arXiv subjects

Sanjay K. Sahay

Publications and source records attributed to Sanjay K. Sahay.

At least 19 recordsLinked to original sources

arXiv2025

AESHA3: Efficient and Secure Sub-Key Generation for AES Using SHA-3

Advanced Encryption Standard (AES) is one of the most widely used symmetric cipher for the confidentiality of data. Also it is used for other security services, viz. integrity, authentication and key establishment. However, recently, authors have shown some weakness in the generation of sub-keys in AES, e.g. bit leakage attack, etc. Also, AES sub-keys are generated sequentially, which is an overhead, especially for resource-constrained devices. Therefore, we propose and investigate a novel encryption AESHA3, which uses sub-keys generated by Secure Hash Algorithm-3 (SHA3). The output of SHA3 is one-way and highly non-linear, and random. The experimental analysis shows that the average time taken for generating the sub-keys to be used for encrypting the data using our approach i.e. AESHA3 is ~1300 times faster than the sub-key generated by the standard AES. Accordingly, we find that AESHA3 will be very relevant not only in terms of security but also it will save the resources in IoT devices. We investigated AESHA3 in Intel Core i7, 6th Generation processor and Raspberry Pi 4B and found that up to two MB data encryption is very significant, and lesser the data size, more the resource saving compared to AES.

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arXiv2025

Modern Hardware Security: A Review of Attacks and Countermeasures

With the exponential rise in the use of cloud services, smart devices, and IoT devices, advanced cyber attacks have become increasingly sophisticated and ubiquitous. Furthermore, the rapid evolution of computing architectures and memory technologies has created an urgent need to understand and address hardware security vulnerabilities. In this paper, we review the current state of vulnerabilities and mitigation strategies in contemporary computing systems. We discuss cache side-channel attacks (including Spectre and Meltdown), power side-channel attacks (such as Simple Power Analysis, Differential Power Analysis, Correlation Power Analysis, and Template Attacks), and advanced techniques like Voltage Glitching and Electromagnetic Analysis to help understand and build robust cybersecurity defense systems and guide further research. We also examine memory encryption, focusing on confidentiality, granularity, key management, masking, and re-keying strategies. Additionally, we cover Cryptographic Instruction Set Architectures, Secure Boot, Root of Trust mechanisms, Physical Unclonable Functions, and hardware fault injection techniques. The paper concludes with an analysis of the RISC-V architecture's unique security challenges. The comprehensive analysis presented in this paper is essential for building resilient hardware security solutions that can protect against both current and emerging threats in an increasingly challenging security landscape.

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arXiv2023

Gravitational wave: generation and detection techniques

In this paper, we review the theoretical basis for generation of gravitational waves and the detection techniques used to detect a gravitational wave. To materialize this goal in a thorough way we first start with a mathematical background for general relativity from which a clue for gravitational wave was conceived by Einstein. Thereafter we give the classification scheme of gravitational waves such as (i) continuous gravitational waves, (ii) compact binary inspiral gravitational waves and (iii) stochastic gravitational wave. Necessary mathematical insight into gravitational waves from binaries are also dealt with which follows detection of gravitational waves based on the frequency classification. Ground based observatories as well as space borne gravitational wave detectors are discussed in a length. We have provided an overview on the inflationary gravitational waves. In connection to data analysis by matched filtering there are a few highlights on the techniques, e.g. (i) Random noise, (ii) power spectrum, (iii) shot noise, and (iv) Gaussian noise. Optimal detection statistics for a gravitational wave detection is also in the pipeline of the discussion along with detailed necessity of the matched filter and deep learning.

gr-qc↗

arXiv2022

Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review

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 many critical hosts in the network. Increasingly advanced deep and machine learning-based solutions have been used in threat detection and protection. The application of these techniques has been reviewed well in the scientific literature. Deep Reinforcement Learning has shown great promise in developing AI-based solutions for areas that had earlier required advanced human cognizance. Different techniques and algorithms under deep reinforcement learning have shown great promise in applications ranging from games to industrial processes, where it is claimed to augment systems with general AI capabilities. These algorithms have recently also been used in cybersecurity, especially in threat detection and endpoint protection, where these are showing state-of-the-art results. Unlike supervised machines and deep learning, deep reinforcement learning is used in more diverse ways and is empowering many innovative applications in the threat defense landscape. However, there does not exist any comprehensive review of these unique applications and accomplishments. Therefore, in this paper, we intend to fill this gap and provide a comprehensive review of the different applications of deep reinforcement learning in cybersecurity threat detection and protection.

