Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks

Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the training process of detection systems. In this research, we performed two adversarial attack scenarios, we used a Generative Adversarial Network (GAN) to generate synthetic intrusion traffic to test the influence of these attacks on the accuracy of machine learning-based Intrusion Detection Systems(IDSs). We conducted two experiments on adversarial attacks including poisoning and evasion attacks on two different types of machine learning models: Decision Tree and Logistic Regression. The performance of implemented adversarial attack scenarios was evaluated using the CICIDS2017 dataset. Also, it was based on a comparison of the accuracy of machine learning-based IDS before and after attacks. The results show that the proposed evasion attacks reduced the testing accuracy of both network intrusion detection systems models (NIDS). That illustrates our evasion attack scenario negatively affected the accuracy of machine learning-based network intrusion detection systems, whereas the decision tree model was more affected than logistic regression. Furthermore, our poisoning attack scenario disrupted the training process of machine learning-based NIDS, whereas the logistic regression model was more affected than the decision tree.

Investigating the use of moving target defense (MTD) mechanisms in IoT networks is ongoing research, with unfathomable potential to equip IoT devices and networks with the ability to fend off cyber attacks despite the computational deficiencies many IoT ecosystems typically have. The AI community has extensively studied adversarial threats and attacks on machine learning-based systems, emphasizing the need to address the potential compromise of anomaly-based intrusion detection systems (IDS) through adversarial attacks. Another concept that has gained significant attention in the networking community is Game Theory. Protecting any given network is almost a never-ending battle between the attacker and defender, and hence a natural game of competitors can be modelled based on one’s parametric specifications to gain more insight into how attackers might interact with one’s system. The goal of this thesis is to propose a comprehensive, experimentally verifiable game-theoretic model of MTD in IoT networks to secure the IDS against adversarial attacks. Once a game with state transitions based on given actions can be modelled, reinforcement learning is used to develop policies based on various episodes (rounds) of the game, ultimately optimizing network decisions to minimize successful attacks on machine learning-based IDS. The state-of-the-art ToN-IoT dataset was investigated for MTD feasibility to implement the feature-based MTD approach. The overall performance of the proposed MTD-based IDS was compared to a conventional IDS by analyzing the accuracy curve of the MTD-based IDS and the conventional IDS for varying attacker success rates and resource demands. Our approach has proven effective in securing the IDS against adversarial learning.

With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework. Additionally, we describe existing countermeasures for enhancing IoT security against adversarial attacks. Finally, we explore the most recent literature on the vulnerability of three ML-based IoT security systems to adversarial attacks.

Advanced EGAN-IDS Framework for Resilience against Adversarial Attacks using Multi-headed Attention Module

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)—a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic—we conclude the paper with a summary of the lessons learned from this experience.

Machine Learning-based Intrusion Detection Systems have been proven to be very effective in the protection of IoT Networks. However, the expansion of Adversarial Machine Learning attacks threatens their efficacy affecting also the security of IoT networks. Thus, this paper proposes a Machine Learning-driven methodology for multiclass classification of cyber-attacks in IoT networks and investigates the robustness of the Machine and Deep Learning classifiers against several well-known Adversarial Machine Learning attacks (JSMA, FGSM, DeepFool). Moreover, the effectiveness of the Adversarial Training defense method has been studied in tackling Adversarial Machine Learning attacks. The proposed methodology was evaluated using a new and large IoT dataset (IoTID20) and the experimental results concluded that the Random Forest classifier can classify the cyber-attacks with high classification accuracy (99.9%) as well as the JSMA, FGSM, and DeepFool attacks can significantly reduce the performance of all the classifiers. Finally, based on the evaluation adversarial training can overall enhance the classifiers’ robustness against all the utilized Adversarial Machine Learning attacks without affecting the performance when only normal samples are present.

In recent years, significant advancements have been made in cyber security, particularly through the application of machine learning (ML) methods. ML-based techniques have enhanced system security by effectively distinguishing between malicious and benign objects across various domains, including spam email detection, social media content filtering, intrusion detection systems, and malware detection. This paper focuses on the specific area of ML-based malware detection and its advantages over traditional methods, such as improved accuracy and the ability to generalize to unknown threats. Despite these advancements, ML-based malware detection systems are vulnerable to adversarial attacks, especially in black-box scenarios where the internal workings of the detection model are not accessible. This paper provides a comprehensive review of adversarial attacks on black-box malware detection systems and examines the current defense mechanisms against these attacks. Understanding the challenges and strategies in this field, this review aims to offer insights into enhancing the robustness and security of ML-based malware detection systems.

Modern digital interaction is made complete with QR codes, which facilitate quick and easy entry to a variety of services and information. Their adoption in finance, marketing, logistics, and healthcare attests to their versatility as well as the risks they pose in the current digital environment. Among these threats, one of the most significant is QR phishing, wherein malicious users avail themselves of both the natural trust and the apparent ease of usage of QR codes to lure victims into giving up sensitive information, visiting counterfeit websites, or downloading malware. This research will investigate vulnerabilities in QR code systems, explore techniques used in carrying out QR phishing, and analyze actual case scenarios to show the effect of such threats. Besides, it presents sophisticated mitigations with the performance analysis, including cryptographic analysis, blockchain-based authentication, and machine learning-based detection systems with the performance analysis to improve the security of QR code applications to maintain the user trust.

