Maria Mushtaq

Natasha Alkhatib, Maria Mushtaq, Hadi Ghauch et al.

Due to the rising number of sophisticated customer functionalities, electronic control units (ECUs) are increasingly integrated into modern automotive systems. However, the high connectivity between the in-vehicle and the external networks paves the way for hackers who could exploit in-vehicle network protocols' vulnerabilities. Among these protocols, the Controller Area Network (CAN), known as the most widely used in-vehicle networking technology, lacks encryption and authentication mechanisms, making the communications delivered by distributed ECUs insecure. Inspired by the outstanding performance of bidirectional encoder representations from transformers (BERT) for improving many natural language processing tasks, we propose in this paper ``CAN-BERT", a deep learning based network intrusion detection system, to detect cyber attacks on CAN bus protocol. We show that the BERT model can learn the sequence of arbitration identifiers (IDs) in the CAN bus for anomaly detection using the ``masked language model" unsupervised training objective. The experimental results on the ``Car Hacking: Attack \& Defense Challenge 2020" dataset show that ``CAN-BERT" outperforms state-of-the-art approaches. In addition to being able to identify in-vehicle intrusions in real-time within 0.8 ms to 3 ms w.r.t CAN ID sequence length, it can also detect a wide variety of cyberattacks with an F1-score of between 0.81 and 0.99.

Natasha Alkhatib, Maria Mushtaq, Hadi Ghauch et al.

Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system which play an important role in detecting unknown attack patterns in network traffic. Hence, in this paper, we compare the performance of different unsupervised deep and machine learning based anomaly detection algorithms, for real-time detection of anomalies on the Audio Video Transport Protocol (AVTP), an application layer protocol implemented in the recent Automotive Ethernet based in-vehicle network. The numerical results, conducted on the recently published "Automotive Ethernet Intrusion Dataset", show that deep learning models significantly outperfom other state-of-the art traditional anomaly detection models in machine learning under different experimental settings.

M. Omer Mushtaq, Furrakh Shahzad, M. Owais Tariq et al.

There is a dynamic escalation and extension in the new infrastructure, educating personnel and licensing new computer programs in the field of IT, due to the emergence of Cloud Computing (CC) paradigm. It has become a quick growing segment of IT business in last couple of years. However, due to the rapid growth of data, people and IT firms, the issue of information security is getting more complex. One of the major concerns of the user is, at what degree the data is safe on Cloud? In spite of all promotional material encompassing the cloud, consortium customers are not willing to shift their business on the cloud. Data security is the major problem which has limited the scope of cloud computing. In new cloud computing infrastructure, the techniques such as the Strong Secure Shell and Encryption are deployed to guarantee the authenticity of the user through logs systems. The vendors utilize these logs to analyze and view their data. Therefore, this implementation is not enough to ensure security, privacy and authoritative use of the data. This paper introduces quad layered framework for data security, data privacy, data breaches and process associated aspects. Using this layered architecture we have preserved the secrecy of confidential information and tried to build the trust of user on cloud computing. This layered framework prevents the confidential information by multiple means i.e. Secure Transmission of Data, Encrypted Data and its Processing, Database Secure Shell and Internal/external log Auditing.