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arXiv2021

Privacy-Preserving Mutual Authentication and Key Agreement Scheme for Multi-Server Healthcare System

The usage of different technologies and smart devices helps people to get medical services remotely for multiple benefits. Thus, critical and sensitive data is exchanged between a user and a doctor. When health data is transmitted over a common channel, it becomes essential to preserve various privacy and security properties in the system. Further, the number of users for remote services is increasing day-by-day exponentially, and thus, it is not adequate to deal with all users using the one server due to the verification overhead, server failure, and scalability issues. Thus, researchers proposed various authentication protocols for multi-server architecture, but most of them are vulnerable to different security attacks and require high computational resources during the implementation. To Tackle privacy and security issues using less computational resources, we propose a privacy-preserving mutual authentication and key agreement protocol for a multi-server healthcare system. We discuss the proposed scheme's security analysis and performance results to understand its security strengths and the computational resource requirement, respectively. Further, we do the comparison of security and performance results with recent relevant authentication protocols.

cs.CR↗

arXiv2021

LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach

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 applicability in IDS is studied largely in the default settings as used in language models. Whereas security applications offer distinct conditions and hence warrant careful consideration while applying such recurrent networks. Therefore, we conducted one of the most exhaustive works on LSTM hyper-parameters for IDS and experimented with approx. 150 LSTM configurations to determine its hyper-parameters relative importance, interaction effects, and optimal selection approach for designing an IDS. We conducted multiple analyses of the results of these experiments and empirically controlled for the interaction effects of different hyper-parameters covariate levels. We found that for security applications, especially for designing an IDS, neither similar relative importance as applicable to language models is valid, nor is the standard linear method for hyper-parameter selection ideal. We ascertained that the interaction effect plays a crucial role in determining the relative importance of hyper-parameters. We also discovered that after controlling for the interaction effect, the correct relative importance for LSTMs for an IDS is batch-size, followed by dropout ratio and padding. The findings are significant because when LSTM was first used for language models, the focus had mostly been on increasing the number of layers to enhance performance.

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arXiv2021

ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator

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 that time it can still infect the entire network. It is challenging to obtain training data for such evasive malware. Therefore, in this paper, we present ADVERSARIALuscator, a novel system that uses specialized Adversarial-DRL to obfuscate malware at the opcode level and create multiple metamorphic instances of the same. To the best of our knowledge, ADVERSARIALuscator is the first-ever system that adopts the Markov Decision Process-based approach to convert and find a solution to the problem of creating individual obfuscations at the opcode level. This is important as the machine language level is the least at which functionality could be preserved so as to mimic an actual attack effectively. ADVERSARIALuscator is also the first-ever system to use efficient continuous action control capable of deep reinforcement learning agents like the Proximal Policy Optimization in the area of cyber security. Experimental results indicate that ADVERSARIALuscator could raise the metamorphic probability of a corpus of malware by >0.45. Additionally, more than 33% of metamorphic instances generated by ADVERSARIALuscator were able to evade the most potent IDS. If such malware could intrude even into any of the IoT networks, then even if the original malware instance gets detected, by that time it can still infect the entire network. Hence ADVERSARIALuscator could be used to generate data representative of a swarm of very potent and coordinated AI-based metamorphic malware attacks. The so generated data and simulations could be used to bolster the defenses of an IDS against an actual AI-based metamorphic attack from advanced malware and ransomware.

cs.CR↗

arXiv2021

DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems

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 builds upon some of the recent developments in Deep-Clustering. In particular, it exploits the self-augmented training mechanism using synthetically generated local perturbations. DRo not only allays the challenges with sparse-labeled data but also offers many unique advantages. We also developed a system named DRoID that uses the DRo mechanism for enhancing the performance of an existing Malware Detection System that uses (low information features like the) Android implicit Intent(s) as the only features. We conduct experiments on DRoID using a popular and standardized Android malware dataset and found that the DRo mechanism could successfully reduce the false-alarms generated by the downstream classifier by 67.9%, and also simultaneously boosts its accuracy by 11.3%. This is significant not only because the gains achieved are unparalleled but also because the features used were never considered rich enough to train a classifier on; and hence no decent performance could ever be reported by any malware classification system till-date using these features in isolation. Owing to the results achieved, the DRo mechanism claims a dominant position amongst all known systems that aims to enhance the classification performance of deep learning models with sparse-labeled data.

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arXiv2021

Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering

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. Therefore in this paper, we propose effective and efficient Android malware detection models based on machine learning and deep learning integrated with clustering. We performed a comprehensive study of different feature reduction, classification and clustering algorithms over various performance metrics to construct the Android malware detection models. Our experimental results show that malware detection models developed using Random Forest eclipsed deep neural network and other classifiers on the majority of performance metrics. The baseline Random Forest model without any feature reduction achieved the highest AUC of 99.4%. Also, the segregating of vector space using clustering integrated with Random Forest further boosted the AUC to 99.6% in one cluster and direct detection of Android malware in another cluster, thus reducing the curse of dimensionality. Additionally, we found that feature reduction in detection models does improve the model efficiency (training and testing time) many folds without much penalty on the effectiveness of the detection model.

