Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Sewak, Mohit"'
Although large language models (LLMs) have advanced the state-of-the-art in NLP significantly, deploying them for downstream applications is still challenging due to cost, responsiveness, control, or concerns around privacy and security. As such, tra
Externí odkaz:
http://arxiv.org/abs/2310.20111
Publikováno v:
International Conference On Secure Knowledge Management In Artificial Intelligence Era. Springer, Cham, 2021
The cybersecurity threat landscape has lately become overly complex. Threat actors leverage weaknesses in the network and endpoint security in a very coordinated manner to perpetuate sophisticated attacks that could bring down the entire network and
Externí odkaz:
http://arxiv.org/abs/2206.02733
Publikováno v:
2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1-9
Advanced metamorphic malware and ransomware, by using obfuscation, could alter their internal structure with every attack. If such malware could intrude even into any of the IoT networks, then even if the original malware instance gets detected, by t
Externí odkaz:
http://arxiv.org/abs/2109.11542
Publikováno v:
2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1-9
Long-Short-Term-Memory (LSTM) networks have shown great promise in artificial intelligence (AI) based language modeling. Recently, LSTM networks have also become popular for designing AI-based Intrusion Detection Systems (IDS). However, its applicabi
Externí odkaz:
http://arxiv.org/abs/2109.11500
Publikováno v:
2021 IEEE 46th Conference on Local Computer Networks (LCN), 2021, pp. 581-588
Supervised Deep Learning requires plenty of labeled data to converge, and hence perform optimally for task-specific learning. Therefore, we propose a novel mechanism named DRo (for Deep Routing) for data-scarce domains like security. The DRo approach
Externí odkaz:
http://arxiv.org/abs/2109.05470
Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out of every five business/industry mobile application leaks sensitive personal data. Traditional
Externí odkaz:
http://arxiv.org/abs/2103.00643
Today anti-malware community is facing challenges due to the ever-increasing sophistication and volume of malware attacks developed by adversaries. Traditional malware detection mechanisms are not able to cope-up with next-generation malware attacks.
Externí odkaz:
http://arxiv.org/abs/2103.00637
Publikováno v:
Defence Science Journal, 71(1), 55-65
In this paper, we propose a novel mechanism to normalize metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation and defense system. We name this system DRLDO, for Deep Reinforcement
Externí odkaz:
http://arxiv.org/abs/2102.00898