As the amount of data being sent has risen dramatically in recent years; protecting that data has become increasingly important. Cryptography is the process of transforming plain text messages into ones that can no longer be decoded. An information security system's algorithm for encrypting and decrypting data cannot function without them. Data transmission via a communication network can benefit greatly from the use of encryption. Short message service (SMS) is still popular despite the availability of several online messaging platforms. Despite this, these services can be easily cracked, making SMS less secure for transferring critical information. The usage of appropriate cryptographic algorithms is essential to ensuring the safety of your data. Cryptanalysis and pattern prediction are better protected. This study uses a hybridized version of the Vigenère cipher with AES. In tests, the Vigenère-AES came out on top, and it may be used to keep sensitive data safe from prying eyes. 

An Encryption and Decryption Model for Data Security Using Vigenere With Advanced Encryption Standard

On the accuracy of BLE indoor localization systems: An assessment survey

The proliferation of Internet of Things (IoT) devices has created a ubiquitous network of interconnected sensors and devices that generate and exchange vast amounts of data. With this increased connectivity comes a pressing need for robust security measures to safeguard IoT communications' integrity, confidentiality, and authenticity. This paper categorizes cryptographic algorithms commonly employed in IoT security, including symmetric and asymmetric encryption methods, digital signatures, and key exchange protocols. By examining the attributes of these algorithms, their strengths, and potential vulnerabilities, this paper assesses their effectiveness within the unique context of IoT. Furthermore, the paper highlights the significance of cryptographic algorithm optimization in IoT security. Strategies for enhancing algorithm efficiency, such as algorithm selection, key management, and hardware acceleration are thoroughly explored. Through these case studies, this paper underscores the practical implications of cryptographic algorithm selection and optimization, illustrating how they can prevent security breaches and enhance IoT security.

Maximizing IoT Security: An Examination of Cryptographic Algorithms

Ambient assisted technology (AAT), which has the potential to enhance patient care and productivity and save costs, has emerged as a strategic goal for developing e-healthcare in the future. However, since the healthcare sensor must be interconnected with other systems at different network tiers, distant enemies have additional options to attack. Data and resources integrated into the AAT are vulnerable to security risks that might compromise privacy, integrity, and availability. The gadgets and network sensor devices are layered with clinical data since they save personal information such as patients’ names, addresses, and medical histories. Considering the volume of data, it is difficult to ensure its confidentiality and security. As sensing devices are deployed over a wider region, protecting the privacy of the collected data becomes more difficult. The current study proposes a lightweight security mechanism to ensure the data’s confidentiality and integrity of the data in ambient-assisted technology. In the current study, the data are encrypted by the master node with adequate residual energy, and the master node is responsible for encrypting the data using the data aggregation model using a node’s key generated using an exclusive basis system and a Chinese remainder theorem. The integrity of the data is evaluated using the hash function at each intermediate node. The current study defines the design model’s layered architecture and layer-wise services. The model is further analyzed using various evaluation metrics, such as energy consumption, network delay, network overhead, time in generating hash, tradeoff between encryption and decryption, and entropy metrics. The model is shown to adequately perform on all measures considered in the analysis. 

A Software Framework for Intelligent Security Measures Regarding Sensor Data in the Context of Ambient Assisted Technology

Proactive SSD Failure Prediction with A Gradient-Guided LSTM-xLSTM Hybrid Model

In order to protect network information security and improve the security of wireless sensor networks, based on chaotic systems, we propose a wireless sensor algorithm based on improved identity encryption. First, the basic principle of chaotic system mapping is specifically analyzed; the two chaotic mapping systems are rectified to obtain the hybrid chaotic mapping system according to the demand of wireless sensing network. After that, an encryption framework and key are designed and the hybrid mapping system is applied to the encryption framework to encrypt the data. In this way, the length of the encryption algorithm is lengthened and the defensibility of the encrypted content is improved. Finally, the performance of the proposed encryption algorithm is tested in terms of information entropy, statistical methods, and ciphertext randomness. The test results show that compared with other encryption algorithms, the running speed of the encryption algorithm proposed in this paper is only 18.51 ms, which is faster than that of other encryption algorithms, and the memory consumption is only 27%, which is much lower than that of other algorithms. It can be seen that the proposed encryption algorithm has strong encryption effect and superior algorithm performance for data encryption in wireless sensor networks.

