A method for estimating process maliciousness with Seq2Seq model

Tobiyama, Shun; Yamaguchi, Yukiko; Hasegawa, Hirokazu; Shimada, Hiroyuki; Akiyama, Mitoshi; Yagi, T.

doi:10.1109/icoin.2018.8343120

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…This makes CNN models robust to input translations [22]. Pooling layers reduce computational burdens by reducing count of connections between convolution layers [23]. Mathematically speaking each CNN layer has a set of kernels which convolve input data.…”

Section: Fig 3 -Tc-cnn Null Value Checksmentioning

confidence: 99%

TC-CNN: A Tree Classified model using AI for identifying malware intrusions in open Networks

Ramachandran¹

2021

pae

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Proliferation of the internet by multiple devices has led to dramatic increases in network traﬃc. The Internet medium has also been growing with this usage, but this fast growth has also resulted in new threats making networks vulnerable to intruders and attackers or malicious users. This has made network security an important factor due to excessive usage of ICT (Information and Communications Technology) as threats to IVTs has also grown manifold. Securing data is a major issue, especially when they are transmitted across open networks. IDSs (Intrusion Detection Systems) are methods or techniques or algorithm which cater to detection of intrusions while on transit. IDSs are useful in identifying harmful operations. Secure automated threat detection and prevention is a more effective procedure to reduce workloads of monitors by scanning the network, server functions and inform monitors on suspicious activity. IDSs monitor systems continually in the angle of threat. This paper’s proposed technique detects suspicious activities using AI (Artificial Intelligence) and analyzes networks concurrently for defense from harmful activities. The proposed algorithm’s experimental results conducted on the UNSW_NB15_training-set shows good performances in terms of accuracy clocking above 96%.

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Section: Fig 3 -Tc-cnn Null Value Checksmentioning

confidence: 99%

TC-CNN: A Tree Classified model using AI for identifying malware intrusions in open Networks

Ramachandran¹

2021

pae

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“…Anomaly detection tasks in medical field such as medical image analysis and clinical electroencephalography (EEG) records had great successes with the aid of deep learning techniques in preventing treatments for different medical conditions. These architectures produced outstanding performance using techniques such as Auto-Encoder(AE) [25], Convolutional Neural Network(CNN) [26], Deep Neural Network(DNN) [27], Deep Belief Network(DBN) [28], Long-Short Term Memory(LSTM) [29], Generative Adversarial Network(GAN) [30], and Recurrent Neural Network(RNN) [31].…”

Section: Medical Anomaly Detectionmentioning

confidence: 99%

AESMOTE: Adversarial Reinforcement Learning with SMOTE for Anomaly Detection

Ma¹

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Intrusion Detection Systems (IDSs) play a vital role in securing today's Data-Centric Networks. In a dynamic environment that is vulnerable to various types of attacks, novel, fast, and robust solutions are in demand to handle fast changing threats and thus the ever-increasing difficulty of detection. In this dissertation, we present a novel reinforcement learning based anomaly detection algorithm that further enables anomaly-based intrusion detection. As anomaly detection frameworks are mostly supervised learning based, which seeks the advantage of stable predictions and good performance with pre-recorded datasets, we have further explored the performance of a combined framework of joining a reinforcement learning algorithm with classimbalance techniques. The motivation of this approach is to not only exploit the auto-learning ability from the reinforcement learning loop, but also correct the classimbalance problem, which is pervasive in existing solutions. Our proposed solution is developed based on AE-RL [1]. We further introduce an adapted SMOTE to address the class-imbalance problem while remodel the behaviors of the environmental agent for better performance. Experiments are conducted using NSL-KDD [2] datasets.Comparative evaluation and their results are presented and analyzed. Using techniques such as SMOTE, ROS, NearMiss1 and NearMiss2, performance measures obtained from our simulations have led us to recognize specific performance trends.The proposed model AESMOTE outperforms the original AE-RL in several cases.Experiment results show an Accuracy greater than 0.82 and F1 greater than 0.824.

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A method for estimating process maliciousness with Seq2Seq model

Cited by 2 publications

References 2 publications

TC-CNN: A Tree Classified model using AI for identifying malware intrusions in open Networks

TC-CNN: A Tree Classified model using AI for identifying malware intrusions in open Networks

AESMOTE: Adversarial Reinforcement Learning with SMOTE for Anomaly Detection

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