Analysis

Malware Analysis for Defenders Malware analysis is a practical tool for security teams. It helps you understand how threats work, what they try to do, and how to stop them. By studying a sample, defenders learn what to monitor, what to block, and how to respond faster. Begin with safe handling. Isolate the sample in a lab or sandbox. Never run unknown software on production machines. Use clean snapshots and controlled networks to prevent spread. This reduces risk while you learn. ...

Threat Intelligence and Malware Analysis for Defenders Threat intelligence and malware analysis are two pillars of strong defense. Threat intel gives context—who is behind attacks, what tools they use, and when they act. Malware analysis reveals how a sample behaves, what it tries to do, and the clues it leaves behind. Used together, they help teams detect sooner, investigate faster, and stay ahead of threats. A simple, repeatable workflow helps. Start with data collection: ...

Threat Intelligence and Malware Analysis in Practice Threat intelligence and malware analysis are two sides of the same shield. Threat intel explains who is behind campaigns, what they seek, where they operate, and why it matters. Malware analysis shows how a program runs, what it tries to do on a device, and how it evades defenses. When teams combine both views, they move from reacting to predicting, and from isolated alerts to concrete containment decisions. ...

Big Data Fundamentals: Storage, Processing, and Analysis Big data means large and fast-changing data from many sources. The value comes when we store it safely, process it efficiently, and analyze it to gain practical insights. Three pillars guide this work: storage, processing, and analysis. Storage foundations Storage must scale with growing data and stay affordable. Many teams use distributed file systems like HDFS or cloud object storage such as S3. A data lake keeps raw data in open formats like Parquet or ORC, ready for later use. For fast, repeatable queries, data warehouses organize structured data with defined schemas and indexes. Good practice includes metadata management, data partitioning, and simple naming rules so you can find data quickly. ...

Malware Analysis for Beginners: Static and Dynamic Techniques Malware analysis helps us learn what a program does, how it hides its actions, and how to defend systems. For beginners, a simple goal is to spot behavior and key indicators without spreading the sample. Start in a safe lab, keep notes, and build up your skills step by step. Static analysis This looks at the file itself, without running it. Check what kind of program it is (EXE, DLL, or a script). Look at the headers, imports, and names inside the binary. Simple steps include using the file command, listing strings the program prints or loads, and examining the import table. Check if the file uses packing or encryption, and measure entropy to see if the body is hidden. Note suspicious sections, unusual API calls, or odd file names. If the sample is a PE or ELF file, you can compare imports and sections to clean versions of the same family. A careful read of strings and headers often reveals clues about its purpose or the attack chain. ...

Threat Intelligence and Malware Analysis: Staying Ahead of Adversaries Threat intelligence and malware analysis are two sides of the same shield. Threat intelligence gives context about who might attack and why, while malware analysis reveals how malicious software behaves. Together, they help security teams detect, understand, and respond faster. This approach works best when teams connect data from networks, endpoints, and trusted sources. Start with a simple workflow: collect signals, enrich them with known tactics, analyze behaviors, and share findings with the right people. Threat intelligence provides attacker profiles, maps activities to MITRE ATT&CK techniques, and highlights likely targets. Malware analysis looks at samples to see file tricks, persistence methods, communication patterns, and evasion steps. ...

Threat intelligence and malware analysis explained Threat intelligence and malware analysis work together to strengthen defenses. Threat intelligence collects knowledge about attacker groups, their goals, tools, and campaigns. Malware analysis studies individual samples to reveal how they operate, how they spread, and how they hide. When used together, they help security teams see both the big picture and the details of a single threat. Threat intelligence sources include open sources, commercial feeds, and the telemetry collected inside an organization. You can map data into these basic types: ...

Malware Analysis: Techniques for Detecting and Defending Malware analysis helps security teams understand how a threat operates and how it can be stopped. By studying its actions, defenders learn what to monitor, what to block, and how to recover quickly after an incident. There are two main paths: static analysis, which looks at the code and structure without running it, and dynamic analysis, which observes behavior in a safe environment. Each path adds pieces to the full picture of a threat. ...

Data Science and Statistics for Decision Making Data science and statistics help people make decisions when data is incomplete or uncertain. They turn numbers into usable insights and show what tends to work in real life. This helps leaders, analysts, and everyday consumers choose options with more confidence. A practical workflow A practical workflow starts with a clear decision and questions. Then collect relevant data, explore it with simple summaries, and test ideas in a careful, repeatable way. ...

Data Science and Statistics for Decision Making Data science and statistics help teams turn data into clear actions. By combining data, models, and human judgment, you can choose the best path among options in marketing, operations, or policy. The goal is not to win with fancy formulas, but to ask the right questions and tell a simple story with numbers. Framing the problem Start with a practical question and a goal you want to influence. For example: should we launch a feature this quarter? Define a clear objective and a minimal risk tolerance. Decide what success looks like, and what outcomes matter most. This framing guides data choices and the models you will use. ...