Anomaly detection

The cybersecurity landscape necessitates continual adaptation and exploration of novel defensive strategies to counter the evolving threats posed by malicious actors. Machine learning (ML) has emerged as a powerful tool for bolstering cybersecurity, offering innovative approaches to anomaly detection, intrusion prevention, and threat identification. This article delves into the potential of ML in cybersecurity, examining its various applications and exploring its strengths and limitations while highlighting its…

Recent AI advancements in API technology involve enhancing natural language processing capabilities, improving algorithmic decision-making through reinforcement learning, and expanding AI integration across diverse sectors like healthcare, finance, and e-commerce to create more intelligent, adaptable, and tailored API solutions. Key Trends and Advancements AutoML for APIs AutoML (Automated Machine Learning) tools are increasingly being used to automate the development of machine learning models that can be exposed through…

from sklearn.ensemble import IsolationForest import pandas as pd # Sample data (replace with your actual data) data = { 'feed_type': , 'volume': } # Create a DataFrame df = pd.DataFrame(data) # Extract feature names feature_names = df.columns.tolist() # Train the model model = IsolationForest(contamination=0.01) # Adjust contamination based on your dataset model.fit(df]) # Function to detect anomalies def detect_anomalies(feed_type, volume): anomaly_prediction = model.predict(]) return…

Neuromorphic chips represent a significant leap in computing, borrowing inspiration from the neural architecture of the human brain. Unlike traditional computer designs that employ von Neumann-based processors, neuromorphic chips mimic the neural network topology of the brain, allowing more efficient and adaptive computing systems. Mimicking the Brain’s Neural Structure Artificial neurons, synapses, and pathways are used in these chips to mimic the complicated connections seen in the human brain. This architecture…

The Industrial Internet of Things (IIoT) has revolutionized the industrial landscape, providing organizations with unprecedented access to real-time data from connected devices and machines. This wealth of data holds the key to improving operational efficiency, reducing downtime, and ensuring the longevity of industrial assets. One of the most transformative applications of IIoT is predictive maintenance and anomaly detection, made possible by the integration of Machine Learning (ML) and Artificial Intelligence (AI)…

This is an article from DZone's 2023 Enterprise Security Trend Report.For more: Read the Report In recent years, developments in artificial intelligence (AI) and automation technology have drastically reshaped application security. On one hand, the progress in AI and automation has strengthened security mechanisms, reduced reaction times, and reinforced system resilience. On the other hand, the challenges in AI and automation have created exploitable biases, overreliance on automation, and expanded attack surfaces…

OpenTelemetry (OTel) is an open-source standard used in the collection, instrumentation, and export of telemetry data from distributed systems. As a framework widely adopted by SRE teams and security teams, OTel is more than just one nice-to-have tool among many; it is critical. In this post, we’ll explore the role that OTel plays in system security. We’ll look at how telemetry data is used to secure systems along with how OTel securely handles telemetry data. Then, we’ll consider concrete practices — basic and advanced —…

Artificial Intelligence: Definition and Practical Applications Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence. It involves the creation of algorithms and models that enable machines to perceive and interpret information, reason, make decisions, learn from data, and interact with their environment. AI encompasses various techniques, including machine learning, natural language processing, computer vision, and robotics. The uses of…

When Celina John finalized her college project “Spam Classification through Machine Learning Algorithms,” we didn’t expect the scope of the work to be so huge since the domain has been constantly evolving and we had to firm up the scope and key deliverables. As technology grows, spam is growing exponentially in all electronic communication channels, be it email, short message service, or social media. No matter how unethical and illegal, the number of spammers is increasing day by day, and sending unsolicited, unwanted,…

What Is Distributed Tracing? The rise of microservices has enabled users to create distributed applications that consist of modular services rather than a single functional unit. This modularity makes testing and deployment easier while preventing a single point of failure with the application. While applications begin to scale and distribute their resources amongst multiple cloud-native services, tracing a single transaction becomes tedious and nearly impossible. Hence, developers need to apply distributed tracing…