Transformers have demonstrated dermatologistlevel performance in skin lesion classification. However, their clinical adoption remains limited owing to the lack of transparency, interpretability, and clinician trust in visual explanations. Current Class Activation Mapping (CAM) techniques often generate coarse saliency maps, overlook patient-specific contexts, and ignore model uncertainty, which are critical limitations in clinical settings. This study proposes a meta-aware multiscale focused CAM (MSF-CAM), a novel post-hoc interpretability framework that integrates (i) multi-scale attention fusion for sharper localization, (ii) metadata-guided modulation for context-aware explanations, and (iii) Monte Carlo dropout for uncertainty-aware saliency filtering. The MSF-CAM was evaluated on the ISIC 2018, ISIC 2019, and PAD-UFES-20 datasets, and it consistently outperformed seven leading CAM methods, including Grad-CAM, GradCAM++, XGradCAM, FinerCAM, LayerCAM, Transformer-CAM, and ViT-ReciproCAM, across localization metrics (IoU, precision, and recall) and a novel uncertainty-aligned Trust Score. MSF-CAM is model-agnostic and compatible with ViT/Swin architectures, offering a scalable and trustworthy XAI solution for dermatology.

Advanced Encryption Standard (AES) is one of the most widely used cipher for data encryption. However, recent studies have pointed out some weaknesses in the subkeys used in AES. Also, the sequential subkeys generation process of AES seems to be inefficient for resource-constrained IoT devices. Therefore, in this paper, we studied how secure and efficient the Secure Hash Algorithm-3 (SHA3) generated subkeys will be for the encryption of IoT data by just replacing the AES subkeys with it. For this purpose we used the statistical test suite provided by the National Institute of Standards and Technology and found that SHA-3 generated subkeys are highly random, and also it is significantly efficient for encrypting the IoT Data. However, as symmetric keys are repeatedly used, therefore, only upto 2 MB data, the encryption is significantly (∼ 1300 times) efficient. From the experimental analysis, we find that SEASHA3 generates subkeys that are random, non-linear, and irreversible, making it more secure and efficient than traditional AES subkey generation.

The Android operating system has constantly remained in the limelight, hence attracts the attention of cyber-criminals. Understanding the rising challenges, many researchers have bagged achievements by applying machine/deep learning techniques for the construction of malware detection models based on popular Drebin malware datasets. However, a cursory look at a table of the frequency of Dalvik opcodes leads us to believe that this dataset may have a massive number of duplicate malicious files. Hence, we used a technique called fitting factor to find the duplicate malicious files in the Drebin datasets on the basis of opcodes occurrence. We found that 51.57% malicious samples in the datasets have one or more duplicates. Hence, accordingly, we studied the performance of the popular detection models with and without duplicates with all the features, top 26 features engineered by Information Gain (IG) and Auto-Encoder (AE). The experimental results show that one of the most popular classical classifiers, the Random Forest classifier, shows a decline in accuracy by 4.2%, 5.3% and 8.8% with all features, top 26 features obtained by IG and AE respectively. To establish the observed facts we further extensively experimented with Decision Tree, Bagging, Gradient Boost, XG Boost, and Deep Neural Network. The most significant decline (12.2%) in accuracy was observed in the Deep Neural Network classifier with the features obtained by IG, i.e., the earlier reported performance of the malware detection models based on Drebin data is exaggerated, and consequently it may lead to a wrong direction in this field of research.

Android malware has grown manifold in the last decade and has become a threat to the ecosystem. Recently published literature suggests that the data mining based models have shown promising results in android malware detection. However, adversaries can identify vulnerabilities in these detection models using adversarial learning. Adversaries can further exploit these vulnerabilities by performing adversarial attacks on these detection models and reduce their performance. Therefore in this work, we first put ourselves in the adversary's shoes and propose an evasion attack to find vulnerabilities in various malware detection models using reinforcement learning. The attack aims to add perturbation(s) in malware samples to generate adversarial samples such that they are forcefully misclassified as benign samples by the malware detection models. The attack is designed for a limited knowledge scenario where an adversary does not know the learning algorithm used to build detection models, which is similar to a real-world scenario. We validated the proposed adversarial attack against ten malware detection models constructed using different learning algorithms. Our proposed attack with limited knowledge and capabilities accomplish more than 58% forced misclassification rate against all ten detection models. We also identified ten vulnerable intents that an adversary could exploit to decrease the performance of malware detection models. Lastly, we propose a defense against evasion attacks to increase the adversarial robustness of all malware detection models.

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 an 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 work on LSTM hyperparameters for IDS and experimented with 150 LSTM configurations to determine its hyper-parameters' relative-importance, interaction-effects, and optimal selection-approach for designing an IDS. We conducted multiple analysis of the results of these experiments and empirically controlled for the interaction effects of different hyper-parameters covariate level. 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 interaction effect plays a crucial role in determining the relative importance of hyperparameters. We also discovered that after controlling for the interaction-effect, the correct relative importance for LSTMs for an IDS are batch-size, followed-by dropout ratio and padding. The findings are significant because when LSTM were first used for language models, the focus had mostly been on increasing the number of layers to enhance performance.

