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Today, Internet communication security has become more complex as technology becomes faster and more efficient, especially for resource-limited devices such as embedded devices, wireless sensors, and radio frequency identification (RFID) tags, and Internet of Things (IoT). Lightweight encryption algorithms provide security for these devices to protect data against intruders. But the limitation of using energy in lightweight block ciphers (LBCs) is one of the major challenges for ever-expanding IoT technologies. Also, these LBC are subject to Side-channel attacks, which are among the most cited threats to these ciphers. In this paper, a differential power attack (DPA) to the Midori64 block cipher is designed. According to the proposed method, an attack on the S-boxes of the first round is done to obtain half of the master key bits. Then, the S-boxes of the second round were attacked to obtain remaining the master key bits. The results confirmed that the key is ultimately obtained. With the low volume of computational complexity, we obtained the Midori block cipher key, which was considered secure, just by using 300 samples of the plaintext. Following the running of Midori64 on the AVR microcontroller of the Atmega32 model, the master key of Midori block cipher is discovered with 300 known texts. Furthermore, we obtained the master key with a smaller number of samples than the electromagnetic analysis attack.
Logic locking is used to protect integrated circuits (ICs) from piracy and counterfeiting. An encrypted IC implements the correct function only when the right key is input. Many existing logic-locking methods are subject to the powerful satisfiabilit
The threat from ransomware continues to grow both in the number of affected victims as well as the cost incurred by the people and organisations impacted in a successful attack. In the majority of cases, once a victim has been attacked there remain o
GPUs are increasingly being used in security applications, especially for accelerating encryption/decryption. While GPUs are an attractive platform in terms of performance, the security of these devices raises a number of concerns. One vulnerability
Membership inference (MI) attacks affect user privacy by inferring whether given data samples have been used to train a target learning model, e.g., a deep neural network. There are two types of MI attacks in the literature, i.e., these with and with
The purpose of this document is to study the security properties of the Silver Bullet algorithm against worst-case RowHammer attacks. We mathematically demonstrate that Silver Bullet, when properly configured and implemented in a DRAM chip, can secur