Book review: Applied cryptography: Protocols, algorithms, and source code in C

In the last few years, we have seen an exponential increase in the number of Internet-enabled devices, which has resulted in popularity of fog and cloud computing among end users. End users expect high data rates coupled with secure data access for various applications executed either at the edge (fog computing) or in the core network (cloud computing). However, the bidirectional data flow between the end users and the devices located at either the edge or core may cause congestion at the cloud data centers, which are used mainly for data storage and data analytics. The high mobility of devices (e.g., vehicles) may also pose additional challenges with respect to data availability and processing at the core data centers. Hence, there is a need to have most of the resources available at the edge of the network to ensure the smooth execution of end-user applications. Considering the challenges of future user demands, we present an architecture that integrates cloud and fog computing in the 5G environment that works in collaboration with the advanced technologies such as SDN and NFV with the NSC model. The NSC service model helps to automate the virtual resources by chaining in a series for fast computing in both computing technologies. The proposed architecture also supports data analytics and management with respect to device mobility. Moreover, we also compare the core and edge computing with respect to the type of hypervisors, virtualization, security, and node heterogeneity. By focusing on nodes' heterogeneity at the edge or core in the 5G environment, we also present security challenges and possible types of attacks on the data shared between different devices in the 5G environment.

Network Service Chaining in Fog and Cloud Computing for the 5G Environment: Data Management and Security Challenges

Cloud computing emerged in the last years to handle systems with large-scale services sharing between vast numbers of users. It provides enormous storage for data and computing power to users over the Internet. There are many issues with the high growth of data. Data security is one of the most important issues in cloud computing. There are many algorithms and implementation for data security. These algorithms provided various encryption methods. In this work, We present a comprehensive study between Symmetric key and Asymmetric key encryption algorithms that enhanced data security in cloud computing system. We discuss AES, DES, 3DES and Blowfish for symmetric encryption algorithms, and RSA, DSA, Diffie-Hellman and Elliptic Curve, for asymmetric encryption algorithms.

Comprehensive study of symmetric key and asymmetric key encryption algorithms

AbstractWith the major advancements in the field of technology and electronics, one persistent obstacle has proven to be one of the major challenges, namely : Data Security. To get connected securely and quickly through the electronic data transfer through the web, the data should be encrypted. Encryption is the process of transforming plain text into ciphered-text, which cannot be understood or altered easily by undesirable people. It can also be defined as the science that uses mathematics in data encryption and decryption operations. In this paper, we discuss several important algorithms used for the encryption and decryption of data in all fields, to make a comparative study for most important algorithms in terms of data security effectiveness, key size, complexity and time, etc. This research focused on different types of cryptography algorithms that are existing, like AES, DES, TDES, DSA, RSA, ECC, EEE and CR4...etc.

A Survey on Cryptography Algorithms

Information Security has become an important issue in data communication. Encryption algorithms have come up as a solution and play an important role in information security system. On other side, those algorithms consume a significant amount of computing resources such as CPU time, memory and battery power. Therefore it is essential to measure the performance of encryption algorithms. In this work, three encryption algorithms namely DES, AES and Blowfish are analyzed by considering certain performance metrics such as execution time, memory required for implementation and throughput. Based on the experiments, it has been concluded that the Blowfish is the best performing algorithm among the algorithms chosen for implementation.

Performance analysis of encryption algorithms for Information Security

The principal goal guiding the design of any encryption algorithm must be security against unauthorized attacks. However, for all practical applications, performance and speed are also important concerns. These are the two main characteristics that differentiate one encryption algorithm from another. This paper provides the performance comparison between four of the most commonly used encryption algorithms: DES(Data Encryption Standard), 3DES(Triple DES), BLOWFISH and AES (Rijndael). The comparison has been conducted by running several setting to process different sizes of data blocks to evaluate the algorithms encryption and decryption speed. Based on the performance analysis of these algorithms under different hardware and software platform, it has been concluded that the Blowfish is the best performing algorithm among the algorithms under the security against unauthorized attack and the speed is taken into consideration.

