Multimedia streaming sessions in general and Voice over Internet Protocol (VoIP) calls in specifi... more Multimedia streaming sessions in general and Voice over Internet Protocol (VoIP) calls in specific are more vulnerable to security attacks than traditional telephone calls due to the vulnerability of the IP network itself. Currently, there is a great interest in securing VoIP calls especially after many recent leaks and eavesdropping cases. The most common security enforcer applied to achieve confidentiality is encryption. Applying encryption techniques to VoIP traffic should take into consideration strict delay requirement for real-time traffic. Also, the type of speech coder has also implications as different speech coders have different accumulation and algorithmic delays. Encryption techniques could be used before or after speech coding. In this paper, several experiments have been conducted to assess the effect of encryption, speech coding and packet loss on speech quality. Perceptual Evaluation of Speech Quality (PESQ) as a speech-specific quality evaluation technique is used in this paper, then it will be mapped to the most common metric in opinion rating; Mean Opinion Score (MOS) values to test the quality of the received voice stream.
Android Operating System becomes a major target for malicious attacks. Static analysis approach i... more Android Operating System becomes a major target for malicious attacks. Static analysis approach is widely used to detect malicious applications. Most of existing studies on static analysis frameworks are limited to certain features. This paper presents an Android Static Analysis Framework (ASAF) which models the overall static analysis phases and approaches for Android applications. ASAF can be implemented for different purposes including Android malicious apps detection. The proposed framework utilizes a parsing tool, Android Static Parse (ASParse) which is also introduced in this paper. Through the extendibility of the ASParse tool, future research studies can easily extend the parsed features and the parsed files to perform parsing based on their specific requirements and goals. Moreover, a case study is conducted to illustrate the implementation of the proposed ASAF.
The booming of Android technology makes Android users and their devices with all running applicat... more The booming of Android technology makes Android users and their devices with all running applications targeted by many security attackers. These attackers intent to inject malicious software in these applications to obtain the user’s data for several purposes. Nowadays, Android users are keen to install clean apps in their devices. At the same time, security solutions have been proposed to detect malware apps. This paper presents a general framework for Android applications scanning process. The aim is to guide researchers and developers to the main phases/steps required to analyze Android applications, check their trustworthiness and protect Android users from being victims to serious malware attacks. Moreover, this research highlights the metrics, tools, mechanisms and datasets that are mostly used throughout the scanning process.
Cybersecurity attacks are still causing significant threats to individuals and organizations, aff... more Cybersecurity attacks are still causing significant threats to individuals and organizations, affecting almost all aspects of life. Therefore, many countries worldwide try to overcome this by introducing and applying cybersecurity regularity frameworks to maintain organizations’ information and digital resources. Saudi Arabia has taken practical steps in this direction by developing the essential cybersecurity control (ECC) as a national cybersecurity regulation reference. Generally, the compliance assessment processes of different international cybersecurity standards and controls (ISO2700x, PCI, and NIST) are generic for all organizations with different scopes, business functionality, and criticality level, where the overall compliance score is absent with no consideration of the security control risk. Therefore, to address all of these shortcomings, this research takes the ECC as a baseline to build a comprehensive and customized risk-based cybersecurity compliance assessment sys...
This paper offers a comprehensive analysis model for android malware. The model presents the esse... more This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based. Current android malware analysis and solutions might consider one or some of these factors while building their malware predictive systems. However, this paper comprehensively highlights these factors and their impacts through a deep empirical study. The study comprises 22 CNN (Convolutional Neural Network) algorithms, 21 of them are well-known, and one proposed algorithm. Additionally, several types of files are considered before converting them to images, and two benchmark android malware datasets are utilized. Finally, comprehensive evaluation metrics are measured to assess the produced predictive models from the security and complexity perspectives. Consequently, guiding researchers and developers to plan and build efficient malware analysis systems that meet their requirements and resources. The r...
The coronavirus pandemic has spread over the past two years in our highly connected and informati... more The coronavirus pandemic has spread over the past two years in our highly connected and information-dense society. Nonetheless, disseminating accurate and up-to-date information on the spread of this pandemic remains a challenge. In this context, opting for a solution based on conversational artificial intelligence, also known under the name of the chatbot, is proving to be an unavoidable solution, especially since it has already shown its effectiveness in fighting the coronavirus crisis in several countries. This work proposes to design and implement a smart chatbot on the theme of COVID-19, called COVIBOT, which will be useful in the context of Saudi Arabia. COVIBOT is a generative-based contextual chatbot, which is built using machine learning APIs that are offered by the cloud-based Azure Cognitive Services. Two versions of COVIBOT are offered: English and Arabic versions. Use cases of COVIBOT are tested and validated using a scenario-based approach. Keywords— Smart Chatbot, COV...
The Internet of Things (IoT) is prone to malware assaults due to its simple installation and auto... more The Internet of Things (IoT) is prone to malware assaults due to its simple installation and autonomous operating qualities. IoT devices have become the most tempting targets of malware due to well-known vulnerabilities such as weak, guessable, or hard-coded passwords, a lack of secure update procedures, and unsecured network connections. Traditional static IoT malware detection and analysis methods have been shown to be unsatisfactory solutions to understanding IoT malware behavior for mitigation and prevention. Deep learning models have made huge strides in the realm of cybersecurity in recent years, thanks to their tremendous data mining, learning, and expression capabilities, thus easing the burden on malware analysts. In this context, a novel detection and multi-classification vision-based approach for IoT-malware is proposed. This approach makes use of the benefits of deep transfer learning methodology and incorporates the fine-tuning method and various ensembling strategies t...
