiques associated with predictive justice. We
sort these critiques into unreasonable objections, reasonable concerns with possible technical solutions, and reasonable concerns demanding further investigation. Navigating the complexities of these critiques, we offer some insights for future research and practical implementation. The nuanced approach taken in this study contributes to the ongoing discourse on predictive justice, emphasising the need for a balanced evaluation of its potential benefits and legal challenge
Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets
This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and associated algorithms in future Capital Markets. New AI algorithms are constantly emerging, with each 'strain' mimicking a new form of human learning, reasoning, knowledge, and decisionmaking. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they now are more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Capital Markets, so it is important to understand their computational strengths and weaknesses. Since ML algorithms effectively self-program and evolve dynamically, financial institutions and regulators are becoming increasingly concerned with ensuring there remains a modicum of human control, focusing on Algorithmic Interpretability/Explainability, Robustness and Legality. For example, the concern is that, in the future, an ecology of trading algorithms across different institutions may 'conspire' and become unintentionally fraudulent (cf. LIBOR) or subject to subversion through compromised datasets (e.g. Microsoft Tay). New and unique forms of systemic risks can emerge, potentially coming from excessive algorithmic complexity. The contribution of this paper is to review AI, ML and associated algorithms, their computational strengths and weaknesses, and discuss their future impact on the Capital Markets
Spatiotemporal Analysis of Web News Archives for Crime Prediction
In today's world, security is the most prominent aspect which has been given higher priority. Despite the rapid growth and usage of digital devices, lucrative measurement of crimes in under-developing countries is still challenging. In this work, unstructural crime data (900 records) from the news archives of the previous eight years were extracted to predict the behavior of criminals' networks and transform it into useful information using natural language processing (NLP). To estimate the next move of criminals in Pakistan, we performed hotspot-based spatial analysis. Later, this information is fed to two different classifiers for possible identification and prediction. We achieved the maximum accuracy of 92% using K-Nearest Neighbor (KNN) and 62% using the Random Forest algorithm. In terms of crimes, the results showed that the most prevalent crime events are robberies. Thus, the usage of digital information archives, spatial analysis, and machine learning techniques can open new ways of handling a peaceful and sustainable society in eradicating crimes for countries having paucity of financial resources
A framework for securing email entrances and mitigating phishing impersonation attacks
Emails are used every day for communication, and many countries and
organisations mostly use email for official communications. It is highly valued
and recognised for confidential conversations and transactions in day-to-day
business. The Often use of this channel and the quality of information it
carries attracted cyber attackers to it. There are many existing techniques to
mitigate attacks on email, however, the systems are more focused on email
content and behaviour and not securing entrances to email boxes, composition,
and settings. This work intends to protect users' email composition and
settings to prevent attackers from using an account when it gets hacked or
hijacked and stop them from setting forwarding on the victim's email account to
a different account which automatically stops the user from receiving emails. A
secure code is applied to the composition send button to curtail insider
impersonation attack. Also, to secure open applications on public and private
devices
A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions
The recent O-RAN specifications promote the evolution of RAN architecture by
function disaggregation, adoption of open interfaces, and instantiation of a
hierarchical closed-loop control architecture managed by RAN Intelligent
Controllers (RICs) entities. This paves the road to novel data-driven network
management approaches based on programmable logic. Aided by Artificial
Intelligence (AI) and Machine Learning (ML), novel solutions targeting
traditionally unsolved RAN management issues can be devised. Nevertheless, the
adoption of such smart and autonomous systems is limited by the current
inability of human operators to understand the decision process of such AI/ML
solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims
at solving this issue, enabling human users to better understand and
effectively manage the emerging generation of artificially intelligent schemes,
reducing the human-to-machine barrier. In this survey, we provide a summary of
the XAI methods and metrics before studying their deployment over the O-RAN
Alliance RAN architecture along with its main building blocks. We then present
various use-cases and discuss the automation of XAI pipelines for O-RAN as well
as the underlying security aspects. We also review some projects/standards that
tackle this area. Finally, we identify different challenges and research
directions that may arise from the heavy adoption of AI/ML decision entities in
this context, focusing on how XAI can help to interpret, understand, and
improve trust in O-RAN operational networks.Comment: 33 pages, 13 figure
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