Overview of Information Hiding Algorithms for Ensuring Security in IoT Based Cyber-Physical Systems

Evsutin, Oleg; Melman, Anna; El-Latif, Ahmed A. Abd

doi:10.1007/978-3-030-85428-7_5

Cited by 8 publications

(13 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we study the security framework in CPS cloud and compare the performance of our suggested technique to that of other existing methods. The existing methods are RSA [22], Twofish algorithm [23], improved chacha20 algorithm (ICC [24]), Fully Homomorphic encryption algorithm (FHEA [25]). Encryption time analyses and illustrates the average amount of time required to encrypt media content files as input.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An enhanced encryption-based security framework in the CPS Cloud

et al. 2022

View full text Add to dashboard Cite

The rapid advancement of computation techniques and cloud computing has led to substantial advancements in Cyber-Physical Systems (CPS), particularly in the field of health care. There are a variety of ways in which CPS is used in healthcare today, including delivering intelligent feedback systems, automatically updating patient data digitally, monitoring patients passively with biosensors, etc. In recent years, cyber-physical systems have become capable of making lifesaving decisions as they are becoming more connected to the cloud. However, healthcare has become one of the most critical issues for many. A CPS network uses the Internet of Medical Things (IoMT) to continuously monitor patients’ health metrics such as body temperature, heart rate, etc. Due to physical connectivity restrictions, networks are more susceptible to security threats. In spite of the fact that the data is stored in the cloud, it is necessary to provide security regardless of device security and network security. Several cyber-security vulnerabilities have been identified in cloud-based healthcare systems in particular. To give patients a reliable healthcare experience, security concerns with CPSs need to be addressed carefully. In this context, this paper proposes a Cross-Breed Blowfish and MD5 (CBM) approach to improve the security of health data in the CPS cloud. The proposed model uses the wireless sensor network, in which data acquired by the network is transmitted via the transmitting node. Using the fuzzified effective trust-based routing protocol (FET-RP), the most efficient path for data travel is selected. The best route is determined using Butter-Ant Optimization (BAO) algorithm. The proposed method conveys data throughput encryption and decryption in a decoded format. The encrypted data is then stored in the cloud database for security reasons. The route finding algorithm is the one which is sending the data from one end to other end. The data is encrypted based on the source and destination. We compare the performance metrics of our recommended technique to those of other existing techniques, such as RSA, Two fish, ICC, and FHEA, in order to ensure that it performs optimally. The values of Cross Breed Blowfish and MD5 and FET-RP with regard to the performance metrics in terms of encryption (60 ms), decryption (55 ms), latency (60 s), throughput (97 mbps), security level (98%), and execution time (57 ms) which outperforms the conventional methods by 10–15%. Also the proposed encryption shows the considerable improvement in the level of security making our model a real world solution.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The amount of time that elapses between the moment that a user submits a request and the time that the server RSA [22] 90…”

Section: Resultsmentioning

confidence: 99%

An enhanced encryption-based security framework in the CPS Cloud

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The difference between the image containing hidden data and the original image can be found only through some statistical treatments through which the proposed method is evaluated, and this will be discussing in detail later in the results section. Candidate pixels will manipulate according to embedding condition then will store again in stego image with different order and illustrated in ( 2) and (3).…”

Section: Figure 5 Classification Of Ir Via Svm Classifiermentioning

confidence: 99%

“…Step 4: extract regions R and P from vectors 𝑅 𝑖 = (𝐾 𝑖1 , 𝐾 𝑖2 , 𝐾 𝑖3 , … , 𝐾 𝑖(𝑛−1) ) 𝐾 𝑖𝑗 = (𝑃 𝑖𝑗 (1) , 𝑃 𝑖𝑗 (2) , 𝑃 𝑖𝑗 (3) , … , 𝑃 𝑖𝑗 (𝑛) )…”

Section: Extractingmentioning

confidence: 99%

“…There are three different basic methodologies to hide data and to ensure the security of data are encryption, steganography and watermark, thus they are no less important than the other [1]. Encryption is one of the basic ways to hide information because it works on a clear challenge in the form of cipher text, which may be simple at first glance, but in order to explain the complexity begins completely [2], [3]. As for concealment, it hides the text in certain media in an innocent way so as not to arouse suspicion during transmission.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hiding text using the least significant bit technique to improve cover image in the steganography system

Halboos

Al-Bakry

2022

Bulletin EEI

View full text Add to dashboard Cite

One of the highest priorities in the era of information technology is to achieve an accurate and effective system for hiding security data. One of the goals of steganography is imperceptability to intruder. So this paper work to increase the imperceptibility on image, which has weaknesses in previous studies, as well as to avoid statistical attacks such as chi-square. A method has been proposed that includes calculating the color contrasts in the homogeneous areas of the image and dividing them according to the color contrast and exploiting the data of pixels that have a high impact to embed on the two first and third bits of least significant bit (LSB) to increase the amount of embedded data, impact regions (IR) classify according to selected features extracted in advance by using the support vector machine (SVM) classifier. Work was done on standard images taken from a standard dataset (USC-SIPI) for two types of gray and color images. The results showed the worth of the proposed method through a high peak signal to noise ratio (PSNR) that reached 89.5 dB due to the distribution of data on pixels according to the proposed method

show abstract