Mazdak technique for PSNR estimation in audio steganography

A novel method to estimate PSNR of the resu lt of audio steganography before embedding is presented. Estimated PSNR by proposed linear interpolation formula was tested and the result was almost the same with the obtained PSNR in practical way

PSW statistical LSB image steganalysis

Steganography is the art and science of producing covert communications by concealing secret messages in apparently innocent media, while steganalysis is the art and science of detecting the existence of these. This manuscript proposes a novel blind statistical steganalysis technique to detect Least Significant Bit (LSB) flipping image steganography. It shows that the technique has a number of major advantages. First, a novel method of pixel color correlativity analysis in Pixel Similarity Weight (PSW). Second, filtering out image pixels according to their statistically detected suspiciousness, thereby excluding neutral pixels from the steganalysis process. Third, ranking suspicious pixels according to their statistically detected suspiciousness and determining the influence of such pixels based on the level of detected anomalies. Fourth, the capability to classify and analyze pixels in three pixel classes of flat, smooth and edgy, thereby enhancing the sensitivity of the steganalysis. Fifth, achieving an extremely high efficiency level of 98.049% in detecting 0.25bpp stego images with only a single dimension analysis

Interpretative key management framework (IKM)

Cryptography has been employed to establishing secure communication over insecure networks. Using symmetric keys to encipher and decipher data is one of common practices for achieving secrecy over networks. To employ symmetric key cryptography we require a secure and reliable key management framework for generating, distributing, and finally revoking keys. Many attacks endanger security of key management in each step and so need of secure key management frameworks now is felt more than ever. Proposed, Interpretative Key Management, framework reduces likelihood of attacks by eliminating key storage, reducing many times key distribution to just one time interpreter distribution, and increases security by means of using minutely, hourly, or daily key without need of key distribution. Also key revocation is automated process and IKM doesn’t require revocation call

Definitions and Criteria of CIA Security Triangle in Electronic Voting System Authors

Confidentiality, Integrity, and Availability are three sides of the famous CIA security triangle. Since the e-voting systems are built from particular components, the CIA security triangle of these systems has particular definitions for each side. This paper presents these CIA security definitions and criteria which each state-of-the-art electronic voting system must meet based on the view point of National Institute of Standard and Technology (NIST) and also the criteria proposed by pioneer e-voting researchers. According to jurisdiction of different countries some of the given definitions and criteria might be excluded for developed e-voting system of their territory. Beside of the definitions and criteria, current threats and proposed solutions (in 2012) of each CIA triangle side and current unresolved security threats are concisely described

Monitoring the security of audio biomedical signals communications in wearable IoT healthcare

The COVID-19 pandemic has imposed new challenges on the healthcare industry as hospital staff are exposed to a massive coronavirus load when registering new patients, taking temperatures, and providing care. The Ebola epidemic of 2014 is another example of a pandemic which a hospital in New York decided to use an audio-based communication system to protect nurses. This idea quickly turned into an Internet of Things (IoT) healthcare solution to help to communicate with patients remotely. However, it has grabbed the attention of criminals who use this medium as a cover for secret communication. The merging of signal processing and machine-learning techniques has led to the development of steganalyzers with very higher efficiencies, but since the statistical properties of normal audio files differ from those of purely speech audio files, the current steganalysis practices are not efficient enough for this type of content. This research considers the Percent of Equal Adjacent Samples (PEAS) feature for speech steganalysis. This feature efficiently discriminates the least significant bit stego speech samples from clean ones with a single analysis dimension. A sensitivity of 99.82% was achieved for the steganalysis of 50% embedded stego instances using a classifier based on the Gaussian membership function

A proposed framework for P2P botnet detection

Botnet is most widespread and occurs commonly in today‘s cyber attacks, resulting in serious threats to our network assets and organization’s properties. Botnets are collections of compromised computers (Bots) which are remotely controlled by its originator (BotMaster) under a common Command-and-Control (C&C) infrastructure. They are used to distribute commands to Bots for malicious activities such as distributed denial-of-service (DDoS) attacks, spam and phishing. Most of the existing botnet detection approaches concentrate only on particular botnet command and control (C&C) protocols (e.g., IRC,HTTP) and structures (e.g., centralized), and can become ineffective as botnets change their structure and C&C techniques. In this paper we proposed a new detection framework which focuses on P2P based botnets. This proposed framework is based on our definition of botnets. We define a botnet as a group of bots that will perform similar communication and malicious activity patterns within the same botnet. In our proposed detection framework, we monitor the group of hosts that show similar communication pattern in one stage and also performing malicious activities in another step, and finding common hosts on them

PFW: Polygonal Fuzzy Weighted—An SVM Kernel for the Classification of Overlapping Data Groups

Support vector machines are supervised learning models which are capable of classifying data and measuring regression by means of a learning algorithm. If data are linearly separable, a conventional linear kernel is used to classify them. Otherwise, the data are normally first transformed from input space to feature space, and then they are classified. However, carrying out this transformation is not always practical, and the process itself increases the cost of training and prediction. To address these problems, this paper puts forward an SVM kernel, called polygonal fuzzy weighted or PFW, which effectively classifies data without space transformation, even if the groups in question are not linearly separable and have overlapping areas. This kernel is based on Gaussian data distribution, standard deviation, the three-sigma rule and a polygonal fuzzy membership function. A comparison of our PFW, radial basis function (RBF) and conventional linear kernels in identical experimental conditions shows that PFW produces a minimum of 26% higher classification accuracy compared with the linear kernel, and it outperforms the RBF kernel in two-thirds of class labels, by a minimum of 3%. Moreover, Since PFW runs within the original feature space, it involves no additional computational cost

A novel approach for genetic audio watermarking

This paper presents a novel, principled approach to resolve the remained problems of substitution technique of audio watermarking. Using the proposed genetic algorithm, message bits are embedded into multiple, vague and higher LSB layers, resulting in increased robustness. The robustness specially would be increased against those intentional attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well