Wireless Sensor Network Security Based on Improved Identity Encryption

Proactive drive failure prediction can help operators handle the failing drives in advance, enhancing the storage system dependability. SSD and HDD failure prediction techniques are currently evolving towards a semi-supervised approach. In this paper, we are dedicated to enhancing the methodology for semi-supervised drive failure prediction from these aspects: design more powerful, robust yet generic models, mine drive failure modes and make the prediction model interpretable. Specifically, we propose two semi-supervised drive failure prediction models, DFP-VL and DFP-FL, from the perspective of reconstructing SMART data and learning SMART data's probability density, respectively. They capture the pattern of healthy drives, and failing drives can be detected when they deviate from the normal pattern. We mine two failure modes, slow deterioration and dramatic deterioration. Based on that, we design two failure detectors, ”ThresholdDetector” and ”HybridDetector”, to determine whether a drive deviates from the normal pattern. We evaluate the proposed methods on both SSD and HDD SMART data. DFP-VL is generic yet effective and can interpret the predicted results. DFP-FL has better performance than DFP-VL, but it cannot interpret the results. ThresholdDetector has low complexity and can detect most failing drives. HybridDetector has high complexity and can further improve the detection performance. 

Proactive Drive Failure Prediction for Cloud Storage System through Semi-supervised Learning

Nowadays, Internet of Things (IoT) based smart home system is equipped with a large number of smart devices, such as smart speakers and cameras, which can greatly facilitate users to control and automate their home environment. However, recent studies have shown that smart home system is at great risk of privacy leakage. Especially, external attackers can infer user privacy information by passively sniffing encrypted smart home network traffic. Traditional methods mainly focus on sniffing WiFi devices but pay less attention to other commodity devices such as Zigbee and Bluetooth (BLE). In this paper, we focus on inferring fine-grained sensitive details about users using diverse commodity devices. We apply deep learning techniques to infer users' behaviors through identifying and localizing smart home devices being used due to the excellent performance of deep learning in many fields. Specifically, we first pre-process encrypted device traffic, select valid features, and use Convolutional Neural Networks (CNN) for device identification. In addition, we extract the Received Signal Strength Indicator (RSSI) from the frame information of traffic packets and employ Sparse Autoencoder (SAE) to extract stable and distinguishable high-dimensional features for RSSI measurement. Features are fed into a Multilayer Perceptron (MLP) to predict the device's localization. In this way, we can infer human activity by identifying and localizing the devices being used. Extensive experiment results show that our work can achieve a mean position estimation error of 1.34m even in an unseen environment, outperforming other common- used localization algorithms based on RSSI fingerprints.

Accurately Identify and Localize Commodity Devices from Encrypted Smart Home Traffic

In industrial, medical, transportation and other key applications, the dependence on the Internet of Things applications is increasing rapidly, which has greatly changed the security landscape. In the past, enterprise applications generally had available resources to deal with security algorithms at any time, but nowadays, enterprise-level IoT applications are suffering from increasing threats, and their attack targets are the expanding network of resource-constrained IoT devices. When enterprises are eager to meet the rapidly emerging opportunities of the Internet of Things, the deployed IoT devices often fail to support basic security measures in function, so it is difficult to protect the stored data, and it is impossible to guarantee the exchange of data and commands on vulnerable networks. The thesis is about the comparison of encryption algorithms for wearable devices. Because the most popular wearable devices in the market are smartwatches, choosing the smartwatches as sample to analysis. Moreover, based on the current cryptographic protocol standards that are being used in IoT, choosing AES, PRESENT, RSA and ECC algorithms as the comparison in the encryption algorithms. Also, the hybrid algorithm called HAN show impressive potential and security in encryption.

/pdf/comparison-of-encryption-algorithms-for-wearable-devices-in-1tbgn125.pdf

Comparison of Encryption Algorithms for Wearable Devices in IoT Systems

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Papers

Wireless Sensor Network Security Based on Improved Identity Encryption

Proactive Drive Failure Prediction for Cloud Storage System through Semi-supervised Learning

Accurately Identify and Localize Commodity Devices from Encrypted Smart Home Traffic

Comparison of Encryption Algorithms for Wearable Devices in IoT Systems