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Memory Manipulation at the Assembly Level: The Dangerous Art of Reverse Engineering
Manipulating memory at the assembly level is where software stops being abstract and becomes physical — registers, stacks, heaps, and raw bytes. For those who work there, it’s intoxicating: you can watch high-level behavior collapse into a handful of instructions, discover why a crash happens, or understand exactly how a program enforces (or fails…
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Advanced Persistent Threats (APT) on Mobile and Network Systems
Advanced Persistent Threats (APTs) represent the highest tier of targeted cyberattacks: long-term, strategic intrusions executed by highly skilled adversaries, often state-sponsored groups or well-funded criminal organizations. Their goal is simple: remain inside a system for as long as possible while silently gathering intelligence, manipulating assets, or preparing for strategic disruption. Unlike common malware or…
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Advanced Persistent Threats (APTs) on Mobile Devices and Enterprise Networks
Advanced Persistent Threats (APTs) are not ordinary cyberattacks. They are long-term, highly coordinated intrusion campaigns typically executed by well-resourced groups with strategic goals. These groups often include state-sponsored units, cyber mercenaries, or organized criminal operations. Their objective is not quick profit or temporary disruption; their goal is ongoing access, intelligence gathering, and silent control.…
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Post-Quantum Cryptography: The Final Digital Defense Line
The rapid progress of quantum computing has forced a major shift in the foundations of modern cybersecurity. Today’s most widely used cryptographic systems — RSA, Diffie-Hellman, and Elliptic Curve Cryptography (ECC) — were designed under the assumption that certain mathematical problems require an impractical amount of time to solve. Quantum computers break that assumption.…
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Artificial Intelligence at the Hardware Level: From TPU to NPU
The Shift Beyond CPUs and GPUs For years, artificial intelligence workloads relied primarily on central processing units (CPUs) and graphics processing units (GPUs). While GPUs revolutionized deep learning with their parallel processing capabilities, they were still general-purpose chips — not built specifically for AI. As models grew larger and more complex, the need for…