Chris Wen's Blog

Physical LayerThe physical layer is the lowest layer in the OSI reference model. It is responsible for the transmission of raw bits over a physical medium, defining the electrical, optical, or wireless signaling that carries the data. Its functions include bit-level transmission, modulation, synchronization, and dealing with noise and interference. 1. Basic Concepts of the Physical Layer Defines the hardware means of sending and receiving data (cables, switches, radios, etc.). Transmits dat...

DHCP Protocol & DHCP SpoofingObjectiveThis experiment aims to observe abnormal behavior in the DHCP protocol by building a simpleLAN topology using Mininet and launching a DHCP spoofing attack with DHCPig [1]. Environment Component Name Version Ubuntu 18.04.1 Mininet 2.3.1b4 DHCPig - isc-dhcp-server[2] - Python3 3.6.9 Network Topology Illustration: The host 1 will act as the DHCP server; The host 2 will act as the client; The host 3 will act as the attacker. Experimen...

DHCP Protocol & DHCP SpoofingDHCP Protocol OverviewThe Dynamic Host Configuration Protocol (DHCP) is a network management protocol used onIP networks to automatically assign IP addresses and other configuration parameters to devicesusing a client-server model [1]. Layer: DHCP operates at the Application layer in the TCP/IP model and uses UDP as itstransport protocol [2].Ports: The server uses UDP port 67, and the client uses UDP port 68 [2]. Primary Function Automatic IP Configuration: ...

Malware ReviewThis article introduces two major malware families, Zeus and Fareit. Zeus is a banking trojanthat steals online financial credentials through techniques such as keylogging and formgrabbing. Fareit is a credential-stealing trojan that collects passwords, usernames, and walletdata, often delivering other malware as well. The study outlines their impacts on target systemsand highlights key traffic patterns, providing insight into how these threats can be identifiedthrough network a...

Naive BayesIntroduction to Naive BayesNaive Bayes is a probability-based machine learning algorithm. It is based on Bayes’ Theorem and assumes that all features are independent of each other. This independence assumption is why it is called “naive”. Although this assumption is often not fully true in real-world data, Naive Bayes still performs very well in many practical tasks. Common applications include: Text classification Spam email detection Sentiment analysis Document classification Ne...

PerceptronWhat Is a Perceptron?A Perceptron is a simple binary classification model. It receives multiple input signals and produces one output signal. The output of a perceptron can only be one of two values: 0 1 A perceptron with two input signals can be represented as: x_1, x_2 \rightarrow yWhere: (x_1, x_2) are input signals. (w_1, w_2) are weights. (y) is the output signal. The circle, or node, is called a neuron. Each input signal is multiplied by its corresponding weight before be...

Logistic RegressionLogistic Regression is a binary classification technique that assumes numeric input variables with a Gaussian distribution. Although this assumption is not mandatory, the algorithm performs well even if the data does not adhere to this pattern. Logistic regression calculates coefficients for each input variable, combining them linearly into a regression function and applying a logistic transformation. While it is simple and fast, its effectiveness depends on the characteris...

Ways to Measure DistanceThere are several ways to measure distance between two points in a multidimensional space. The choice of distance metric depends on the specific problem and the characteristics of the data. Euclidean DistanceThe Euclidean distance is the straight-line distance between two points in a multidimensional space. It is calculated as the square root of the sum of the squared differences between the corresponding coordinates of the two points. 1d(A, B) = sqrt((x1 - y1)^2 + (x2...

Logistic RegressionLinear RegressionLinear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables. It is used to predict the value of the dependent variable based on the values of the independent variables. Working PrincipleThe linear regression model is based on the assumption that there is a linear relationship between the dependent variable and the independent variables. The model is trained on a dataset of input-...

KNN AlgorithmsK-Nearest Neighbors (KNN) is a simple, non-parametric, and lazy learning algorithm used for classification and regression tasks. Working PrincipleThe KNN algorithm works by finding the K nearest neighbors to a given data point and making predictions based on the majority class (for classification) or the average value (for regression) of those neighbors.Normally, when we do prediction, there are two main methods: classification and regression. KNN can be used for both of these m...