Ids-1-.xls ⚡ Limited

Since the file "ids-1-.xls" typically refers to a dataset for Intrusion Detection Systems (IDS)

—often a subset or specific version of industry-standard benchmarks like

—this report focuses on the deep technical analysis of such a dataset.

Below is a draft of a "deep report" designed for security analysts and data scientists to summarize findings from a network traffic intrusion analysis. Intrusion Detection Analysis Report: IDS-1 Data Evaluation Document ID: SEC-RPT-2026-001 Dataset Reference: ids-1-.xls Classification: Internal Use Only 1. Executive Summary This report details the deep analysis of the ids-1-.xls ids-1-.xls

dataset, which contains captured network traffic features used to identify potential security breaches. The analysis focuses on distinguishing traffic from various

vectors (e.g., DoS, DDoS, PortScan, and Brute Force). Our findings indicate a high prevalence of automated scanning activities and specific anomalies in packet length distributions that correlate with malicious intent. Stamus Networks 2. Data Profile & Feature Engineering

The dataset consists of multiple columns representing network flow metrics. Total Records: Approximately 200,000+ entries. Key Features Analyzed: Source/Destination IP & Port : Identifying internal vs. external traffic. Flow Duration : Measuring the lifespan of a connection. Packet Length (Min/Max/Mean) Since the file "ids-1-

: Differentiating between small "ping" packets and large data exfiltration flows. TCP/UDP Flags : Identifying SYN floods or unauthorized port scans. Preprocessing:

Normalization was applied to scale feature values between 0 and 1, ensuring that high-magnitude features (like byte counts) do not disproportionately bias the detection model. ScienceDirect.com 3. Deep Findings & Statistical Analysis

Using Deep Synthesis and Machine Learning methods, the following anomalies were identified: A. Traffic Distribution Traffic Type Percentage Primary Indicators Steady inter-arrival times, standard packet sizes. High volume of flows from single sources; short duration. Sequential destination port attempts within milliseconds. Infiltration Unusual destination IPs and high outbound packet counts. B. Model Performance We utilized a Deep Synthesis Insider Intrusion Detection (DS-IID) framework to classify threats. False Positive Rate: 1.2% (Vital for reducing "alert fatigue" in IT teams). High-Risk Signature: Open the file in Excel 2016 or later

Attacks such as "Heartbleed" or "Infiltration" were most accurately identified via Random Forest models, which outperformed traditional rule-based systems. ScienceDirect.com 4. Threat Landscape Observations

Method 1: Excel's Built-in Converter

• Open the file in Excel 2016 or later.
• Go to File > Save As.
• Choose:
  • Excel Workbook (*.xlsx) – for standard data (macros removed).
  • Excel Binary Workbook (*.xlsb) – for large datasets (faster, smaller).
  • CSV UTF-8 (*.csv) – for database imports.

1. Network Security Logs (IDS Reports)

Security analysts often export logs from Snort, Suricata, or Cisco IDS (Intrusion Detection Systems) to Excel for offline analysis. An exported report might be automatically named ids-1-.xls, followed by ids-2-.xls, etc.

How to Open ids-1-.xls (Step-by-Step)

Because it's an older .xls file, modern versions of Excel (2016, 2019, 2021, 365) can still open it, but with some security restrictions.

5. Exploratory data analysis (EDA) plan

• Summary stats: row count, unique counts per identifier, null percentages.
• Time series: events per hour/day, moving averages, seasonality.
• Top sources/destinations: by event count or severity.
• Severity distribution: pie/bar charts of event severity.
• Event types over time: stacked area or heatmap.
• Correlation matrix for numeric fields (counts, bytes, duration).
• Text summary: most frequent words/phrases in message field (after stopword removal).

KPIs to compute:

• Events per minute/hour/day
• Top N IPs/users by event count
• Mean/median response time or duration (if present)
• False positive rate (if labels available)
• Trend slopes (increase/decrease) using linear regression on counts

Step 4: Hex Inspection (Advanced)

• Download HxD (free hex editor).
• Open ids-1-.xls in HxD.
• Check the first 8 bytes. A valid old Excel file starts with D0 CF 11 E0 A1 B1 1A E1. If you see PK (50 4B), the file is actually a ZIP-based .xlsx – just rename it.