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Layers of Security for Active RFID Tags
Published in Syed Ahson, Mohammad Ilyas, RFID Handbook, 2017
Shenchih Tung, Swapna Dontharaju, Leonid Mats, Peter J. Hawrylak, James T. Cain, Marlin H. Mickle, Alex K. Jones
Juels proposes an authentication protocol to combat tag cloning even in environments with untrusted readers [44]. This protocol assumes an authenticated tag that contains its own unique EPC identifier and its own 32-bit secret key. The readers authenticated to communicate with the tag contain a database of secret keys associated with the authenticated tags. During a transaction, a tag identifies itself and the reader verifies the identifier with its database. The reader responds with the appropriate secret key. Finally, the tag verifies the secret key from the authenticated reader. Unfortunately, if any adversary eavesdrops during this authentication process the tag identifier and secret key can be captured. Data encryption is required to protect this technique for this information leakage.
Image encryption algorithm based on semi-tensor product theory
Published in Journal of Modern Optics, 2022
Yi Xiao, Zhen-Rong Lin, Qian Xu, Jin Du, Li-Hua Gong
According to the definition of Shannon entropy, the maximal entropy value of a 256-level grey image is 8 bits. The closer the entropy value of the image is to 8 bits, the better the image encryption algorithm working on pixel uniformity will be. The semi-tensor product-based image encryption algorithm can diffuse the pixel values rapidly. And the confusing and diffusing effect of the pixels can be enhanced by the four-way diffusion operation with random interpolation and Josephus scrambling algorithm in the octal plane, so that the pixels can be distributed as evenly as possible. Table 4 shows the results of information entropy values before and after image encryption. The results are compared with [18,34,39,40] and shown in Table 5. It can be seen that the global information entropy of images ‘Peppers’, ‘House’, ‘Black’ and ‘White’ are close to 8 bits after being encrypted by the proposed image encryption algorithm, and the local Shannon entropy are within the critical range at 5%, 1% and 0.1%, which indicates that the encryption pixels tend to distribute with equal probability. Therefore, the image encryption scheme based on chaotic system and semi-tensor product is secure in the event of the statistical analysis attack and can effectively avoid information leakage.
Metaheuristic neural networks for anomaly recognition in industrial sensor networks with packet latency and jitter for smart infrastructures
Published in International Journal of Computers and Applications, 2021
Amin Mansouri, Babak Majidi, Abdolah Shamisa
The recognition of anomalies in the industrial sensor networks is investigated by many researchers during the past decades. Brundle et al. [1] discussed the importance of security for industrial control systems and listed the SCADA security challenges and provided suitable industry responses to these challenges. Hong at al. [2] discussed the security issues in the SCADA and smart grid technologies. Kang et al. [3] presented anomalies faced by the SCADA systems and the methods for finding these anomalies. The anomalies and the threats to the SCADA systems are commonly one of the following: Traffic Analysis, Sabotage, Information Leakage, Authorization Violation, Trap Door / Back Door, Scavenging, Intercept / Alter, Bombs (Logic or Time), Trojan Horse, Spying, Interference Database Query Analysis, Browsing, Tunnelling, Service Spoofing, Masquerade, Bypassing Controls, Unauthorized Access Violations of Permission, Sniffers, Physical Intrusion, Data Modification, Unauthorized Access, Substitution, Replay, Denial of Service, Virus, Repudiation, Eavesdropping, Worm, Theft and Resource Exhaustion. These attacks are aimed at accessing the SCADA servers. The IDS are used to collect and analyze system activity data in order to monitor the state of a system. They also examine a system state and run integrated checks on files inside the system. Many IDS use machine learning algorithms to detect patterns in order to detect malicious activities that are abnormal for the system.
Exploration for Software Mitigation to Spectre Attacks of Poisoning Indirect Branches
Published in IETE Technical Review, 2018
Baozi Chen, Qingbo Wu, Yusong Tan, Liu Yang, Peng Zou
Traditional security researches try to avoid sensitive information leakage through covert channels by restricting information-flow policies [1] or data encryption [2]. However, attackers can bypass those restrictions or encryptions and leak sensitive data through the side channel. Since personal multimedia information is stored in the cloud service, privacy protection becomes increasingly important [3–5]. Recently, researchers have discovered a new kind of attacks named Spectre which exploits speculative behavior of modern processors and side channels [6, 7]. By training the branch predictor, the adversary can misdirect the processor to execute unauthorized code speculatively and leak information through side channels. There are mainly two variants of Spectre attacks. One leverages conditional branches, and the other leverages indirect branches. Mitigating new attacks is thought to be expensive at the moment because it is based on common optimization techniques on modern processors. Since Spectre utilizes speculative execution and cache, simply disabling either of two would cause great degradation of performance.