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arXiv2021

Identification of Significant Permissions for Efficient Android Malware Detection

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 signature/heuristic-based malware detection systems are unable to cope up with current malware challenges and thus threaten the Android ecosystem. Therefore recently researchers have started exploring machine learning and deep learning based malware detection systems. In this paper, we performed a comprehensive feature analysis to identify the significant Android permissions and propose an efficient Android malware detection system using machine learning and deep neural network. We constructed a set of $16$ permissions ($8\%$ of the total set) derived from variance threshold, auto-encoders, and principal component analysis to build a malware detection engine that consumes less train and test time without significant compromise on the model accuracy. Our experimental results show that the Android malware detection model based on the random forest classifier is most balanced and achieves the highest area under curve score of $97.7\%$, which is better than the current state-of-art systems. We also observed that deep neural networks attain comparable accuracy to the baseline results but with a massive computational penalty.

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arXiv2021

DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware

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 Learning based De-Obfuscator. With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against 'zero-day' attacks from obfuscated and metamorphic variants of existing malware. This gains importance, not only because there exists no system to date that uses advanced DRL to intelligently and automatically normalize obfuscation down even to the opcode level, but also because the DRLDO system does not mandate any changes to the existing IDS. The DRLDO system does not even mandate the IDS' classifier to be retrained with any new dataset containing obfuscated samples. Hence DRLDO could be easily retrofitted into any existing IDS deployment. We designed, developed, and conducted experiments on the system to evaluate the same against multiple-simultaneous attacks from obfuscations generated from malware samples from a standardized dataset that contains multiple generations of malware. Experimental results prove that DRLDO was able to successfully make the otherwise un-detectable obfuscated variants of the malware detectable by an existing pre-trained malware classifier. The detection probability was raised well above the cut-off mark to 0.6 for the classifier to detect the obfuscated malware unambiguously. Further, the de-obfuscated variants generated by DRLDO achieved a very high correlation (of 0.99) with the base malware. This observation validates that the DRLDO system is actually learning to de-obfuscate and not exploiting a trivial trick.

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arXiv2021

Robust Android Malware Detection System against Adversarial Attacks using Q-Learning

The current state-of-the-art Android malware detection systems are based on machine learning and deep learning models. Despite having superior performance, these models are susceptible to adversarial attacks. Therefore in this paper, we developed eight Android malware detection models based on machine learning and deep neural network and investigated their robustness against adversarial attacks. For this purpose, we created new variants of malware using Reinforcement Learning, which will be misclassified as benign by the existing Android malware detection models. We propose two novel attack strategies, namely single policy attack and multiple policy attack using reinforcement learning for white-box and grey-box scenario respectively. Putting ourselves in the adversary's shoes, we designed adversarial attacks on the detection models with the goal of maximizing fooling rate, while making minimum modifications to the Android application and ensuring that the app's functionality and behavior do not change. We achieved an average fooling rate of 44.21% and 53.20% across all the eight detection models with a maximum of five modifications using a single policy attack and multiple policy attack, respectively. The highest fooling rate of 86.09% with five changes was attained against the decision tree-based model using the multiple policy approach. Finally, we propose an adversarial defense strategy that reduces the average fooling rate by threefold to 15.22% against a single policy attack, thereby increasing the robustness of the detection models i.e. the proposed model can effectively detect variants (metamorphic) of malware. The experimental analysis shows that our proposed Android malware detection system using reinforcement learning is more robust against adversarial attacks.

cs.CR↗

arXiv2020

Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS

Recurrent deep learning language models like the LSTM are often used to provide advanced cyber-defense for high-value assets. The underlying assumption for using LSTM networks for malware-detection is that the op-code sequence of malware could be treated as a (spoken) language representation. There are differences between any spoken-language (sequence of words/sentences) and the machine-language (sequence of op-codes). In this paper, we demonstrate that due to these inherent differences, an LSTM model with its default configuration as tuned for a spoken-language, may not work well to detect malware (using its op-code sequence) unless the network's essential hyper-parameters are tuned appropriately. In the process, we also determine the relative importance of all the different hyper-parameters of an LSTM network as applied to malware detection using their op-code sequence representations. We experimented with different configurations of LSTM networks, and altered hyper-parameters like the embedding-size, number of hidden layers, number of LSTM-units in a hidden layer, pruning/padding-length of the input-vector, activation-function, and batch-size. We discovered that owing to the enhanced complexity of the malware/machine-language, the performance of an LSTM network configured for an Intrusion Detection System, is very sensitive towards the number-of-hidden-layers, input sequence-length, and the choice of the activation-function. Also, for (spoken) language-modeling, the recurrent architectures by-far outperform their non-recurrent counterparts. Therefore, we also assess how sequential DL architectures like the LSTM compare against their non-sequential counterparts like the MLP-DNN for the purpose of malware-detection.

