436,273 research outputs found
MACHINE LEARNING ALGORITHMS FOR DETECTION OF CYBER THREATS USING LOGISTIC REGRESSION
The threat of cyberattacks is expanding globally; thus, businesses are developing intelligent artificial intelligence systems that can analyze security and other infrastructure logs from their systems department and quickly and automatically identify cyberattacks. Security analytics based on machine learning the next big thing in cybersecurity is machine data, which aims to mine security data to show the high maintenance costs of static relationship rules and methods. But, choosing the appropriate machine learning technique for log analytics using ML continues to be a significant barrier to AI success in cyber security due to the possibility of a substantial number of false-positive detections in large-scale or global Security Operations Centre (SOC) settings, selecting the proper machine learning technique for security log analytics remains a substantial obstacle to AI success in cyber security. A machine learning technique for a cyber threat exposure system that can minimize false positives is required. Today\u27s machine learning methods for identifying threats frequently use logistic regression. Logistic regression is the first of three machine learning subcategories—supervised, unsupervised, and reinforcement learning. Any machine learning enthusiast will encounter this supervised machine learning algorithm at the beginning of their machine learning career. It\u27s an essential and often applied classification algorithm
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their predictions. The vulnerability of machine learning to such wild
patterns (also referred to as adversarial examples), along with the design of
suitable countermeasures, have been investigated in the research field of
adversarial machine learning. In this work, we provide a thorough overview of
the evolution of this research area over the last ten years and beyond,
starting from pioneering, earlier work on the security of non-deep learning
algorithms up to more recent work aimed to understand the security properties
of deep learning algorithms, in the context of computer vision and
cybersecurity tasks. We report interesting connections between these
apparently-different lines of work, highlighting common misconceptions related
to the security evaluation of machine-learning algorithms. We review the main
threat models and attacks defined to this end, and discuss the main limitations
of current work, along with the corresponding future challenges towards the
design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201
Exploiting Machine Learning to Subvert Your Spam Filter
Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—even if the adversary’s access is limited to only 1 % of the training messages. We further demonstrate a new class of focused attacks that successfully prevent victims from receiving specific email messages. Finally, we introduce two new types of defenses against these attacks.
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ACCOUNTING AND FINANCIAL STATEMENTS AUTO ANALYSIS SYSTEM
This project was motivated by the need to revolutionize the generation of financial statements and financial analysis process thus speeding up business decision making. The research questions were: 1) How can machine learning increase the speed of financial statement preparation and automate financial statements analysis? 2) How can businesses balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias? 3) Can the Java J2EE framework provide a reliable running environment for machine learning?
The findings were: 1) Machine learning can significantly increase the accuracy and speed of financial analysis. Using machine learning algorithms, financial data can be processed and analyzed in real-time, allowing for quicker and more precise financial analysis. Machine learning models can identify patterns and trends in financial data that may not be easily detectable by humans, leading to more accurate financial statements and analysis. Additionally, machine learning can automate repetitive tasks in the financial analysis process, saving time and resources for businesses. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, it also requires handling sensitive financial data. Therefore, it is crucial for businesses to implement robust data security measures to protect against potential data breaches and ensure compliance with privacy regulations. Additionally, businesses need to be mindful of potential biases in machine learning algorithms, as biased algorithms can result in biased financial analysis. Regular audits and monitoring of machine learning models should be conducted to address and mitigate any potential biases. 3) The Java J2EE framework can provide a reliable running environment for machine learning. Java J2EE (Java 2 Platform, Enterprise Edition) is a widely used and mature framework for developing enterprise applications, including machine learning applications. It offers scalability, reliability, and security features that are essential for running machine learning algorithms in a production environment. Java J2EE provides robust support for distributed computing, allowing for efficient processing of large financial datasets. Furthermore, it offers a wide range of libraries and tools for implementing machine learning algorithms, making it a viable choice for running machine learning applications in the financial industry.
The conclusions were: 1) Machine learning has the potential to significantly increase the accuracy and speed of financial analysis, thereby revolutionizing the generation of financial statements and the financial analysis process. Various machine learning algorithms, such as decision trees, random forests, and deep learning algorithms, can be utilized to identify patterns, trends, and hidden risks in financial data, leading to more informed and efficient business decision making. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, there are ethical considerations that need to be addressed, such as ensuring data privacy, implementing effective data security measures, and mitigating biases in machine learning algorithms used in financial analysis. Businesses should adopt a responsible approach to machine learning implementation, considering the potential risks and benefits. 3) The Java J2EE framework can provide a reliable running environment for machine learning applications, but further research is needed to evaluate the performance and scalability of machine learning models in this framework. Identifying potential optimizations for running machine learning applications at scale in the Java J2EE framework can lead to more efficient and effective implementation of machine learning in financial analysis and decision-making processes. Further research in this area can contribute to the development of robust and scalable machine learning applications for financial analysis in the business domain.
Areas for further study include: 1) Exploring different machine learning algorithms and techniques to further improve the accuracy and speed of financial analysis. 2) Conducting research on the impact of machine learning on financial decision making and business performance. 3) Investigating methods for addressing and mitigating biases in machine learning algorithms used in financial analysis. 4) Evaluating the effectiveness of different data security measures in protecting sensitive financial data in machine learning applications. 5) Studying the performance and scalability of machine learning models in the Java J2EE framework and identifying potential optimizations for running machine learning applications at scale
Security Evaluation of Support Vector Machines in Adversarial Environments
Support Vector Machines (SVMs) are among the most popular classification
techniques adopted in security applications like malware detection, intrusion
detection, and spam filtering. However, if SVMs are to be incorporated in
real-world security systems, they must be able to cope with attack patterns
that can either mislead the learning algorithm (poisoning), evade detection
(evasion), or gain information about their internal parameters (privacy
breaches). The main contributions of this chapter are twofold. First, we
introduce a formal general framework for the empirical evaluation of the
security of machine-learning systems. Second, according to our framework, we
demonstrate the feasibility of evasion, poisoning and privacy attacks against
SVMs in real-world security problems. For each attack technique, we evaluate
its impact and discuss whether (and how) it can be countered through an
adversary-aware design of SVMs. Our experiments are easily reproducible thanks
to open-source code that we have made available, together with all the employed
datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector
Machine Applications
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