Bypassing system calls-based intrusion detection systems

2016 ◽  
Vol 29 (16) ◽  
pp. e4023 ◽  
Author(s):  
Ishai Rosenberg ◽  
Ehud Gudes
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
System Calls

scholarly journals Methods for Host-Based Intrusion Detection with Deep Learning

2021 ◽  
Author(s):  
John Ring ◽  
Colin Van Oort ◽  
Samson Durst ◽  
Vanessa White ◽  
Joseph Near ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Black Box ◽  
Intrusion Detection Systems ◽  
System Behavior ◽  
Detection Systems ◽  
System Calls ◽  
Baseline System ◽  
Sequence Behavior ◽  
Learning Architectures

Host-based Intrusion Detection Systems (HIDS) automatically detect events that indicate compromise by adversarial applications. HIDS are generally formulated as analyses of sequences of system events such as bash commands or system calls. Anomaly-based approaches to HIDS leverage models of normal (aka baseline) system behavior to detect and report abnormal events, and have the advantage of being able to detect novel attacks. In this paper we develop a new method for anomaly-based HIDS using deep learning predictions of sequence-to-sequence behavior in system calls. Our proposed method, called the ALAD algorithm, aggregates predictions at the application level to detect anomalies. We investigate the use of several deep learning architectures, including WaveNet and several recurrent networks. We show that ALAD empowered with deep learning significantly outperforms previous approaches. We train and evaluate our models using an existing dataset, ADFA-LD, and a new dataset of our own construction, PLAID. As deep learning models are black box in nature we use an alternate approach, allotaxonographs, to characterize and understand differences in baseline vs.~attack sequences in HIDS datasets such as PLAID.

Disadvantages of Modern Intrusion Detection Systems

2006 ◽  
Vol 65 (10) ◽  
pp. 929-936
Author(s):  
A. V. Agranovskiy ◽  
S. A. Repalov ◽  
R. A. Khadi ◽  
M. B. Yakubets
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems

APPLYING OF NEURAL NETWORKS IN INTRUSION DETECTION SYSTEMS

2020 ◽  
Author(s):  
I. D. Popov ◽  
◽  
V. V. Komashinsky ◽  
M. N. Isaeva ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems

Intrusion Detection Systems-Enabled Power Electronics for Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Mohammad Ashiqur Rahman ◽  
Md Tauhidur Rahman ◽  
Mithat Kisacikoglu ◽  
Kemal Akkaya
Keyword(s):  
Intrusion Detection ◽  
Unmanned Aerial Vehicles ◽  
Power Electronics ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Aerial Vehicles

scholarly journals A Review of Rule Learning Based Intrusion Detection Systems and Their Prospects in Smart Grids

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qi Liu ◽  
Veit Hagenmeyer ◽  
Hubert B. Keller
Keyword(s):  
Intrusion Detection ◽  
Smart Grids ◽  
Rule Learning ◽  
Intrusion Detection Systems ◽  
Detection Systems
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