The document summarizes various techniques for automated software testing using fuzzing, including coverage-based fuzzing (AFL), directed greybox fuzzing (AflGO), and neural network-based approaches (FuzzGuard). It discusses how genetic algorithms and simulated annealing are used in AFL and AflGO respectively to guide test case mutation towards new code areas. It also provides examples of vulnerabilities found using these fuzzing tools.
「C言語のポインタ(型の変数)は、可変長配列を扱うために使う」という点に絞って、50分間程度の解説をしています。
最終的に下記の12行のプログラムを47分間使って解説します。
(7行目、11行目の”<”は除いています)
1: int size = N;
2: int x[size];
3: int *p;
4:
5: p = x;
6:
7: for ( int = 0; i size; i++)
8: p[i] = i;
9:
10: int y = 0
11: for ( int i = 0; i size; i++)
12: y = y + p[i];
https://www.youtube.com/watch?v=KLFlk1dohKQ&t=1496s
1. The model is a polynomial regression model that fits a polynomial function to the training data.
2. The loss function used is the sum of squares of the differences between the predicted and actual target values.
3. The optimizer used is GradientDescentOptimizer which minimizes the loss function to fit the model parameters.
This paper proposes enhancing trusted domain enforcement through a VMM interruption mechanism. Current systems lack fine-grained input validation and dynamic access control to resources. The proposed system detects illegal inputs and moves processes to an untrusted domain for sandboxing. When an invalid input is detected, the guest OS notifies the VMM through virtualized interrupts. The VMM then isolates the compromised domain by disabling its network and block devices. The system was implemented through Linux kernel patches and a security module to validate inputs and enforce domain transitions. Performance testing showed the module approach had lower overhead than modifying the kernel directly.
「C言語のポインタ(型の変数)は、可変長配列を扱うために使う」という点に絞って、50分間程度の解説をしています。
最終的に下記の12行のプログラムを47分間使って解説します。
(7行目、11行目の”<”は除いています)
1: int size = N;
2: int x[size];
3: int *p;
4:
5: p = x;
6:
7: for ( int = 0; i size; i++)
8: p[i] = i;
9:
10: int y = 0
11: for ( int i = 0; i size; i++)
12: y = y + p[i];
https://www.youtube.com/watch?v=KLFlk1dohKQ&t=1496s
1. The model is a polynomial regression model that fits a polynomial function to the training data.
2. The loss function used is the sum of squares of the differences between the predicted and actual target values.
3. The optimizer used is GradientDescentOptimizer which minimizes the loss function to fit the model parameters.
This paper proposes enhancing trusted domain enforcement through a VMM interruption mechanism. Current systems lack fine-grained input validation and dynamic access control to resources. The proposed system detects illegal inputs and moves processes to an untrusted domain for sandboxing. When an invalid input is detected, the guest OS notifies the VMM through virtualized interrupts. The VMM then isolates the compromised domain by disabling its network and block devices. The system was implemented through Linux kernel patches and a security module to validate inputs and enforce domain transitions. Performance testing showed the module approach had lower overhead than modifying the kernel directly.