Adversarial attacks aim to deceive the target system. Recently, deep learning methods have become a target of adversarial attacks. Even small perturbations could lead to classification errors in deep learning models. In an intrusion detection system using deep learning method, adversarial attack can cause classification error and malicious traffic can be classified as benign. In this study, the effects of adversarial attacks on the accuracy of deep learning-based intrusion detection systems were examined. CICIDS2017 dataset was used to test the detection systems . At first, DDoS attacks weredetected using Autoencoder, MLP, AEMLP, DNN, AEDNN, CNN and AECNN methods. Then, the Fast Gradient Sign Method (FGSM) is used to perform adversarial attacks. Finally, the sensitivity of the methods against the adversarial attacks were examined. Our results showed that the classification performance of deep learning based detection methods decreased up to %17 after the adversarial attacks. The results obtained in this study form the basis for the validation and validation studies of learning-based intrusion detection systems.

Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e.g., autonomous navigation and continuous robot arm control.) However, the learning-based systems and the associated models may be threatened by the risks of intentionally adaptive (e.g., noisy sensor confusion) and adversarial perturbations from real-world scenarios. In this paper, we introduce timing-based adversarial strategies against a DRL-based navigation system by jamming in physical noise patterns on the selected time frames. To study the vulnerability of learning-based navigation systems, we propose two adversarial agent models: one refers to online learning; another one is based on evolutionary learning. Besides, three open-source robot learning and navigation control environments are employed to study the vulnerability under adversarial timing attacks. Our experimental results show that the adversarial timing attacks can lead to a significant performance drop, and also suggest the necessity of enhancing the robustness of robot learning systems.

Rapid digitalization of essential national infrastructure has created new vulnerabilities to cyber threats, leading to major security threats against the nation. The current security measures prove inadequate for keeping pace with developing cyberattacks, so artificial intelligence needs integration for threat detection enhancements and response improvements. The combination of AI-enabled cybersecurity systems gives them the ability to examine huge data collections instantly, monitor irregularities and perform automatic threat response functions to improve national security. Research investigates the system of artificial intelligence to enhance cyber threat response capabilities alongside its specific use for defending crucial infrastructure elements like energy networks as well as financial organizations and government IT infrastructure. Methodology The study combines qualitative approaches with quantitative methods as its research methodology. The analysis includes a structured review of existing frameworks that use AI for cybersecurity purposes with their performance evaluation. The paper evaluates real-world AI deployments across critical infrastructure systems through case studies to reveal successful strategies with encountered problems. The empirical proof of machine learning-based intrusion detection systems is carried out by testing IDS along with real-world dataset assessment to verify AI's threat mitigation effectiveness through the accuracy and precision & recall method. Security experts who perform interviews deliver valuable information about the current use of AI technology in national security applications. The national cybersecurity capabilities gain strength from AI-driven systems because these systems accomplish improved threat detection and swift responses without requiring human involvement. AI deliver its maximum effectiveness only when data privacy issues with adversarial AI attacks and regulatory hurdles, receive proper solutions,

Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems

The last decade witnessed an exponential rise of Android smartphones and malware attacks on them. Researchers have investigated and proposed many promising malware detection models based on machine learning. However, these malware detection models are susceptible to adversarial attacks which threaten the Android security ecosystem. In this article, we propose to develop malware detection models which are more robust against adversarial attacks. We first designed an i-bit adversarial attack policy which achieved an average fooling rate of 51% with maximum ten modifications across twelve different malware detection models. Later we also propose an adversarial defence mechanism which enhanced the robustness of the malware detection models by reducing the fooling rate to one-third against the same adversarial attack.

Intrusion detection systems are used to detect attacks in a network. Machine learning (ML) approaches have been widely used to build such Intrusion Detection Systems (IDSs) because they are more accurate when built from a very large and representative dataset. In recent times, one of the benchmark datasets that are used to build ML-based Intrusion Detection models is CICIDS2017 dataset. The dataset is contained in eight groups and was collected from the Canadian Institute on Cyber Security dataset repository. The dataset is available in both PCAP and netflow formats. This study used the netflow records in the CIDIDS2017 dataset as they are found to contain newer attacks,very large and are found useful for traffic analysis. Exploratory Data Analysis (EDA) techniques were used to reveal various characteristics of the dataset. The general objective is to provide more insights on the nature, structure and issues with the dataset so as to identify the best ways for using it to achieve improved ML-based IDS models. Furthermore, some of the open problems that can arise from the use of the dataset in any machine learning-based Intrusion Detection systems are highlighted and possible solutions are briefly discussed. The EDA techniques used revealed important relationships among input variables and the target class. The study concluded that the EDA can better influence the decision of future IDS researches that use the dataset. Thus, improved machine learning-based intrusion detection systems can be built from the dataset once it is well understood and pre-processed.

Deep learning has revolutionized image recognition. One significant obstacle still remains, the vulnerability of these models to adversarial attacks. These attacks manipulate images with subtle changes that cause CNN misclassification. While methods, such as adversarial training, have been proposed to defend against adversarial attacks, they incur additional training costs, either extra input samples or auxiliary models. In this work, we propose an efficient approach to deploying robust models that utilize image transformations to remove adversarial noises. We investigate the performance of simple transformations and report several effective ones, including Affine blur, Gaussian blur, Median blur, and Bilateral blur against various adversarial attack methods, such as Fast Gradient Sign Method (FGSM), Randomized + FGSM (RFGSM), and Projected Gradient Descent (PGD). We apply these image transformation techniques to the widely used ImageNet dataset, and experimental results demonstrate the potential of image transformation methods as a strong defense against adversarial attacks in deep learning-based image classification systems, especially when combined with cutting-edge neural network architectures such as ResNet50 and DenseNet121. Our comprehensive results show that these transformations can significantly improve the robustness of CNN models against adversarial attacks on ImageNet, achieving a recovery rate of up to 85% to 90% without incurring extra resource costs.