Multimedia streaming sessions in general and Voice over Internet Protocol (VoIP) calls in specifi... more Multimedia streaming sessions in general and Voice over Internet Protocol (VoIP) calls in specific are more vulnerable to security attacks than traditional telephone calls due to the vulnerability of the IP network itself. Currently, there is a great interest in securing VoIP calls especially after many recent leaks and eavesdropping cases. The most common security enforcer applied to achieve confidentiality is encryption. Applying encryption techniques to VoIP traffic should take into consideration strict delay requirement for real-time traffic. Also, the type of speech coder has also implications as different speech coders have different accumulation and algorithmic delays. Encryption techniques could be used before or after speech coding. In this paper, several experiments have been conducted to assess the effect of encryption, speech coding and packet loss on speech quality. Perceptual Evaluation of Speech Quality (PESQ) as a speech-specific quality evaluation technique is used in this paper, then it will be mapped to the most common metric in opinion rating; Mean Opinion Score (MOS) values to test the quality of the received voice stream.
Android Operating System becomes a major target for malicious attacks. Static analysis approach i... more Android Operating System becomes a major target for malicious attacks. Static analysis approach is widely used to detect malicious applications. Most of existing studies on static analysis frameworks are limited to certain features. This paper presents an Android Static Analysis Framework (ASAF) which models the overall static analysis phases and approaches for Android applications. ASAF can be implemented for different purposes including Android malicious apps detection. The proposed framework utilizes a parsing tool, Android Static Parse (ASParse) which is also introduced in this paper. Through the extendibility of the ASParse tool, future research studies can easily extend the parsed features and the parsed files to perform parsing based on their specific requirements and goals. Moreover, a case study is conducted to illustrate the implementation of the proposed ASAF.
The booming of Android technology makes Android users and their devices with all running applicat... more The booming of Android technology makes Android users and their devices with all running applications targeted by many security attackers. These attackers intent to inject malicious software in these applications to obtain the user’s data for several purposes. Nowadays, Android users are keen to install clean apps in their devices. At the same time, security solutions have been proposed to detect malware apps. This paper presents a general framework for Android applications scanning process. The aim is to guide researchers and developers to the main phases/steps required to analyze Android applications, check their trustworthiness and protect Android users from being victims to serious malware attacks. Moreover, this research highlights the metrics, tools, mechanisms and datasets that are mostly used throughout the scanning process.
Cybersecurity attacks are still causing significant threats to individuals and organizations, aff... more Cybersecurity attacks are still causing significant threats to individuals and organizations, affecting almost all aspects of life. Therefore, many countries worldwide try to overcome this by introducing and applying cybersecurity regularity frameworks to maintain organizations’ information and digital resources. Saudi Arabia has taken practical steps in this direction by developing the essential cybersecurity control (ECC) as a national cybersecurity regulation reference. Generally, the compliance assessment processes of different international cybersecurity standards and controls (ISO2700x, PCI, and NIST) are generic for all organizations with different scopes, business functionality, and criticality level, where the overall compliance score is absent with no consideration of the security control risk. Therefore, to address all of these shortcomings, this research takes the ECC as a baseline to build a comprehensive and customized risk-based cybersecurity compliance assessment sys...
This paper offers a comprehensive analysis model for android malware. The model presents the esse... more This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based. Current android malware analysis and solutions might consider one or some of these factors while building their malware predictive systems. However, this paper comprehensively highlights these factors and their impacts through a deep empirical study. The study comprises 22 CNN (Convolutional Neural Network) algorithms, 21 of them are well-known, and one proposed algorithm. Additionally, several types of files are considered before converting them to images, and two benchmark android malware datasets are utilized. Finally, comprehensive evaluation metrics are measured to assess the produced predictive models from the security and complexity perspectives. Consequently, guiding researchers and developers to plan and build efficient malware analysis systems that meet their requirements and resources. The r...
The coronavirus pandemic has spread over the past two years in our highly connected and informati... more The coronavirus pandemic has spread over the past two years in our highly connected and information-dense society. Nonetheless, disseminating accurate and up-to-date information on the spread of this pandemic remains a challenge. In this context, opting for a solution based on conversational artificial intelligence, also known under the name of the chatbot, is proving to be an unavoidable solution, especially since it has already shown its effectiveness in fighting the coronavirus crisis in several countries. This work proposes to design and implement a smart chatbot on the theme of COVID-19, called COVIBOT, which will be useful in the context of Saudi Arabia. COVIBOT is a generative-based contextual chatbot, which is built using machine learning APIs that are offered by the cloud-based Azure Cognitive Services. Two versions of COVIBOT are offered: English and Arabic versions. Use cases of COVIBOT are tested and validated using a scenario-based approach. Keywords— Smart Chatbot, COV...
The Internet of Things (IoT) is prone to malware assaults due to its simple installation and auto... more The Internet of Things (IoT) is prone to malware assaults due to its simple installation and autonomous operating qualities. IoT devices have become the most tempting targets of malware due to well-known vulnerabilities such as weak, guessable, or hard-coded passwords, a lack of secure update procedures, and unsecured network connections. Traditional static IoT malware detection and analysis methods have been shown to be unsatisfactory solutions to understanding IoT malware behavior for mitigation and prevention. Deep learning models have made huge strides in the realm of cybersecurity in recent years, thanks to their tremendous data mining, learning, and expression capabilities, thus easing the burden on malware analysts. In this context, a novel detection and multi-classification vision-based approach for IoT-malware is proposed. This approach makes use of the benefits of deep transfer learning methodology and incorporates the fine-tuning method and various ensembling strategies t...
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