=Paper=
{{Paper
|id=Vol-2786/Paper9
|storemode=property
|title=Securing Intelligent Autonomous Systems Through Artificial Intelligence
|pdfUrl=https://ceur-ws.org/Vol-2786/Paper9.pdf
|volume=Vol-2786
|authors=Ganpathi Mani,Bharat Bhargava,Jason Kobes,Justin King,James MacDonald
|dblpUrl=https://dblp.org/rec/conf/isic2/ManiBKKM21
}}
==Securing Intelligent Autonomous Systems Through Artificial Intelligence==
https://ceur-ws.org/Vol-2786/Paper9.pdf
67
Securing Intelligent Autonomous Systems Through Artificial
Intelligence
Ganapathy Mania, Bharat Bhargavaa, Jason Kobesb, Justin Kingb , James MacDonaldb
a
Purdue University, West Lafayette, Indiana, USA
b
Northrop Grumman Corporation, McLean, Virginia, USA
Abstract
Intelligent Autonomous Systems (IAS) reconstruct their perception through adaptive learning
and meet mission objectives. IAS are highly cognitive, rich in knowledge discovery, reflective
through rapid adaptation, and provide security assurance. It is paramount to have effective
reasoning, decision-making, and understanding of operational context since IAS are exposed
to advanced multi-stage attacks during training and inference time. Advanced malware types
such as file-less malware with benign initial execution phase can mislead IAS to accept them
as normal processes and execute malicious code later. IAS are also exposed to adaptive
poisoning attacks where adversary inputs malicious data into training/testing set to manipulate
the learning. Hence it is vital to monitor IAS activities/interactions to conduct forensics. This
project will advance science of security in IAS through multifaceted advanced analytics,
cognitive and adversarial machine learning, and cyber attribution based on the following
approaches.
(a) Implement deep learning-based application profiling to categorize adaptive cyber-
attacks and poison attacks on machine learning models using contextual information
about the origin, trust, and transformation of data.
(b) Using HW/OS/SW data to develop perception algorithms using LSTM deep neural
networks for detecting malware/anomalies and classifying dynamic attack contexts.
(c) Facilitate cyber attribution for forensics through privacy-preserving provenance
structure for knowledge representation and perform intrusion detection sampling on
HW /OS/SW data.
(d) Employ advanced data analytics to aid ontological and semantic reasoning models to
enhance decision-making, attack adaptiveness, and self-healing.
Keywords 1
autonomy, machine learning, deep learning, cybersecurity, lstm
International Semantic Intelligence Conference (ISIC 2021), Feb
25-27, 2021, New Delhi, India
EMAIL: bbshail@purdue.edu (A. 2);
©️ 2020 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
68
1. Solution Overview Intelligent autonomous systems receive
large amounts of diverse data from various
Our focus is on constraints, barriers and data sources. In addition, they operate in a
challenges such as poorly understood attack dynamic operational context and interact
surfaces, data set training availability and with numerous entities such as other TAS,
biases, processing latency, human UAVs, satellites, sensors, cloud systems,
understanding of AI results, AI/ML analysts, malicious actors, and
countermeasures, human-machine disparity, compromised systems.
measurement of effects. We propose novel Cyber attribution module constitutes a
approaches for privacy-preserving cyber stream data processor where data streams
attribution, intrusion detection, adversarial are labeled / tagged on-the-fly for better
machine learning, malware/anomaly detection, knowledge representation and
reasoning, and decision-making. Cyber categorization. This data is stored as
attribution involves extracting software, monitored or provenance data with its
hardware, and operating system data to origin and historical information. For
perform intrusion detection sampling (fixed or preserving privacy, detailed provenance
dynamic sampling), generating efficient data is reduced in its scope to include only
provenance structure that is populated with necessary data for a particular analysis or
specific data required for a particular analysis learning. This module uses Provenance
or learning, and labeling and tagging to Ontology (PROV-O) structure (elaborated
properly represent the information obtained. in a later section) to obscure unnecessary
The processed data is distributed to the or privacy-compromising data.
