skip to main content

Search for: All records

Creators/Authors contains: "Saadawi, T."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Blockchain technology has recently gained high popularity in data security, primarily to mitigate against data breach and manipulation. Since its inception in 2008, it has been applied in different areas mainly to maintain data integrity and consistency. Blockchain has been tailored to secure data due to its data immutability and distributive technology. Despite the high success rate in data security, the inability to identify compromised insider nodes is one of the significant problems encountered in blockchain architectures. A Blockchain network is made up of nodes that initiate, verify and validate transactions. If compromised, these nodes can manipulate submitted transactions, inject fake transactions, or retrieve unauthorized information that might eventually compromise the stored data's integrity and consistency. This paper proposes a novel method of detecting these compromised blockchain nodes using a server-side authentication process and thwart their activities before getting updated in the blockchain ledger. In evaluating the proposed system, we perform four common insider attacks, which fall under the following three categories: (1)Those attacks targeting the Blockchain to bring it down. (2) the attacks that attempt to inject fake data into the database. (3) The attacks that attempt to hijack or retrieve unauthorized data. We described how we implement themore »attacks and how our architecture detects them before they impact the network. Finally, we displayed the attack detection time for each attack and compared our approach with other existing methods.« less
  2. The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individual life. One of the emerging technologies that enables the evolution to 5G by bringing cloud capabilities near to the end users is Edge Computing or also known as Multi-Access Edge Computing (MEC) that will become pertinent towards the evolution of 5G. This evolution also entails growth in the threat landscape and increase privacy in concerns at different application areas, hence security and privacy plays a central role in the evolution towards 5G. Since MEC application instantiated in the virtualized infrastructure, in this paper we present a distributed application that aims to constantly introspect multiple virtual machines (VMs) in order to detect malicious activities based on their anomalous behavior. Once suspicious processes detected, our IDS in real-time notifies system administrator about the potential threat. Developed software is able to detect keyloggers, rootkits, trojans, process hiding and other intrusion artifacts via agent-less operation, by operating remotely or directly from the host machine. Remote memory introspection means no software to install, no notice tomore »malware to evacuate or destroy data. Experimental results of remote VMI on more than 50 different malicious code demonstrate average anomaly detection rate close to 97%. We have established wide testbed environment connecting networks of two universities Kyushu Institute of Technology and The City College of New York through secure GRE tunnel. Conducted experiments on this testbed deliver high response time of the proposed system.« less
  3. As organizations drastically expand their usage of collaborative systems and multi-user applications during this period of mass remote work, it is crucial to understand and manage the risks that such platforms may introduce. Improperly or carelessly deployed and configured systems hide security threats that can impact not only a single organization, but the whole economy. Cloud-based architecture is used in many collaborative systems, such as audio/video conferencing, collaborative document sharing/editing, distance learning and others. Therefore, it is important to understand that safety risk can be triggered by attacks on remote servers and confidential information might be compromised. In this paper, we present an AI powered application that aims to constantly introspect multiple virtual servers in order to detect malicious activities based on their anomalous behavior. Once the suspicious process(es) detected, the application in real-time notifies system administrator about the potential threat. Developed software is able to detect user space based keyloggers, rootkits, process hiding and other intrusion artifacts via agent-less operation, by operating directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Conducted experiments on more than twenty different types of malicious applications provide evidence of highmore »detection accuracy« less
  4. Despite the increased accuracy of intrusion detection systems (IDS) in identifying cyberattacks in computer networks and devices connected to the internet, distributed or coordinated attacks can still go undetected or not detected on time. The single vantage point limits the ability of these IDSs to detect such attacks. Due to this reason, there is a need for attack characteristics’ exchange among different IDS nodes. Researchers proposed a cooperative intrusion detection system to share these attack characteristics effectively. This approach was useful; however, the security of the shared data cannot be guaranteed. More specifically, maintaining the integrity and consistency of shared data becomes a significant concern. In this paper, we propose a blockchain-based solution that ensures the integrity and consistency of attack characteristics shared in a cooperative intrusion detection system. The proposed architecture achieves this by detecting and preventing fake features injection and compromised IDS nodes. It also facilitates scalable attack features exchange among IDS nodes, ensures heterogeneous IDS nodes participation, and it is robust to public IDS nodes joining and leaving the network. We evaluate the security analysis and latency. The result shows that the proposed approach detects and prevents compromised IDS nodes, malicious features injection, manipulation, or deletion, andmore »it is also scalable with low latency.« less
  5. In this on-going research, we propose a blockchain-based solution that facilitates a scalable and secured inter-healthcare EHRs exchange. These healthcare systems maintain their records on separate blockchain networks and are independent of each other. The proposed architecture can detect and prevent malicious activities on both stored and shared EHRs from either outsider or insider threats. It can also verify the integrity and consistency of EHR requests and replies from other healthcare systems and presents them in a standard format that can be easily understood by different healthcare nodes. In the preliminary result, we evaluate the security analysis against frequently encounter outsider and insider threats within a healthcare system. The result shows that the architecture detects and prevents outsider threats from uploading compromising EHRs into the blockchain and also prevents unauthorized retrieval of patient's information
  6. Software Keyloggers are dominant class of malicious applications that surreptitiously logs all the user activity to gather confidential information. Among many other types of keyloggers, API-based keyloggers can pretend as unprivileged program running in a user-space to eavesdrop and record all the keystrokes typed by the user. In a Linux environment, defending against these types of malware means defending the kernel against being compromised and it is still an open and difficult problem. Considering how recent trend of edge computing extends cloud computing and the Internet of Things (IoT) to the edge of the network, a new types of intrusiondetection system (IDS) has been used to mitigate cybersecurity threats in edge computing. Proposed work aims to provide secure environment by constantly checking virtual machines for the presence of keyloggers using cutting edge artificial immune system (AIS) based technology. The algorithms that exist in the field of AIS exploit the immune system’s characteristics of learning and memory to solve diverse problems. We further present our approach by employing an architecture where host OS and a virtual machine (VM) layer actively collaborate to guarantee kernel integrity. This collaborative approach allows us to introspect VM by tracking events (interrupts, system calls, memory writes,more »network activities, etc.) and to detect anomalies by employing negative selection algorithm (NSA).« less