Visible to the public Biblio

Filters: Author is Guizani, Mohsen  [Clear All Filters]
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A
Ahmed, Abdelmuttlib Ibrahim Abdalla, Khan, Suleman, Gani, Abdullah, Hamid, Siti Hafizah Ab, Guizani, Mohsen.  2018.  Entropy-based Fuzzy AHP Model for Trustworthy Service Provider Selection in Internet of Things. 2018 IEEE 43rd Conference on Local Computer Networks (LCN). :606—613.

Nowadays, trust and reputation models are used to build a wide range of trust-based security mechanisms and trust-based service management applications on the Internet of Things (IoT). Considering trust as a single unit can result in missing important and significant factors. We split trust into its building-blocks, then we sort and assign weight to these building-blocks (trust metrics) on the basis of its priorities for the transaction context of a particular goal. To perform these processes, we consider trust as a multi-criteria decision-making problem, where a set of trust worthiness metrics represent the decision criteria. We introduce Entropy-based fuzzy analytic hierarchy process (EFAHP) as a trust model for selecting a trustworthy service provider, since the sense of decision making regarding multi-metrics trust is structural. EFAHP gives 1) fuzziness, which fits the vagueness, uncertainty, and subjectivity of trust attributes; 2) AHP, which is a systematic way for making decisions in complex multi-criteria decision making; and 3) entropy concept, which is utilized to calculate the aggregate weights for each service provider. We present a numerical illustration in trust-based Service Oriented Architecture in the IoT (SOA-IoT) to demonstrate the service provider selection using the EFAHP Model in assessing and aggregating the trust scores.

B
Basyoni, Lamiaa, Erbad, Aiman, Alsabah, Mashael, Fetais, Noora, Guizani, Mohsen.  2019.  Empirical Performance Evaluation of QUIC Protocol for Tor Anonymity Network. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :635—642.
Tor's anonymity network is one of the most widely used anonymity networks online, it consists of thousands of routers run by volunteers. Tor preserves the anonymity of its users by relaying the traffic through a number of routers (called onion routers) forming a circuit. The current design of Tor's transport layer suffers from a number of problems affecting the performance of the network. Several researches proposed changes in the transport design in order to eliminate the effect of these problems and improve the performance of Tor's network. In this paper. we propose "QuicTor", an improvement to the transport layer of Tor's network by using Google's protocol "QUIC" instead of TCP. QUIC was mainly developed to eliminate TCP's latency introduced from the handshaking delays and the head-of-line blocking problem. We provide an empirical evaluation of our proposed design and compare it to two other proposed designs, IMUX and PCTCP. We show that QuicTor significantly enhances the performance of Tor's network.
E
Essam, Gehad, Shehata, Heba, Khattab, Tamer, Abualsaud, Khalid, Guizani, Mohsen.  2019.  Novel Hybrid Physical Layer Security Technique in RFID Systems. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :1299–1304.
In this paper, we propose a novel PHY layer security technique in radio frequency identification (RFID) backscatter communications system. In order to protect the RFID tag information confidentiality from the eavesdroppers attacks, the proposed technique deploys beam steering (BS) using a one dimensional (1-D) antenna array in the tag side in addition to noise injection from the reader side. The performance analysis and simulation results show that the new technique outperforms the already-existing noise injection security technique and overcomes its design limitations.
R
Rathore, Heena, Samant, Abhay, Guizani, Mohsen.  2019.  A Bio-Inspired Framework to Mitigate DoS Attacks in Software Defined Networking. 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1–5.
Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. Existing DoS prevention and mitigation models for SDN are computationally expensive and are slow to react. This paper introduces a novel biologically inspired architecture for SDN to detect DoS flooding attacks. The proposed biologically inspired architecture utilizes the concepts of the human immune system to provide a robust solution against DoS attacks in SDNs. The two layer immune inspired framework, viz innate layer and adaptive layer, is initiated at the data layer and the control layer of SDN, respectively. The proposed model is reactive and lightweight for DoS mitigation in SDNs.
W
Wei, Shengjun, Zhong, Hao, Shan, Chun, Ye, Lin, Du, Xiaojiang, Guizani, Mohsen.  2018.  Vulnerability Prediction Based on Weighted Software Network for Secure Software Building. 2018 IEEE Global Communications Conference (GLOBECOM). :1-6.

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.

X
Xia, Qi, Sifah, Emmanuel Boateng, Obour Agyekum, Kwame Opuni-Boachie, Xia, Hu, Acheampong, Kingsley Nketia, Smahi, Abla, Gao, Jianbin, Du, Xiaojiang, Guizani, Mohsen.  2019.  Secured Fine-Grained Selective Access to Outsourced Cloud Data in IoT Environments. IEEE Internet of Things Journal. 6:10749–10762.
With the vast increase in data transmission due to a large number of information collected by devices, data management, and security has been a challenge for organizations. Many data owners (DOs) outsource their data to cloud repositories due to several economic advantages cloud service providers present. However, DOs, after their data are outsourced, do not have complete control of the data, and therefore, external systems are incorporated to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data but prove to be deficient in providing suitable solutions to the problem. In this article, we propose a secure fine-grain access control system for outsourced data, which supports read and write operations to the data. We make use of an attribute-based encryption (ABE) scheme, which is regarded as a suitable scheme to achieve access control for security and privacy (confidentiality) of outsourced data. This article considers different categories of data users, and make provisions for distinct access roles and permissible actions on the outsourced data with dynamic and efficient policy updates to the corresponding ciphertext in cloud repositories. We adopt blockchain technologies to enhance traceability and visibility to enable control over outsourced data by a DO. The security analysis presented demonstrates that the security properties of the system are not compromised. Results based on extensive experiments illustrate the efficiency and scalability of our system.
Y
Yang, Wenti, Wang, Ruimiao, Guan, Zhitao, Wu, Longfei, Du, Xiaojiang, Guizani, Mohsen.  2020.  A Lightweight Attribute Based Encryption Scheme with Constant Size Ciphertext for Internet of Things. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.