DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkAndroid malware has continued to grow in volume and complexity posing significant threats to the security of mobile devices and the services they enable. This has prompted increasing interest in employing machine learning to improve Android malware detection. In this paper, we present a novel classifier fusion approach based on a multilevel architecture that enables effective combination of machine learning algorithms for improved accuracy. The framework (called DroidFusion), generates a model by training base classifiers at a lower level and then applies a set of ranking-based algorithms on their predictive accuracies at the higher level in order to derive a final classifier. The induced multilevel DroidFusion model can then be utilized as an improved accuracy predictor for Android malware detection. We present experimental results on four separate datasets to demonstrate the effectiveness of our proposed approach. Furthermore, we demonstrate that the DroidFusion method can also effectively enable the fusion of ensemble learning algorithms for improved accuracy. Finally, we show that the prediction accuracy of DroidFusion, despite only utilizing a computational approach in the higher level, can outperform stacked generalization, a well-known classifier fusion method that employs a meta-classifier approach in its higher level

HMCMA: Design of an Efficient Model with Hybrid Machine Learning in Cyber security for Enhanced Detection of Malicious Activities

In the rapidly evolving landscape of cyber security, the incessant advancement of malicious activities presents a formidable challenge, necessitating a paradigm shift in detection methodologies. Traditional methods, primarily reliant on static rule-based systems, exhibit palpable limitations in grappling with the dynamic and sophisticated nature of modern cyber threats. This inadequacy underscores the urgent need for innovative approaches that can adeptly adapt and respond to the ever-changing threat environment. Addressing this exigency, the present research introduces a novel hybrid machine learning model, ingeniously crafted to transcend the constraints of existing malicious activity detection frameworks. The proposed model synergizes the strengths of diverse machine learning strategies, including anomaly detection techniques including Isolation Forest and One-Class SVM, and validates the results of these classifiers using Random Forest and Gradient Boosting operations. The validated malware instances are classified into malware types using fusion of Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) based Recurrent Neural Networks (RNNs) under real-time network configuration sets. This eclectic amalgamation not only leverages the unique capabilities of each algorithm but also harmonizes them to forge a more robust and precise detection mechanisms. The strategic integration of these algorithms facilitates a comprehensive analysis of network traffic and system logs, thereby significantly enhancing the detection accuracy. Furthermore, the model's adaptive learning component ensures its relevance and efficacy in the face of evolving cyber threats, a quintessential feature for contemporary cyber security solutions. Empirical evaluations, conducted using multiple malware datasets and samples, substantiate the model's superiority over existing methods. It exhibited a remarkable 10.4% improvement in precision, an 8.5% increase in accuracy, a 4.9% enhancement in recall, an 8.3% rise in AUC, a 4.5% boost in specificity, and a notable 2.5% reduction in detection delay. These compelling results underscore the model's potential in revolutionizing malicious activity detection, providing organizations with a more effective and resilient defense mechanism against a spectrum of cyber threats. The research culminates in a significant stride forward in cyber security, offering a robust, adaptive, and comprehensive solution that addresses the pressing need for advanced malicious activity detection, thereby bolstering the overall cyber security posture of organizations in the digital age sets

