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Full-Text Articles in Computer Engineering
Lsav: Lightweight Source Address Validation In Sdn To Counteract Ip Spoofing-Based Ddos Attacks, Ali̇ Karakoç, Fati̇h Alagöz
Lsav: Lightweight Source Address Validation In Sdn To Counteract Ip Spoofing-Based Ddos Attacks, Ali̇ Karakoç, Fati̇h Alagöz
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we propose a design to detect and prevent IP spoofing-based distributed denial of service (DDoS) attacks on software-defined networks (SDNs). DDoS attacks are still one of the significant problems for internet service providers (ISPs) and individual users. These attacks can disrupt customer services by targeting the availability of the system, and in some cases, they can completely shut down the target infrastructure. Protecting the system against DDoS attacks is therefore crucial for ensuring the reliability and availability of internet services. To address this problem, we propose a lightweight source address validation (LSAV) framework that leverages the flexibility …
Secure State Estimation Of Distribution Network Based On Kalman Filter Decomposition, Xinghua Liu, Siwen Dong, Jiaqiang Tian
Secure State Estimation Of Distribution Network Based On Kalman Filter Decomposition, Xinghua Liu, Siwen Dong, Jiaqiang Tian
Journal of System Simulation
A new state estimation algorithm is proposed to improve the accuracy to obtain the optimal state estimation of distribution network against FDI attack. In the case of phasor measurement units being attacked and the measurement results being altered,the optimal Kalman estimate can be decomposed into a weighted sum of local state estimates. Focusing on the insecurity of the weighted sum method,a convex optimization based on local estimation is proposed to replace the method and combine the local estimation into a secure state estimation. The simulation results show that the proposed estimator is consistent with the Kalman …
Secure And Efficient Federated Learning, Xingyu Li
Secure And Efficient Federated Learning, Xingyu Li
Theses and Dissertations
In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …
Fuzzing Php Interpreters By Automatically Generating Samples, Jacob S. Baumgarte
Fuzzing Php Interpreters By Automatically Generating Samples, Jacob S. Baumgarte
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Modern web development has grown increasingly reliant on scripting languages such as PHP. The complexities of an interpreted language means it is very difficult to account for every use case as unusual interactions can cause unintended side effects. Automatically generating test input to detect bugs or fuzzing, has proven to be an effective technique for JavaScript engines. By extending this concept to PHP, existing vulnerabilities that have since gone undetected can be brought to light. While PHP fuzzers exist, they are limited to testing a small quantity of test seeds per second. In this thesis, we propose a solution for …
Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula
Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula
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Malware detection is a critical task in ensuring the security of computer systems. Due to a surge in malware and the malware program sophistication, machine learning methods have been developed to perform such a task with great success. To further learn structural semantics, Graph Neural Networks abbreviated as GNNs have emerged as a recent practice for malware detection by modeling the relationships between various components of a program as a graph, which deliver promising detection performance improvement. However, this line of research attends to individual programs while overlooking program interactions; also, these GNNs tend to perform feature aggregation from neighbors …
Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani
Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani
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Identifying the version of the Solidity compiler used to create an Ethereum contract is a challenging task, especially when the contract bytecode is obfuscated and lacks explicit metadata. Ethereum bytecode is highly complex, as it is generated by the Solidity compiler, which translates high-level programming constructs into low-level, stack-based code. Additionally, the Solidity compiler undergoes frequent updates and modifications, resulting in continuous evolution of bytecode patterns. To address this challenge, we propose using deep learning models to analyze Ethereum bytecodes and infer the compiler version that produced them. A large number of Ethereum contracts and the corresponding compiler versions is …
Path-Safe :Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan
Path-Safe :Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan
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Deploying Mandatory Access Controls (MAC) is a popular way to provide host protection against malware. Unfortunately, current implementations lack the flexibility to adapt to emergent malware threats and are known for being difficult to configure. A core tenet of MAC security systems is that the policies they are deployed with are immutable from the host while they are active. This work looks at deploying a MAC system that leverages using encrypted security tokens to allow for redeploying policy configurations in real-time without the need to stop a running process. This is instrumental in developing an adaptive framework for security systems …
The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii
The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii
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The widespread expansion of Electric Vehicles (EV) throughout the world creates a requirement for charging stations. While Cybersecurity research is rapidly expanding in the field of Electric Vehicle Infrastructure, efforts are impacted by the availability of testing platforms. This paper presents a solution called the “Open Charge Point Protocol (OCPP) Cyber Range.” Its purpose is to conduct Cybersecurity research against vulnerabilities in the OCPP v1.6 protocol. The OCPP Cyber Range can be used to enable current or future research and to train operators and system managers of Electric Charge Vehicle Supply Equipment (EVSE). This paper demonstrates this solution using three …
Contributors To Pathologic Depolarization In Myotonia Congenita, Jessica Hope Myers
Contributors To Pathologic Depolarization In Myotonia Congenita, Jessica Hope Myers
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Myotonia congenita is an inherited skeletal muscle disorder caused by loss-of-function mutation in the CLCN1 gene. This gene encodes the ClC-1 chloride channel, which is almost exclusively expressed in skeletal muscle where it acts to stabilize the resting membrane potential. Loss of this chloride channel leads to skeletal muscle hyperexcitability, resulting in involuntary muscle action potentials (myotonic discharges) seen clinically as muscle stiffness (myotonia). Stiffness affects the limb and facial muscles, though specific muscle involvement can vary between patients. Interestingly, respiratory distress is not part of this disease despite muscles of respiration such as the diaphragm muscle also carrying this …
Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty
Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty
Electrical & Computer Engineering Faculty Publications
There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. …