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 network, then even if the original malware instance get detected, by that time it can still infect the entire network. The IoT era also required Industry 4.0 grade AI based defense against such advanced malware. But AI algorithm need a lot of training data, and 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) deep reinforcement learning to obfuscate malware at the opcode level and create multiple metamorphic instances of the same. To the best of our knowledge, 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. is also the first-ever system to use efficient continuous action control capable deep reinforcement learning agents like the Proximal Policy Optimization in the area of cyber security. Experimental results indicate that could raise the metamorphic probability of a corpus of malware by ≥ 0.45. Additionally, more than 33% of metamorphic instances generated by were able to evade even the most potent IDS and penetrate the target system, even when the defending IDS could detect the original malware instance. Hence could be used to generate data representative of a swarm of very potent and coordinated AI based metamorphic malware attack. 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.

The popularity of android mobile phones has increased manifolds in the last few years, which has attracted many malware developers. Researchers have proposed several new-age malware detection models using machine and deep learning algorithms to strengthen the current detection engines. However, we found that these models are adversarially vulnerable, which will jeopardize their adoption in the security ecosystem. We proposed a framework where we first stepped into the attacker's shoes to design a correlation-based evasion attack and tested it against four different malware detection models. The attack exploited vulnerabilities and drastically reduced the performance of all four detection models. Later we proposed adversarial retraining as the defense strategy to counter the attacks and improve the adversarial robustness of android malware detection models.

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.

The tremendous increase of malicious applications in the android ecosystem has prompted researchers to explore deep learning based malware detection models. However, research in other domains suggests that deep learning models are adversarially vulnerable, and thus we aim to investigate the robustness of deep learning based malware detection models. We first developed two image-based E-CNN malware detection models based on android permission and intent. We then acted as an adversary and designed the ECO-FGSM evasion attack against the above models, which achieved more than 50% fooling rate with limited perturbations. The evasion attack converts maximum malware samples into adversarial samples while minimizing the perturbations and maintaining the sample's syntactical, functional, and behavioral integrity. Later, we used adversarial retraining to counter the evasion attack and develop adversarially superior malware detection models, which should be an essential step before any real-world deployment.

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.

Android-based smartphones and IoT devices have grown at an exponential rate in the last decade. Meanwhile, malicious applications have also increased dramatically, which threaten the Android ecosystem. The anti-malware community has proposed data mining based malware detection models which have shown encouraging results. However, these detection models are vulnerable to adversarial attacks. In this work, we first acted as an adversary and performed adversarial attacks on eight different malware detection models. We found all the eight detection models vulnerable to adversarial attacks and fooling rate of more than 90% was achieved against each of them. We also propose defence against these attacks by adversarial retraining and accomplish encouraging results to improve the overall robustness of malware detection models.

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 a 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 layers, 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 outperforms their non-recurrent counterparts. Therefore, we also assess how sequential DL architectures like the LSTM compares against their non-sequential counterparts like the MLP-DNN for the purpose of malware-detection.

We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Encoders, and Deep Neural Networks with varying layers over Malicia malware dataset on which earlier studies have obtained an accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these results were done using extensive man-made custom domain features and investing corresponding feature engineering and design efforts. In our proposed approach, besides improving the previous best results (99.21% accuracy and an False Positive Rate of 0.19%) indicates that Deep Learning based systems could deliver an effective defense against malware. Since it is good in automatically extracting higher conceptual features from the data, Deep Learning based systems could provide an effective, general and scalable mechanism for detection of existing and unknown malware.

Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc. Although, in general, it is computationally more expensive as compared to classical machine learning techniques, their results are found to be more effective in some cases. Therefore, in this paper, we investigated and compared one of the Deep Learning Architecture called Deep Neural Network (DNN) with the classical Random Forest (RF) machine learning algorithm for the malware classification. We studied the performance of the classical RF and DNN with 2, 4 & 7 layers architectures with the four different feature sets, and found that irrespective of the features inputs, the classical RF accuracy outperforms the DNN.

The World Wide Web serves as a huge repository of information that is highly dynamic, diverse and growing at an exponential rate in a lightening speed. In order to speed-up and further improve tasks like information search and retrieval, personalization etc; it is highly important to develop techniques to classify text documents more accurately and efficiently than before. This paper is an effort in that direction, where the effectiveness of Extreme Learning Machines(ELM) in the domain of text classification is studied and compared with many of the existing relevant techniques like Support Vector Machines(SVM), which are currently one of the most popular and effective techniques for classifying text documents. Ours is one of the few works that highlight the high performance of ELM in the field of text classification, by implementing classifiers based on different interpretations of ELM, analyzing their performance, and studying which feature selection techniques are most suited to improve their accuracy. In our multi-class classification problem, we studied a single ELM classifier based on the one-against-all scheme, and a multi-layer ELM classifier inspired from deep networks, and then perform extensive experiments on different datasets to demonstrate the applicability and effectiveness of our approach. Results show that ELM based classifiers can outperform many of the traditional classification techniques including the most powerful state-of-the-art technique such as SVM.