Using Python to Extract System Log Artifacts from MacOS
Using Python to Extract System Log Artifacts from MacOS
Using Python to Extract System Log Artifacts from MacOS
MacOS Forensic Artifacts Location
Walkthrough of the Hawat machine from Proving Grounds
Walkthrough of Wombo from Proving Grounds
Walkthrough of the Kevin machine from Proving Grounds
Walkthrough of the Helpdesk machine from Proving Grounds
This is a walkthrough of the flimsy machine that can be found in offsec's Proving Grounds. This machine is generally used for training for pentest certification exams, such as offsec's OSCP
Generative Artificial Intelligence (AI) has gained significant attention in recent years due to its potential to create new and innovative content, such as images, music, and text. However, as with any technology, generative AI is not immune to security threats and vulnerabilities.
In summary, the journal presents a convincing method, a TKRD (Trusted Kernel Rootkit Detection), to detect known and unknown rootkits in VMs from private cloud environments. The method combines the memory forensic analysis and machine learning to detect viruses with proven experimental results. However, some assumptions are required for further study.
The widespread use of PDF documents is often a pervasive channel for malware distribution. This is accomplished by embedding malware and malicious code within PDFs, as PDFs can contain static elements (i.e., images and text), dynamic elements (i.e., JavaScript, forms) and embedded signatures.
The most dangerous attacks on internet services and networks are Distributed Denial of Service Attacks (DDoS), as discussed in the article " Distributed Denial of Service Attacks - TCP Syn Flooding Attack Mitigation ". The TCP syn flood DDoS attacks on the Apache server are mitigated using a method that is given. With a chosen time period, the effect of syn flooding will be lessened.
SEC Proposed Cybersecurity Disclosure Rules: In March 2022, the Securities and Exchange Commission (“SEC”) announced [proposed amendments to its rules](https://www.sec.gov/rules/proposed/2022/33-11038.pdf) (“Proposed Rules”) concerning cybersecurity disclosures for publicly traded companies (“issuers”).
Intelligent OS X malware threat detection with code inspection
A Kernel Rootkit Detection Approach Based on Virtualization and Machine Learning
Tcp Syn Flood Attack Detection and Prevention System using Adaptive Thresholding Method
Detecting and Preventing Kernel Rootkit Attacks with Bus Snooping
A Method for Windows Malware Detection Based on Deep Learning
On November 9, 2022, the New York Department of Financial Services (NYDFS) released proposed amendments to its cybersecurity regulations that govern Class A financial entities licensed to operate in the state of New York defined as having over 2,000 employees or over $1 billion in gross annual revenue, and at least $20M in gross annual revenue in each of the last two fiscal years from business operations in New York.
Some individuals believe that cybersecurity is solely the government’s responsibility, or the responsibility of technology companies. However, every organization and individual has a responsibility to defend against cyberattacks.