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Transport Layer Security (TLS) based malware is one of the most hazardous malware types, as it relies on encryption to conceal connections. Due to the complexity of TLS traffic decryption, several anomaly-based detection studies have been conducted to detect TLS-based malware using different features and machine learning (ML) algorithms. However, most of these studies utilized flow features with no feature transformation or relied on inefficient flow feature transformations like frequency-based periodicity analysis and outliers percentage. This paper introduces TLSMalDetect, a TLS-based malware detection approach that integrates periodicity-independent entropy-based flow set (EFS) features generated by a flow feature transformation technique to solve flow feature utilization issues in related research. EFS features effectiveness was evaluated in two ways: (1) by comparing them to the corresponding outliers percentage and flow features using four feature importance methods, and (2) by analyzing classification performance with and without EFS features. Moreover, new Transmission Control Protocol features not explored in literature were incorporated into TLSMalDetect, and their contribution was assessed. This study’s results proved EFS features of the number of packets sent and received were superior to related outliers percentage and flow features and could remarkably increase the performance up to ~42% in the case of Support Vector Machine accuracy. Furthermore, using the basic features, TLSMalDetect achieved the highest accuracy of 93.69% by Naïve Bayes (NB) among the ML algorithms applied. Also, from a comparison view, TLSMalDetect’s Random Forest precision of 98.99% and NB recall of 92.91% exceeded the best relevant findings of previous studies. These comparative results demonstrated the TLSMalDetect’s ability to detect more malware flows out of total malicious flows than existing works. It could also generate more actual alerts from overall alerts than earlier re

Encryption is used to securely transmit data in open networks. Each type of data has its own features, therefore different techniques should be used to protect confidential image data from unauthorized access. Most of the available encryption algorithms are mainly used for textual data and may not be suitable for multimedia data such as images. In this paper, we introduce a block-based transformation algorithm based on the combination of image transformation and a well known encryption and decryption algorithm called Blowfish. The original image was divided into blocks, which were rearranged into a transformed image using a transformation algorithm presented here, and then the transformed image was encrypted using the Blowfish algorithm. The results showed that the correlation between image elements was significantly decreased by using the proposed technique. The results also show that increasing the number of blocks by using smaller block sizes resulted in a lower correlation and higher entropy

Most of the recent efforts addressing the issue of privacy have focused on devising algorithms for the anonymization and diversification of data.

This paper presents DPICS framework for composing component-based software system that can be modified at runtime. It is based on message pattern interaction between system’s components, which facilitates adding, removing, or replacing a component while the whole system is running.

Componentware seems to be a promising methodology for software development in order to cope with software complexity. With componentware, the software development is shifted from building everything from scratch into just assembling existing components.

This paper presents DPICS framework for composing component-based software system that can be modified at runtime. It is based on message pattern interaction between system’s components, which facilitates adding, removing, or replacing a component while the whole system is running.

In Component-based development, assembling components into systems is the major activity. Therefore, Components must be integrated through well-defined infrastructure. This paper presents a framework for composing component-based systems based on message-pattern interaction among the components; it also presents protocol-based rules to govern messages exchanges.

Many papers propose different approaches to evaluate the reliability of black-box components based on component's success. This paper determines or evaluates component's reliability based on its failure. Since even reliable components might fail, we argue that component's acquirer should evaluate the component's reliability based on its failure and not on its success- In this paper we propose a new approach towards determining the component's failure. Both the operational profile and the appropriate test cases are needed to support our approach.

Component-based software systems achieve their functionalities through interaction between their components. Analyzing the dependencies between systems components is an essential task in system reconfiguration. This paper discusses dependencies analysis significance when updating component-based system dynamically. It presents a service-based matrix model and nested graph as approaches to capture components' dependencies; it discusses using dependencies analysis for safe dynamic updating in component-based software systems.

Agent design patterns form a new methodology used to improve the development of software agents. Agent design patterns can help by capturing solutions to common problems in agent design [Lange and Oshima, 1998].Agent design patterns are applied in different systems such as knowledge management systems, real-time systems, and network management systems. Agent design patterns for business-based systems, aim to support different ecommerce paradigms business-to-business (B2B) and business-to-consumer (B2C).In this paper, we developed an approach for extracting agent-based design patterns for B2C e-commerce to improve business-based processes.This approach is called an Agent Pattern Driven Business Engineering (APDBE).Based on this approach, we derived two agent-based commerce design patterns namely, the De-coupler Design Pattern (DecDP), and the Dynamic Design Pattern (DynDP). These design patterns are used to support selling/buying-based processes in e-commerce domain.