Zobrazeno 1 - 10
of 1 835
pro vyhledávání: '"RAHMAN, ABDUL"'
Autor:
Rahman, Abdul, Upadhye, Neelesh
In high frequency trading, accurate prediction of Order Flow Imbalance (OFI) is crucial for understanding market dynamics and maintaining liquidity. This paper introduces a hybrid predictive model that combines Vector Auto Regression (VAR) with a sim
Externí odkaz:
http://arxiv.org/abs/2411.08382
Autor:
Ahamed, Sayyed Farid, Banerjee, Soumya, Roy, Sandip, Quinn, Devin, Vucovich, Marc, Choi, Kevin, Rahman, Abdul, Hu, Alison, Bowen, Edward, Shetty, Sachin
Over the last few years, federated learning (FL) has emerged as a prominent method in machine learning, emphasizing privacy preservation by allowing multiple clients to collaboratively build a model while keeping their training data private. Despite
Externí odkaz:
http://arxiv.org/abs/2407.19119
Autor:
Wang, Cheng, Redino, Christopher, Clark, Ryan, Rahman, Abdul, Aguinaga, Sal, Murli, Sathvik, Nandakumar, Dhruv, Rao, Roland, Huang, Lanxiao, Radke, Daniel, Bowen, Edward
Ransomware presents a significant and increasing threat to individuals and organizations by encrypting their systems and not releasing them until a large fee has been extracted. To bolster preparedness against potential attacks, organizations commonl
Externí odkaz:
http://arxiv.org/abs/2406.17576
Autor:
Rahman, Abdul
This paper investigates the potential of Bayesian optimization (BO) to optimize the atr multiplier and atr period -the parameters of the Supertrend indicator for maximizing trading profits across diverse stock datasets. By employing BO, the thesis ai
Externí odkaz:
http://arxiv.org/abs/2405.14262
Autor:
Kulkarni, Ajay, Wang, Yingjie, Gopinath, Munisamy, Sobien, Dan, Rahman, Abdul, Batarseh, Feras A.
The increasing utilization of emerging technologies in the Food & Agriculture (FA) sector has heightened the need for security to minimize cyber risks. Considering this aspect, this manuscript reviews disclosed and documented cybersecurity incidents
Externí odkaz:
http://arxiv.org/abs/2403.08036
Autor:
Wang, Cheng, Redino, Christopher, Rahman, Abdul, Clark, Ryan, Radke, Daniel, Cody, Tyler, Nandakumar, Dhruv, Bowen, Edward
Command and control (C2) channels are an essential component of many types of cyber attacks, as they enable attackers to remotely control their malware-infected machines and execute harmful actions, such as propagating malicious code across networks,
Externí odkaz:
http://arxiv.org/abs/2402.09200
Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. The main goal of a federated learning architecture is to protect the privacy of each client while still contributing
Externí odkaz:
http://arxiv.org/abs/2312.04587
Publikováno v:
Arab Gulf Journal of Scientific Research, 2023, Vol. 42, Issue 3, pp. 621-635.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/AGJSR-10-2022-0218
Autor:
Banerjee, Soumya, Roy, Sandip, Ahamed, Sayyed Farid, Quinn, Devin, Vucovich, Marc, Nandakumar, Dhruv, Choi, Kevin, Rahman, Abdul, Bowen, Edward, Shetty, Sachin
The membership inference attack (MIA) is a popular paradigm for compromising the privacy of a machine learning (ML) model. MIA exploits the natural inclination of ML models to overfit upon the training data. MIAs are trained to distinguish between tr
Externí odkaz:
http://arxiv.org/abs/2312.00051
Autor:
Rishu, Riddam, Kakkar, Akshay, Wang, Cheng, Rahman, Abdul, Redino, Christopher, Nandakumar, Dhruv, Cody, Tyler, Clark, Ryan, Radke, Daniel, Bowen, Edward
Building on previous work using reinforcement learning (RL) focused on identification of exfiltration paths, this work expands the methodology to include protocol and payload considerations. The former approach to exfiltration path discovery, where r
Externí odkaz:
http://arxiv.org/abs/2310.03667