scholarly journals A New Proposal on the Advanced Persistent Threat: A Survey

2020 ◽  
Vol 10 (11) ◽  
pp. 3874
Author(s):  
Santiago Quintero-Bonilla ◽  
Angel Martín del Rey
Keyword(s):  
Machine Learning ◽  
Life Cycle ◽  
Early Detection ◽  
Machine Learning Techniques ◽  
Cyber Attack ◽  
New Approach ◽  
Advanced Persistent Threat ◽  
Learning Techniques ◽  
Proposed Model ◽  
Mitigation Techniques

An advanced persistent threat (APT) can be defined as a targeted and very sophisticated cyber attack. IT administrators need tools that allow for the early detection of these attacks. Several approaches have been proposed to provide solutions to this problem based on the attack life cycle. Recently, machine learning techniques have been implemented in these approaches to improve the problem of detection. This paper aims to propose a new approach to APT detection, using machine learning techniques, and is based on the life cycle of an APT attack. The proposed model is organised into two passive stages and three active stages to adapt the mitigation techniques based on machine learning.

scholarly journals Parallel processing using big data and machine learning techniques for intrusion detection

2020 ◽  
Vol 9 (3) ◽  
pp. 553
Author(s):  
Alaeddine Boukhalfa ◽  
Nabil Hmina ◽  
Habiba Chaoni
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Processing Time ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
New Approach ◽  
Learning Techniques ◽  
Proposed Model ◽  
Multiple Devices

Currently, information technology is used in all the life domains, multiple devices produce data and transfer them across the network, these transfers are not always secured, they can contain new menaces invisible by the current security devices. Moreover, the large amount and variety of the exchanged data cause difficulties related to the detection time. To solve these issues, we suggest in this paper, a new approach based on storing the large amount and variety of network traffic data employing Big Data techniques, and analyzing these data with Machine Learning algorithms, in a distributed and parallel way, in order to detect new hidden intrusions with less processing time. According to the results of the experiments, the detection accuracy of the Machine Learning methods reaches 99.9 %, and their processing time has been reduced considerably by applying them in a parallel and distributed way, which proves that our proposed model is effective for the detection of new intrusions.

Early Detection of Bipolar Disorder Four Years Before Onset in a 22-Year Population-Based Birth Cohort Using Advanced Machine Learning Techniques

2020 ◽  
Author(s):  
Francisco Diego Rabelo-da-Ponte ◽  
Jacson Gabriel Feiten ◽  
Benson Mwangi ◽  
Fernando C. Barros ◽  
Fernando C. Wehrmeister ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Early Detection ◽  
Birth Cohort ◽  
Population Based ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals An Advance Resource Reservation Approach in a Cloud Database Environment

2017 ◽  
Author(s):  
Vinicius Da S. Segalin ◽  
Carina F. Dorneles ◽  
Mario A. R. Dantas
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Resource Reservation ◽  
Machine Learning Techniques ◽  
Advance Reservation ◽  
Running Time ◽  
Knowing That ◽  
Learning Techniques ◽  
Proposed Model ◽  
Cloud Database

AA well-known challenge with long running time queries in database environments is how much time a query will take to execute. This prediction is relevant for several reasons. For instance, by knowing that a query will take longer to execute than desired, one resource reservation mechanism can be performed, which means reserving more resources in order to execute this query in a shorter time in a future request. In this research work, it is presented a proposal in which the use of an advance reservation mechanism in a cloud database environment, considering machine learning techniques, provides resource recommendation. The proposed model is presented, in addition to some experiments that evaluate benefits and the efficiency of this enhanced proposal.

Leveraging Machine Learning in Financial Fraud Forensics in the Age of Cybersecurity

2022 ◽  
pp. 220-249
Author(s):  
Md Ariful Haque ◽  
Sachin Shetty
Keyword(s):  
Machine Learning ◽  
Financial Institutions ◽  
Machine Learning Techniques ◽  
Cyber Attack ◽  
Financial Industry ◽  
Security Breaches ◽  
Financial Gain ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Machine Learning Models

Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source e-commerce transaction dataset to illustrate the forensic processes by creating a machine learning model to classify fraudulent transactions. Overall, the chapter focuses on how the machine learning models can help detect and prevent fraudulent activities in the financial sector in the age of cybersecurity.

scholarly journals A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift

2019 ◽  
Vol 18 (02) ◽  
pp. 1950010 ◽  
Author(s):  
Niddal Imam ◽  
Biju Issac ◽  
Seibu Mary Jacob
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Research Community ◽  
Machine Learning Techniques ◽  
Spam Detection ◽  
Learning Approach ◽  
New Approach ◽  
Detection Systems ◽  
Learning Techniques ◽  
Over Time

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become very attractive to spammers. Unlike other types of spam, Twitter spam has become a serious issue in the last few years. The large number of users and the high amount of information being shared on Twitter play an important role in accelerating the spread of spam. In order to protect the users, Twitter and the research community have been developing different spam detection systems by applying different machine-learning techniques. However, a recent study showed that the current machine learning-based detection systems are not able to detect spam accurately because spam tweet characteristics vary over time. This issue is called “Twitter Spam Drift”. In this paper, a semi-supervised learning approach (SSLA) has been proposed to tackle this. The new approach uses the unlabeled data to learn the structure of the domain. Different experiments were performed on English and Arabic datasets to test and evaluate the proposed approach and the results show that the proposed SSLA can reduce the effect of Twitter spam drift and outperform the existing techniques.

scholarly journals Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

2021 ◽  
Vol 8 ◽  
Author(s):  
Daniele Roberto Giacobbe ◽  
Alessio Signori ◽  
Filippo Del Puente ◽  
Sara Mora ◽  
Luca Carmisciano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Prediction Models ◽  
Early Recognition ◽  
Model Performance ◽  
Daily Practice ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Long Run ◽  
Learning Techniques

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

scholarly journals A Threat-Hunting of UNSW-NB15 with Machine Learning Techniques to Achieve Resilience

2019 ◽  
Vol 9 (1S3) ◽  
pp. 488-494
Keyword(s):  
Machine Learning ◽  
Fault Tolerance ◽  
Critical Role ◽  
Cyber Attacks ◽  
Three Dimensions ◽  
Machine Learning Techniques ◽  
Advanced Persistent Threat ◽  
Learning Techniques

In order to focus on the mission and functions of the business of the organizations, cyber resiliency have to play a critical role against the adversaries’ target. The strategy recommended by NIST to reduce the suspect ability of cyber-attacks of the system with the three dimensions such as harden the target, limit the damage to the target and make the target resilient. The threats could be based on cyber and noncyber. The objective is to provide cyber resiliency on the Advanced Persistent Threat (APT), has born with the nature of sophisticated, stealthy, persistent towards target and highly adoptable to the environment. The challenge is to provide cyber resilience to the system from compromising tactics of the adversaries, uncertain in eradication of threat due to its persistent nature, recognizing its adapting ability. The cyber resiliency also links with other disciplines like safety, fault tolerance, privacy, resilience and survivability, reliability and security

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