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arXiv2020

DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture

The Intent in Android plays an important role in inter-process and intra-process communications. The implicit Intent that an application could accept are declared in its manifest and are amongst the easiest feature to extract from an apk. Implicit Intents could even be extracted online and in real-time. So far neither the feasibility of developing an Intrusion Detection System solely on implicit Intent has been explored, nor are any benchmarks available of a malware classifier that is based on implicit Intent alone. We demonstrate that despite Intent is implicit and well declared, it can provide very intuitive insights to distinguish malicious from non-malicious applications. We conducted exhaustive experiments with over 40 different end-to-end Deep Learning configurations of Auto-Encoders and Multi-Layer-Perceptron to create a benchmark for a malware classifier that works exclusively on implicit Intent. Using the results from the experiments we create an intrusion detection system using only the implicit Intents and end-to-end Deep Learning architecture. We obtained an area-under-curve statistic of 0.81, and accuracy of 77.2% along with false-positive-rate of 0.11 on Drebin dataset.

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arXiv2020

DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS

We designed and developed DOOM (Adversarial-DRL based Opcode level Obfuscator to generate Metamorphic malware), a novel system that uses adversarial deep reinforcement learning to obfuscate malware at the op-code level for the enhancement of IDS. The ultimate goal of DOOM is not to give a potent weapon in the hands of cyber-attackers, but to create defensive-mechanisms against advanced zero-day attacks. Experimental results indicate that the obfuscated malware created by DOOM could effectively mimic multiple-simultaneous zero-day attacks. To the best of our knowledge, DOOM is the first system that could generate obfuscated malware detailed to individual op-code level. DOOM is also the first-ever system to use efficient continuous action control based deep reinforcement learning in the area of malware generation and defense. Experimental results indicate that over 67% of the metamorphic malware generated by DOOM could easily evade detection from even the most potent IDS. This achievement gains significance, as with this, even IDS augment with advanced routing sub-system can be easily evaded by the malware generated by DOOM.

cs.CR↗

arXiv2020

Secure and Energy-Efficient Key-Agreement Protocol for Multi-Server Architecture

Authentication schemes are practised globally to verify the legitimacy of users and servers for the exchange of data in different facilities. Generally, the server verifies a user to provide resources for different purposes. But due to the large network system, the authentication process has become complex and therefore, time-to-time different authentication protocols have been proposed for the multi-server architecture. However, most of the protocols are vulnerable to various security attacks and their performance is not efficient. In this paper, we propose a secure and energy-efficient remote user authentication protocol for multi-server systems. The results show that the proposed protocol is comparatively ~44% more efficient and needs ~38% less communication cost. We also demonstrate that with only two-factor authentication, the proposed protocol is more secure from the earlier related authentication schemes.

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arXiv2020

Secure Communication Protocol for Smart Transportation Based on Vehicular Cloud

The pioneering concept of connected vehicles has transformed the way of thinking for researchers and entrepreneurs by collecting relevant data from nearby objects. However, this data is useful for a specific vehicle only. Moreover, vehicles get a high amount of data (e.g., traffic, safety, and multimedia infotainment) on the road. Thus, vehicles expect adequate storage devices for this data, but it is infeasible to have a large memory in each vehicle. Hence, the vehicular cloud computing (VCC) framework came into the picture to provide a storage facility by connecting a road-side-unit (RSU) with the vehicular cloud (VC). In this, data should be saved in an encrypted form to preserve security, but there is a challenge to search for information over encrypted data. Next, we understand that many of vehicular communication schemes are inefficient for data transmissions due to its poor performance results and vulnerable to different fundamental security attacks. Accordingly, on-device performance is critical, but data damages and secure on-time connectivity are also significant challenges in a public environment. Therefore, we propose reliable data transmission protocols for cutting-edge architecture to search data from the storage, to resist against various security attacks, and provide better performance results. Thus, the proposed data transmission protocol is useful in diverse smart city applications (business, safety, and entertainment) for the benefits of society.

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arXiv2019

An Efficient Detection of Malware by Naive Bayes Classifier Using GPGPU

Due to continuous increase in the number of malware (according to AV-Test institute total ~8 x 10^8 malware are already known, and every day they register ~2.5 x 10^4 malware) and files in the computational devices, it is very important to design a system which not only effectively but can also efficiently detect the new or previously unseen malware to prevent/minimize the damages. Therefore, this paper presents a novel group-wise approach for the efficient detection of malware by parallelizing the classification using the power of GPGPU and shown that by using the Naive Bayes classifier the detection speed-up can be boosted up to 200x. The investigation also shows that the classification time increases significantly with the number of features.

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