Multimodal Approach for Malware Detection

Although malware detection is a very active area of research, few works were focused on using physical properties (e.g., power consumption) and multimodal features for malware detection. We designed an experimental testbed that allowed us to run samples of malware and non-malicious software applications and to collect power consumption, network traffic, and system logs data, and subsequently to extract dynamic behavioral-based features. We also extracted code-based static features of both malware and non-malicious software applications. These features were used for malware detection based on: feature level fusion using power consumption and network traffic data, feature level fusion using network traffic data and system logs, and multimodal feature level and decision level fusion. The contributions when using feature level fusion of power consumption and network traffic data are: (1) We focused on detecting real malware using the extracted dynamic behavioral features (both power-based and network traffic-based) and supervised machine learning algorithms, which has not been done by any of the prior works. (2) We ran a large number of machine learning experiments, which allowed us to identify the best performing learner, DC voltage rails that led to the best malware detection performance, and the subset of features that are the best predictors for malware detection. (3) The comparison of malware detection performance was done using a comprehensive set of metrics that reflect different aspects of the quality of malware detection. In the case of the feature level fusion using network traffic data and system logs, the contributions are: (1) Most of the previous works that have used network flows-based features have done classification of the network traffic, while our focus was on classifying the software running in a machine as malware and non-malicious software using the extracted dynamic behavioral features. (2) We experimented with different sizes of the training set (i.e., 90%, 75%, 50%, and 25% of the data) and found that smaller training sets produced very good classification results. This aspect of our work has a practical value because the manual labeling of the training set is a tedious and time consuming process. In this dissertation we present a multimodal deep learning neural network that integrates different modalities (i.e., power consumption, system logs, network traffic, and code-based static data) using decision level fusion. We evaluated the performance of each modality individually, when using feature level fusion, and when using decision level fusion. The contributions of our multimodal approach are as follow: (1) Collecting data from different modalities allowed us to develop a multimodal approach to malware detection, which has not been widely explored by prior works. Even more, none of the previous works compared the performance of feature level fusion with decision level fusion, which is explored in this dissertation. (2) We proposed a multimodal decision level fusion malware detection approach using a deep neural network and compared its performance with the performance of feature level fusion approaches based on deep neural network and standard supervised machine learning algorithms (i.e., Random Forest, J48, JRip, PART, Naive Bayes, and SMO)

EKMPRFG: Ensemble of KNN, Multilayer Perceptron and Random Forest using Grading for Android Malware Classification

Android is the most popular Operating Systems with over 2.5 billion devices across the globe. The popularity of this OS has unfortunately made the devices and the services they enable, vulnerable to numerous security threats. As a result of this, a significant research is being done in the field of Android Malware Detection employing Machine Learning Algorithms. Our current work emphasizes on the possible use of Machine Learning techniques for the detection of malware on such android devices. The proposed EKMPRFG is applied for the classification of Android Malware after a preprocessing phase involving a hybrid Feature Selection model using proposed Standard Deviation of Standard Deviation of Ranks (SDSDR) and several other builtin Feature Selection algorithms such as Correlation based Feature Selection (CFS), Classifier SubsetEval, Consistency SubsetEval, and Filtered SubsetEval followed by Principal Component Analysis(PCA) for dimensionality reduction. The experimental results obtained on two data sets indicate that EKMPRFG outperforms the existing works in terms of Prediction Accuracy and Weighted F- Measure values

Artificial Intelligence and Machine Learning in Cybersecurity: Applications, Challenges, and Opportunities for MIS Academics

The availability of massive amounts of data, fast computers, and superior machine learning (ML) algorithms has spurred interest in artificial intelligence (AI). It is no surprise, then, that we observe an increase in the application of AI in cybersecurity. Our survey of AI applications in cybersecurity shows most of the present applications are in the areas of malware identification and classification, intrusion detection, and cybercrime prevention. We should, however, be aware that AI-enabled cybersecurity is not without its drawbacks. Challenges to AI solutions include a shortage of good quality data to train machine learning models, the potential for exploits via adversarial AI/ML, and limited human expertise in AI. However, the rewards in terms of increased accuracy of cyberattack predictions, faster response to cyberattacks, and improved cybersecurity make it worthwhile to overcome these challenges. We present a summary of the current research on the application of AI and ML to improve cybersecurity, challenges that need to be overcome, and research opportunities for academics in management information systems

