Design of secure and robust cognitive system for malware detection

Sanket Shukla

RECORD

Design of secure and robust cognitive system for malware detection

Sanket Shukla

Cryptography and Security · Submitted 3 Aug 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system.

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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What the abstract itself states, as probabilities: claims first 0.15 · states limitation 0.13 · human participants 0.02 · controlled comparison 0.15 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.18 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples. Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples. There exists majority of the software based adversarial attacks and defenses. To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data. This can cause an additional overhead and can prolong the real-time malware detection. So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries. Therefore, the aim of this thesis is to address the above mentioned critical system security issues. The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system. First, a novel technique to detect stealthy malware is proposed. The technique uses malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted. Secondly, I demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. I also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images.	background 1.00	0.05	0.02	asserted 0.98
s1	Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples.	background 0.85	0.05	0.03	asserted 0.57
s2	Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples.	background 1.00	0.05	0.02	asserted 1.00
s3	There exists majority of the software based adversarial attacks and defenses.	background 0.95	0.05	0.02	asserted 0.99
s4	To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data.	background 0.78	0.06	0.03	asserted 0.97
s5	This can cause an additional overhead and can prolong the real-time malware detection.	background 0.69	0.08	0.02	hedged possibility 0.74
s6	So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries.	contribution 0.76	0.30	0.06	asserted 0.43
s7	Therefore, the aim of this thesis is to address the above mentioned critical system security issues.	contribution 0.74	0.12	0.05	asserted 0.89
s8	The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system.	contribution 0.98	0.63	0.15	asserted 1.00
s9	First, a novel technique to detect stealthy malware is proposed.	contribution 1.00	0.88	0.07	asserted 0.96
s10	The technique uses malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained.	method 0.95	0.86	0.04	asserted 0.99
s11	Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted.	result 0.99	0.90	0.15	asserted 1.00
s12	Secondly, I demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics.	contribution 0.81	0.92	0.05	asserted 1.00
s13	I also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.	contribution 0.96	0.88	0.22	asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.54.

What was examined, and what was not

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arXiv listing abstract v1

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full text
figures and tables
references and citing sentences
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples. Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples. There exists majority of the software based adversarial attacks and defenses. To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data. This can cause an additional overhead and can prolong the real-time malware detection. So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries. Therefore, the aim of this thesis is to address the above mentioned critical system security issues. The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system. First, a novel technique to detect stealthy malware is proposed. The technique uses malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted. Secondly, I demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. I also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 8 sentences without a confident label

Uses the method or tool of 1 paper

The adversarial perturbation generated with FGSM [ref] is mathematically given as

Explaining and Harnessing Adversarial Examples · Fast Gradient Sign Method (FGSM) · 0.86

Cites as background 3 papers

Adversarial training is one of the preliminary solutions for making the ML classifiers robust against the adversarial examples, proposed in [ref] .

Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization · Adversarial Training · 0.86
Poisoning attacks are attacks on the ML classifier during the training phase [ref] , and the evasive attacks are targeted for inference stage of machine learning techniques.

Poisoning Attacks against Support Vector Machines · Need for RRAM-Neuromorphic Architecture based Defense Against Adversarial Attacks · 0.88
Fast Gradient Sign method (FGSM) [ref] is one of the first known adversarial attacks.

Explaining and Harnessing Adversarial Examples · Fast Gradient Sign Method (FGSM) · 0.87

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