Visible to the public Biblio

Filters: Author is Tunc, Cihan  [Clear All Filters]
2020-08-13
Shao, Sicong, Tunc, Cihan, Al-Shawi, Amany, Hariri, Salim.  2019.  One-Class Classification with Deep Autoencoder Neural Networks for Author Verification in Internet Relay Chat. 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). :1—8.
Social networks are highly preferred to express opinions, share information, and communicate with others on arbitrary topics. However, the downside is that many cybercriminals are leveraging social networks for cyber-crime. Internet Relay Chat (IRC) is the important social networks which can grant the anonymity to users by allowing them to connect channels without sign-up process. Therefore, IRC has been the playground of hackers and anonymous users for various operations such as hacking, cracking, and carding. Hence, it is urgent to study effective methods which can identify the authors behind the IRC messages. In this paper, we design an autonomic IRC monitoring system, performing recursive deep learning for classifying threat levels of messages and develop a novel author verification approach with one-class classification with deep autoencoder neural networks. The experimental results show that our approach can successfully perform effective author verification for IRC users.
2020-08-24
Fargo, Farah, Franza, Olivier, Tunc, Cihan, Hariri, Salim.  2019.  Autonomic Resource Management for Power, Performance, and Security in Cloud Environment. 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). :1–4.
High performance computing is widely used for large-scale simulations, designs and analysis of critical problems especially through the use of cloud computing systems nowadays because cloud computing provides ubiquitous, on-demand computing capabilities with large variety of hardware configurations including GPUs and FPGAs that are highly used for high performance computing. However, it is well known that inefficient management of such systems results in excessive power consumption affecting the budget, cooling challenges, as well as reducing reliability due to the overheating and hotspots. Furthermore, considering the latest trends in the attack scenarios and crypto-currency based intrusions, security has become a major problem for high performance computing. Therefore, to address both challenges, in this paper we present an autonomic management methodology for both security and power/performance. Our proposed approach first builds knowledge of the environment in terms of power consumption and the security tools' deployment. Next, it provisions virtual resources so that the power consumption can be reduced while maintaining the required performance and deploy the security tools based on the system behavior. Using this approach, we can utilize a wide range of secure resources efficiently in HPC system, cloud computing systems, servers, embedded systems, etc.
2021-06-24
Wu, Chongke, Shao, Sicong, Tunc, Cihan, Hariri, Salim.  2020.  Video Anomaly Detection using Pre-Trained Deep Convolutional Neural Nets and Context Mining. 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA). :1—8.
Anomaly detection is critically important for intelligent surveillance systems to detect in a timely manner any malicious activities. Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. These deep learning methods use large-scale training data with large complexity. As a solution, in this paper, we show how to use pre-trained convolutional neural net models to perform feature extraction and context mining, and then use denoising autoencoder with relatively low model complexity to provide efficient and accurate surveillance anomaly detection, which can be useful for the resource-constrained devices such as edge devices of the Internet of Things (IoT). Our anomaly detection model makes decisions based on the high-level features derived from the selected embedded computer vision models such as object classification and object detection. Additionally, we derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method. We use two UCSD datasets to demonstrate that our approach with relatively low model complexity can achieve comparable performance compared to the state-of-the-art approaches.
2021-08-02
Fargo, Farah, Franza, Olivier, Tunc, Cihan, Hariri, Salim.  2020.  VM Introspection-based Allowlisting for IaaS. 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS). :1—4.
Cloud computing has become the main backend of the IT infrastructure as it provides ubiquitous and on-demand computing to serve to a wide range of users including end-users and high-performance demanding agencies. The users can allocate and free resources allocated for their Virtual Machines (VMs) as needed. However, with the rapid growth of interest in cloud computing systems, several issues have arisen especially in the domain of cybersecurity. It is a known fact that not only the malicious users can freely allocate VMs, but also they can infect victims' VMs to run their own tools that include cryptocurrency mining, ransomware, or cyberattacks against others. Even though there exist intrusion detection systems (IDS), running an IDS on every VM can be a costly process and it would require fine configuration that only a small subset of the cloud users are knowledgeable about. Therefore, to overcome this challenge, in this paper we present a VM introspection based allowlisting method to be deployed and managed directly by the cloud providers to check if there are any malicious software running on the VMs with minimum user intervention. Our middleware monitors the processes and if it detects unknown events, it will notify the users and/or can take action as needed.