Neural malware detection

At the heart of today’s malware problem lies theoretically infinite diversity created by metamorphism. The majority of conventional machine learning techniques tackle the problem with the assumptions that a sufficiently large number of training samples exist and that the training set is independent and identically distributed. However, the lack of semantic features combined with the models under these wrong assumptions result largely in overfitting with many false positives against real world samples, resulting in systems being left vulnerable to various adversarial attacks. A key observation is that modern malware authors write a script that automatically generates an arbitrarily large number of diverse samples that share similar characteristics in program logic, which is a very cost-effective way to evade detection with minimum effort. Given that many malware campaigns follow this paradigm of economic malware manufacturing model, the samples within a campaign are likely to share coherent semantic characteristics. This opens up a possibility of one-to-many detection. Therefore, it is crucial to capture this non-linear metamorphic pattern unique to the campaign in order to detect these seemingly diverse but identically rooted variants. To address these issues, this dissertation proposes novel deep learning models, including generative static malware outbreak detection model, generative dynamic malware detection model using spatio-temporal isomorphic dynamic features, and instruction cognitive malware detection. A comparative study on metamorphic threats is also conducted as part of the thesis. Generative adversarial autoencoder (AAE) over convolutional network with global average pooling is introduced as a fundamental deep learning framework for malware detection, which captures highly complex non-linear metamorphism through translation invariancy and local variation insensitivity. Generative Adversarial Network (GAN) used as a part of the framework enables oneshot training where semantically isomorphic malware campaigns are identified by a single malware instance sampled from the very initial outbreak. This is a major innovation because, to the best of our knowledge, no approach has been found to this challenging training objective against the malware distribution that consists of a large number of very sparse groups artificially driven by arms race between attackers and defenders. In addition, we propose a novel method that extracts instruction cognitive representation from uninterpreted raw binary executables, which can be used for oneto- many malware detection via one-shot training against frequency spectrum of the Transformer’s encoded latent representation. The method works regardless of the presence of diverse malware variations while remaining resilient to adversarial attacks that mostly use random perturbation against raw binaries. Comprehensive performance analyses including mathematical formulations and experimental evaluations are provided, with the proposed deep learning framework for malware detection exhibiting a superior performance over conventional machine learning methods. The methods proposed in this thesis are applicable to a variety of threat environments here artificially formed sparse distributions arise at the cyber battle fronts.Doctor of Philosoph

Protecting Android Devices from Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges

Advancements in microelectronics have increased the popularity of mobile devices like cellphones, tablets, e-readers, and PDAs. Android, with its open-source platform, broad device support, customizability, and integration with the Google ecosystem, has become the leading operating system for mobile devices. While Android's openness brings benefits, it has downsides like a lack of official support, fragmentation, complexity, and security risks if not maintained. Malware exploits these vulnerabilities for unauthorized actions and data theft. To enhance device security, static and dynamic analysis techniques can be employed. However, current attackers are becoming increasingly sophisticated, and they are employing packaging, code obfuscation, and encryption techniques to evade detection models. Researchers prefer flexible artificial intelligence methods, particularly deep learning models, for detecting and classifying malware on Android systems. In this survey study, a detailed literature review was conducted to investigate and analyze how deep learning approaches have been applied to malware detection on Android systems. The study also provides an overview of the Android architecture, datasets used for deep learning-based detection, and open issues that will be studied in the future

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security

Detecting Abnormal Social Robot Behavior through Emotion Recognition

Sharing characteristics with both the Internet of Things and the Cyber Physical Systems categories, a new type of device has arrived to claim a third category and raise its very own privacy concerns. Social robots are in the market asking consumers to become part of their daily routine and interactions. Ranging in the level and method of communication with the users, all social robots are able to collect, share and analyze a great variety and large volume of personal data.In this thesis, we focus the community’s attention to this emerging area of interest for privacy and security research. We discuss the likely privacy issues, comment on current defense mechanisms that are applicable to this new category of devices, outline new forms of attack that are made possible through social robots, highlight paths that research on consumer perceptions could follow, and propose a system for detecting abnormal social robot behavior based on emotion detection