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Topic Review
Rate-Distortion-Based Steganography Via Generative Adversarial Network
Information hiding can imperceptibly transfer secret information into chosen cover media. It can ensure the origins of data and behave as a second channel for data transmission. Steganography is the art of covering or hiding extra data inside a chosen cover message, e.g., an image. The term itself dates back to the 15th century; in a typical scenario, the sender hides a secret message inside a cover image and transmits it to the receiver, who recovers the message. Even if eavesdroppers monitor or intercept the communication in-between, no one besides the sender and receiver should detect the presence of the hidden message.
  • 290
  • 04 Aug 2022
Topic Review
Ransomware-Resilient Self-Healing XML Documents
The cybersecurity threat would inherently cause substantial financial losses and time wastage for affected organizations and users. A great deal of research has taken place across academia and around the industry to combat this threat and mitigate its danger. These ongoing endeavors have resulted in several detection and prevention schemas.  The self-healing version-aware ransomware recovery (SH-VARR) framework for XML documents is based on the novel idea of using the link concept to maintain file versions in a distributed manner while applying access-control mechanisms to protect these versions from being encrypted or deleted.
  • 667
  • 07 May 2022
Topic Review
Ransomware Detection, Avoidance, and Mitigation Scheme
Ransomware attacks have emerged as a major cyber-security threat wherein user data is encrypted upon system infection. Latest Ransomware strands using advanced obfuscation techniques along with offline C2 Server capabilities are hitting Individual users and big corporations alike. This problem has caused business disruption and, of course, financial loss. 
  • 915
  • 13 Jan 2022
Topic Review
Ransomware Detection Approaches and Techniques
In the Machine Learning approach, machine learning algorithms analyze and categorize ransomware behavior. Trained on datasets of both known ransomware and benign samples, these algorithms identify new ransomware based on learned characteristics. Machine learning techniques, such as Decision Trees, Support Vector Machines, and Artificial Neural Networks, are applied. Advantages include adaptability to new ransomware variations and scalability for handling large datasets.
  • 167
  • 23 Jan 2024
Topic Review
Ransomware Attack Detection
Several malware variants have attacked systems and data over time. Ransomware is among the most harmful malware since it causes huge losses. In order to get a ransom, ransomware is software that locks the victim’s machine or encrypts his personal information. Numerous research has been conducted to stop and quickly recognize ransomware attacks.
  • 206
  • 29 Nov 2023
Topic Review
Range Encoding
Range encoding is an entropy coding method defined by G. Nigel N. Martin in a 1979 paper, which effectively rediscovered the FIFO arithmetic code first introduced by Richard Clark Pasco in 1976. Given a stream of symbols and their probabilities, a range coder produces a space-efficient stream of bits to represent these symbols and, given the stream and the probabilities, a range decoder reverses the process. Range coding is very similar to arithmetic encoding, except that encoding is done with digits in any base, instead of with bits, and so it is faster when using larger bases (e.g. a byte) at small cost in compression efficiency. After the expiration of the first (1978) arithmetic coding patent, range encoding appeared to clearly be free of patent encumbrances. This particularly drove interest in the technique in the open source community. Since that time, patents on various well-known arithmetic coding techniques have also expired.
  • 377
  • 14 Oct 2022
Topic Review
Random Number Generation
Ever since the antiquity, random number generation has played an important role both in common everyday life activities, such as leisure games, as well as in the advancement of science. Such means as dice and coins have been employed since the ancient times in order to generate random numbers that were used for gambling, dispute resolution, leisure games, and perhaps even fortune-telling. The theory behind the generation of random numbers, as well as the ability to potentially predict the outcome of this process, has been heavily studied and exploited by mathematics, in an attempt to either ensure the randomness of the process, to gain an advantage in correctly predicting its future outcomes, or to approximate the results of rather complicated computations. Especially in cryptography, random numbers are used due to the aforementioned properties, so that attackers have no other option but to guess the secret. This fact, in conjunction with the ongoing digitalisation of our world, has led to an interest in random number generation within the framework of computer science. In this context, random number generation systems are classified into two main categories: pseudorandom number generators and true random number generators, with the former generating sequences of numbers that appear to be random, but are in fact completely predictable when the initial value (being referred to as the seed) and conditions used for the number generation process are known, and with the latter generating truly random sequences of numbers that can only be predicted (correctly) with negligible probability, even if the initial value and conditions are known. 
  • 991
  • 24 Mar 2023
Topic Review
Random Neighbor-Based Differential Evolution
Symmetry in a differential evolution (DE) transforms a solution without impacting the family of solutions. For symmetrical problems in differential equations, DE is a strong evolutionary algorithm that provides a powerful solution to resolve global optimization problems. DE/best/1 and DE/rand/1 are the two most commonly used mutation strategies in DE. The former provides better exploitation while the latter ensures better exploration. DE/Neighbor/1 is an improved form of DE/rand/1 to maintain a balance between exploration and exploitation which was used with a random neighbor-based differential evolution (RNDE) algorithm.
  • 160
  • 03 Nov 2023
Topic Review
Random Forest, Feedforward Neural Network, GRU and FinGAT
Stock prediction has garnered considerable attention among investors, with a recent focus on the application of machine learning techniques to enhance predictive accuracy. Prior research has established the effectiveness of machine learning in forecasting stock market trends, irrespective of the analytical approach employed, be it technical, fundamental, or sentiment analysis. 
  • 248
  • 18 Dec 2023
Topic Review
Rainfall Prediction System
Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations.
  • 976
  • 18 May 2022
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