cognitive module where the data is checked Furthermore, the attribution model
for any malicious data presence through monitors data generated by software
poison attack filter. The filtered data is (application parameters), hardware
transmitted to cognitive computing module (memory bytes and instructions), and
and knowledge discovery module, where the operating system (system calls). This data
data is fed into supervised, unsupervised, and is used to conduct periodic sampling to
LSTM models to perform learning and identify signatures of intrusion activities.
advanced analytics. Based on multifaceted Once the data is processed, it goes through
dimensions of data analytics, reasoning and adversarial machine learning model.
decision-making ability of IAS are enhanced. Attackers can insert malicious data into
The overall architecture of the proposed training and testing dataset to influence
model-secure intelligent autonomous systems machine learning models. In order to
with cyber attribution-is demonstrated in isolate poisonous data, poison data filter
figure 1. performs methods such as classification of
verified and unverified data as well as
outlier extraction. Once the poisonous data
is removed the data (raw or provenance
data) is sent to Cognitive computing
module and Knowledge discovery module.
In Cognitive computing module, depends
on the data and efficiency of machine
learning methods, malware / anomaly
detection is performed through either deep
learning methodologies such as Long
short-term memory (LSTM) e.g. Recurrent
Neural Networks (RNN) or Convolutional
Figure 1: Comprehensive Architecture of Neural Networks (CNN) or light-weight
Secure Intelligent Autonomous Systems with yet powerful machine learning methods
Cyber such as Support Vector Machines (SVM),
General characteristics of the proposed unified Random Forests (RF), and K-Nearest
architecture are given as follows: Neighbors (KNN). In addition, cognitive
computing module consists of reasoning
69
engine, which is driven by rule sets, reversing the error correction coding technique
semantic, and ontological reasoning. Both known as Golay coding [4][8]. The scheme
anomaly detection module and reasoning utilizes 223 number of binary vectors of size
engine module influence the attack 23 bits to profile features and cluster the data
adaptiveness (reflexivity) and self-healing items. Since the method is built based on error
of IAS, where decisions obtained through correction scheme, it exhibits fault tolerance in
reasoning and learning are turned into wrongly labeled data. Similarly, we perform
actions. With this extensive cognitive privacy-preserving knowledge discovery
computing modules, the final response through perturbed aggregation in untrusted
from IAS to other interacting entities will cloud [5]. In this project, we will use advanced
be a secure and trusted one. data analytics to enable reasoning module for
Knowledge discovery module facilitates assisting attack adaptation and reflexivity of
multi-faceted dimensions of advanced data the system.
analytics including regression analysis,
supervised learning, unsupervised 3. Cognitive Autonomy for
learning, and pattern-recognition.
Discovered knowledge is shared with Cybersecurity in Autonomous
cognitive computing module for further Systems
learning. The proposed structure provides
robust cyber resilience and autonomous Decentralized machine learning is a promising
operation of the system. emerging paradigm in view of global
challenges of data ownership and privacy. We
consider learning of linear classification and
2. Background on Cognitive regression models, in the setting where the
training data is decentralized over many user
Autonomy devices, and the learning algorithm must run
on device, on an arbitrary communication
Cognitive computing is a vital part of network, without a central coordinator. We
security in autonomous systems. In particular, plan to utilize and advance COLA, a new
malware and anomaly detection has become a decentralized training algorithm [23] with
biggest challenge with increase in strong theoretical guarantees and superior
sophistication in attacks such as file-less practical performance. This framework
malware [1] and ransomware [2]. Behavior- overcomes many limitations of existing
based malware detection system (pBMDS) methods, and achieves communication
was proposed in [3]. The technique observes efficiency, scalability, elasticity as well as
unique behaviors of applications as well as resilience to changes in data and participating
users and leverages Hidden Markov Model devices. We will consider fault tolerance to
(HMM) to learn application and user behaviors dropped and oscillation of nodes from
based on two features: process state transitions connected to disconnected and attacks on the
and user operational patterns. One of the nodes. The learning has to be communication-
drawbacks of the HMM model is that it has efficient decentralized framework and free of
very limited memory thus cannot be used for parameter tuning. COLA offers full adaptively
sequential data. In this project, we leverage to heterogeneous distributed systems on
hardware, software, and operating system data arbitrary network topologies and is adaptive to
and apply long short-term memory units to changes in network size and data and offers
identify anomalous behavior. We will also fault tolerance and elasticity. IAS should have
profile applications and malware using HW clear understanding of its operational context,
data (memory bytes and instruction sequences) it's won processes, and its interactions with
to whitelist benign processes and blacklist neighboring entities. In this project, the
malicious processes. In order to enable better cognitive computing module consists of three
results for LSTM deep learning major components: (1) Malware / anomaly
methodologies, knowledge discovery and detection module, (2) Reasoning engine, and (
representation are important. We proposed a 4) Reflexivity engine. Cyber attribution data
metadata labeling scheme, BFC, for (system monitoring data or provenance data) is
information tagging and clustering by
70
sent to cognitive computing engine for
analysis where the system profiles the
applications based on machine learning
models. In this paper, we will focus on the
cognitive autonomy property of the
autonomous systems.