Peformance analysis of data encryption algorithms

The image forgery activities are on the rise because of the development of various image editing tools. Such activities are done by attackers with intentions of defaming people and websites or for gaining monetary advantage, extortion etc. Image forgeries are carried out through various ways, among one is the copy-move forgery. The basic process of copy-move image forgery is copying the objects present in an image and create the new image by using the copied objects or placing the copied object on the same image on a different location, hence the need for a forgery detection system to protect the authenticity of images. The existing forgery detection techniques detect the tampered regions with less efficiency because of the large size and lower contrast of the images. This article proposes an efficient technique for detecting the copy-move forged image based on deep learning. The proposed algorithm initializes the tampered image as the input for our system to detect the tampered region. Our system includes processes like segmentation, feature extraction, dense depth reconstruction, and finally identifying the tampered areas. The proposed deep learning based system can save on computational time and detect the duplicated regions with more accuracy.

An efficient copy move forgery detection using deep learning feature extraction and matching algorithm

Digital Forensic Investigation has seen a tremendous change in the past 25 years. From the age of early computers to the current day mobile devices and storage devices, the crime rate has also followed growth. With the diversity in crimes, frameworks have also been modified over time to cope-up with the pace of crimes being committed. The paper amalgamates all major approaches and models presented that have helped in shaping the digital forensic process. Each discussed model is followed by its advantages and shortcomings.

Review of Digital Forensic Investigation Frameworks

Amazon’s Alexa, Apple’s Siri, Google Assistant and Microsoft’s Cortana, clearly illustrate the impressive research work and potentials to be explored in the field of conversational agents. Conversational agent, chatter-bot or chatbot is a program expected to converse with near-human intelligence. Chatbots are designed to be used either as task-oriented ones or simply open-ended dialogue generator. Many approaches have been proposed in this field which ranges from earlier versions of hard-coded response generator to the advanced development techniques in Artificial Intelligence. In a broader sense, these can be categorized as rule-based and neural network based. While rule-based relies on predefined templates and responses, a neural network based relies on deep learning models. Rule-based are preferable for simpler task-oriented conversations. Open-domain conversational modeling is a more challenging area and uses mostly neural network-based approaches. This paper begins with an introduction of chatbots, followed by in-depth discussion on various classical or rule-based and neural-network-based approaches. The evaluation metrics employed for chatbots are mentioned. The paper concludes with a table consisting of recent research done in the field. It covers all the latest and significant publications in the field, the evaluation metrics employed, the corpus which is used as well as the possible areas of enhancement that exist in the proposed techniques.

https://link.springer.com/content/pdf/10.1007/s42979-020-00255-3.pdf

Review of State-of-the-Art Design Techniques for Chatbots

Digital images face widespread tampering through a copy-move attack, wherein objects are added or removed from an image to create the copy-move forged image. This paper proposes a robust Copy Move Forgery Detection (CMFD) scheme by integrating both block-based and keypoint-based CMFD techniques. The input image is first segmented into non-overlapping blocks using a modified FCM clustering algorithm based on superpixels. The neighboring and similar superpixels influences are encompassed into FCM, and Emperor Penguin Optimization (EPO) is used to optimize the influential degree, which increases the segmentation performance. Gabor filter is applied to extract the features from each block. The Block features are then matched with each other to show the suspected forgery regions. The experimental results illustrate that the proposed technique attains better performance in terms of precision, recall, F1-score, and E-measure. It accomplishes excellent forgery detection outcomes compared with other existing CMFD processes, even under different perplexing circumstances.

Ritu Agarwal

Papers

Peformance analysis of data encryption algorithms

An efficient copy move forgery detection using deep learning feature extraction and matching algorithm

Review of Digital Forensic Investigation Frameworks

Review of State-of-the-Art Design Techniques for Chatbots

Robust copy-move forgery detection using modified superpixel based FCM clustering with emperor penguin optimization and block feature matching