Research and Development: Driving Innovation in Cyber Security
NCCS is dedicated to driving innovation and advancing the field of cyber security through research and development. On this page, you will find thought leadership content, collaborations and partnerships, and a list of our publications, all showcasing our expertise and commitment to improving our services and protecting our clients from cyber threats.
Privacy preserving mobile forensic framework using role‐based access control and cryptography
Authors: Muhammad Faraz Hyder, Saadia Arshad, Asad Arfeen, Tasbiha Fatima
Publication date: 2022/10/25
The rise of social media‐related crimes has led to the rise of mobile forensics. Since mobile forensics and privacy preservation are conflicting fields, it is important to find a middle ground where forensics can be performed on any device without compromising the confidentiality of an individual. This paper presents a framework called “role‐based mobile forensics framework with cryptography (RBMF2C)” that can be easily implemented and protects users' privacy and does not interfere with the forensic process. A mobile forensic platform called Sher‐locked phones developed using C# is also presented in this paper that is developed following the aforementioned RBMF2C framework.
Mission‐critical open‐source software adoption model validation using Partial Least Square‐Structural Equation Modeling
Authors: Umm‐e Laila, Najeed Ahmed, Asad Arfeen, Agha Yasir Ali, Mohammad Khurram, Muzammil Ahmed Khan
Publication date: 2022/10/17
This paper aims to validate the mission‐critical OSS (open‐source software) model acceptance process using a third‐order formative‐formative measuring model. A two‐stage formative‐formative model was used for partial least square analysis. It includes eight primary mission‐critical OSS adoption constructs and three second‐order (technological, organizational, and environmental).
Process based volatile memory forensics for ransomware detection
Authors: Asad Arfeen, Muhammad Asim Khan, Obad Zafar, Usama Ahsan
Publication date: 2022/2/15
Ransomware is an emerging category of malware that locks computer data via powerful cryptographic algorithms. The global propagation of ransomware is a serious threat for individuals and organizations. The banking sector and financial institutions are the prime targets of such ransomware attacks. In case of such an attack, the field of digital forensics helps in estimation of the severity and data loss caused by the attack. Traditional digital forensics investigations make use of static or behavioral analysis to detect malware in infected systems.
Toward accurate and intelligent detection of malware
Authors: Asad Arfeen, Zunair Ahmed Khan, Riaz Uddin, Usama Ahsan
Publication date: 2022/2/15
Malware is a constant threat to the safety of the public Internet and private networks. It also affects the security of endpoint devices. An infected endpoint device can take part in aggressive or slow distributed denial of service attacks globally. Polymorphic malware has rendered traditional signature‐based detection ineffective. Hence the efforts to identify malware have been focused on behavioral modeling to identify and classify malware. This behavioral identification paved the way for artificial intelligence (AI) in cybersecurity.
Endpoint Detection & Response: A Malware Identification Solution
Authors: Asad Arfeen, Saad Ahmed, Muhammad Asim Khan, Syed Faraz Ali Jafri
Publication date: 2021/11/23
Malicious hackers breach security perimeters, cause infrastructure disruptions as well as steal proprietary information, financial data, and violate consumers’ privacy. Protection of the whole organization by using the firm's security officers can be besieged with faulty warnings. Engineers must shift from console to console to put together investigative clues as a result of today's fragmented security technologies that cause frustratingly sluggish investigations.
A generalized machine learning‐based model for the detection of DDoS attacks
Authors: Murk Marvi, Asad Arfeen, Riaz Uddin
Publication date: 2021/11
As time is progressing, the number and the complexity of methods adopted for launching distributed denial of service (DDoS) attacks are changing. Therefore, we propose a methodology for the development of a generalized machine learning (ML)‐based model for the detection of DDoS attacks. After exploring various attributes of the dataset chosen for this study, we propose an integrated feature selection (IFS) method which consists of three stages and integration of two different methods, that is, filter and embedded methods to select features which highly contribute to the detection of various types of DDoS attacks.
Application layer classification of Internet traffic using ensemble learning model
Authors: Asad Arfeen, Khizar Ul Haq, Syed Muhammad Yasir
Publication date: 2021/7
Accurate application layer classification of Internet traffic has been a necessary requirement for various regulatory, control, and operational purposes of Internet service provider (ISP). Due to the dynamic and ever evolving nature of Internet applications generating a diverse mixture of Internet traffic, it has been necessary to apply deep packet inspection (DPI) techniques for traffic classification. DPI methods offer accuracy but degrade overall network throughput and thus cause problems in ensuring quality of service (QoS) and maintaining service‐level agreements.