4. Malware and Anomalous
Application Behavior Profiling Figure 3: Malware/anomaly Detection with
Light-weight Machine Learning Methods
with Deep Learning Model:
Advanced malware such as ransomware
encrypts IAS data without authorization. Since
it does not alter the system configurations and
leave a footprint, it is difficult to detect them.
But based on the executed instruction
sequences and constants (also known as magic
constants) used for encryption mechanism
during malware execution, applications can be
profiled. First, we will sample the address
spots for every 1,000,000 instructions (fixed
Figure 2: Recurrent Neural Network (RNN) sampling). After a fixed period of time, we
model for application behavior profiling will calculate the frequently occurring
addresses and their relevant process ids. A
We use instruction sequences executed in threshold T will be set for data extraction. For
memory by application to understand the example, extract memory bytes and
behavior of each application. instructions from top T = 10% of the global
Input: n-gram sequences of instructions from list of sampled addresses (sorted in descending
memory order based on their frequency of occurrence).
Output: Binary classification of benign or Once opcode and memory bytes data is
malicious collected, we will extract features such as n-
Step 1: Define a finite set I of instructions gram, bigram, unigram features, magic
{i1, i2, ..., in} in the system. Instructions are constants feature, cosine similarity with
executed based on time epochs i.e., time- instructions occurrences, and standard
series data. deviation. Cosine similarity metric is one of
Step 2: Given an observed sequence of {i1, the most efficient method to learn from large
i2, ..., in}, we find the set N of the top P datasets [20]. It plays a crucial role in
sequences to be executed at time t. The understanding similarity between two feature
size of the set N varies in each prediction vectors when the magnitude of the vector is
and is determined by n-grams input as well large or unspecified
as the clusters in the output of the model. i.e., it can either be unigram, bigram, or n-
Step 3: At time t, the sequence {i1, i2, ..., gram features. Given two feature vectors Vi =
in} is benign if i1 is in P, otherwise {f11, f12, ...} and Vi = {f21, f22, ...}, where f11,
malicious. f21, . . .are values of a particular feature, the
cosine similarity is given as,
Algorithm 1: Application Behavioral
Profiling Algorithm
5. Malware and Anomaly
Detection with Light-weight The cosine similarity lies between O and 1. If
the orientation of the two feature vectors is the
Machine Learning Models: same then the similarity between them is Cos
O = 1 i.e., there is zero angle between them.
71
But when the angle is 90° (the orientation of [3] Xie, Liang, Xinwen Zhang, Jean-Pierre
the feature vectors is at an angle of 90) then Seifert, and Sencun Zhu. "pBMDS: a
the behavior-based malware detection system
similarity is Cos 90 = 0. The similarity score for cellphone devices." In Proceedings of
varies between [O, ½). Once the features are the third A CM conference on Wireless
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KNN learning models. K-NN is one of the [4] Mani, Ganapathy, Bharat Bhargava, and
simplest yet powerful classifier with high Jason Kobes. "Scalable Deep Learning
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[6]. Autonomous Systems." In IEEE
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6. Conclusion Intelligence and Knowledge Engineering
(AI.KE), pp. IEEE. 2018.
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Measures Effect on the Performance of
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