Adversarial Machine Learning for 5G Communications Security

In this paper, we investigate the coexistence of the 5G communication network with a fixed-satellite service (FSS) in the 3.5 GHz and 26 GHz frequency bands. We analyze a distance protection scheme for the FSS Earth station (ES) and 5G base stations (BS). Furthermore, we define the exclusion and restriction zones to develop a transmit power control scheme based on the Citizens Broadband Radio Service (CBRS). An interactive power control scheme is also devised for the restriction zone and extensively analyzed through simulations. The proposed scheme is examined for practical scenarios such as the rural macrocells (RMa), urban macrocells (UMa), and urban microcells (UMi) as defined by the 3GPP. The impact of the antenna type is also investigated, and BSs with omnidirectional, 4 × 4, 8 × 8, and 16 × 16 antenna arrays are examined, as defined by 3GPP, for the 5G networks. The results confirm that 5G systems can coexist with the FSS and provide quantitative insights into the selection of the system parameters, including interference margins, exclusion sizes, and reduction zones, for different scenarios and antenna types.

A novel method based on machine learning is proposed to estimate the electromagnetic radiation level at the ground plane near fifth‐generation (5G) base stations. The machine learning model was trained using data from various 5G base stations, enabling it to estimate the electric field intensity at any arbitrary radiation point when the base station provides service to different numbers of 5G terminals which are in different service modes. The inputs required for the model include the transmit power of the antenna, the antenna gain, the distance between the 5G base station and 5G terminals, terminal service modes, the number of 5G terminals and the environmental complexity around the 5G base station. Experimental results demonstrate the feasibility and effectiveness of the estimation method, with the mean absolute percentage error of the machine learning model being approximately 5.98%. This level of accuracy showcases the reliability of the approach. Moreover, the proposed method offers low costs when compared with on‐site measurements. The estimated results can be utilised to reduce test costs and provide valuable guidance for optimal site selection, thereby facilitating radio wave coverage or electromagnetic radiation regulation of 5G base stations.

Cyber Physical Systems (CPS) are characterized by their ability to integrate\nthe physical and information or cyber worlds. Their deployment in critical\ninfrastructure have demonstrated a potential to transform the world. However,\nharnessing this potential is limited by their critical nature and the far\nreaching effects of cyber attacks on human, infrastructure and the environment.\nAn attraction for cyber concerns in CPS rises from the process of sending\ninformation from sensors to actuators over the wireless communication medium,\nthereby widening the attack surface. Traditionally, CPS security has been\ninvestigated from the perspective of preventing intruders from gaining access\nto the system using cryptography and other access control techniques. Most\nresearch work have therefore focused on the detection of attacks in CPS.\nHowever, in a world of increasing adversaries, it is becoming more difficult to\ntotally prevent CPS from adversarial attacks, hence the need to focus on making\nCPS resilient. Resilient CPS are designed to withstand disruptions and remain\nfunctional despite the operation of adversaries. One of the dominant\nmethodologies explored for building resilient CPS is dependent on machine\nlearning (ML) algorithms. However, rising from recent research in adversarial\nML, we posit that ML algorithms for securing CPS must themselves be resilient.\nThis paper is therefore aimed at comprehensively surveying the interactions\nbetween resilient CPS using ML and resilient ML when applied in CPS. The paper\nconcludes with a number of research trends and promising future research\ndirections. Furthermore, with this paper, readers can have a thorough\nunderstanding of recent advances on ML-based security and securing ML for CPS\nand countermeasures, as well as research trends in this active research area.\n

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.

Software-defined networking (SDN) is becoming a predominant architecture for managing diverse networks. However, recent research has exhibited the susceptibility of SDN architectures to cyberattacks, which increases its security challenges. Many researchers have used machine learning (ML) and deep learning (DL) classifiers to mitigate cyberattacks in SDN architectures. Since SDN datasets could suffer from class imbalance issues, the classification accuracy of predictive classifiers is undermined. Therefore, this research conducts a comparative analysis of the impact of utilizing oversampling and principal component analysis (PCA) techniques on ML and DL classifiers using publicly available SDN datasets. This approach combines mitigating the class imbalance issue and maintaining the effectiveness of the performance when reducing data dimensionality. Initially, the oversampling techniques are used to balance the classes of the SDN datasets. Then, the classification performance of ML and DL classifiers is evaluated and compared to observe the effectiveness of each oversampling technique on each classifier. PCA is applied to the balanced dataset, and the classifier’s performance is evaluated and compared. The results demonstrated that Random Oversampling outperformed the other balancing techniques. Furthermore, the XGBoost and Transformer classifiers were the most sensitive models when using oversampling and PCA algorithms. In addition, macro and weighted averages of evaluation metrics were calculated to show the impact of imbalanced class datasets on each classifier.

It is important to capture passengers’ public transit behavior and their mobility to create profiles, which are critical for analyzing human activities, understanding the social and economic structure of cities, improving public transportation, assisting urban planning, and promoting smart cities. In this paper, we develop a generative adversarial machine learning network to characterize the temporal and spatial mobility behavior of public transit passengers, based on massive smart card data and road network data. The Apriori algorithm is extended with spatio-temporal constraints to extract frequent transit mobility patterns of individual passengers based on a reconstructed personal trip dataset. This individual-level pattern information is used to construct personalized feature vectors. For regular and frequent public transit passengers, we identify similar transit mobility groups using spatio-temporal constraints to construct a group feature vector. We develop a generative adversarial network to embed public transit mobility of passengers. The proposed model’s generator consists of an auto-encoder, which extracts a low-dimensional and compact representation of passenger behavior, and a pre-trained sub-generator containing generalization features of public transit passengers. Shenzhen City is taken as the study area in this paper, and experiments were carried out based on smart card data, road network data, and bus GPS data. Clustering analysis of embedding vector representation and estimation of the top K transit destinations were conducted, verifying that the proposed method can profile passenger transit mobility in a comprehensive and compact manner.

The increasing complexity of power systems as well as the rapid expansion of renewable energy resources and deregulation of electricity industry lead to continual demand for operating power systems at higher efficiencies. The modern power systems are pushed to operate close to their secure limits. When subjected to a severe contingency, the stressed power systems may lose its dynamic security, resulting in catastrophic consequences such as cascading failure and even wide-spread blackout. Under this new reality, online and real-time power system stability assessments have great impacts on avoiding blackout events. The traditional stability methods are mainly based on time-domain simulation, which are not fast enough to satisfy the online and real-time application requirements. In recent years, with the development of advanced real-time system monitoring and communication techniques, the data-driven methods have been identified as powerful tools to help power systems counteract large disturbance and avoid instability. To solve above threats, this Ph.D. research aims to develop data-analytics methodologies from power grid measurement data to recognize the system stability, covering both pre-fault dynamic security assessment (DSA) and post-fault real-time short-term voltage stability (STVS) assessment areas under some extreme scenarios and practical issues. Specific objectives of this Ph.D. research are to develop an intelligent system for the future smart grid. Based on several kinds of promising advanced data-analytics technologies, including randomized learning, incremental broad learning (BL), transfer learning (TL), adversarial machine learning (ML), and generative adversarial network (GAN), etc, an intelligent system will be developed to interpret the power grid stability characteristics, and to support stability-constrained operation control actions. Hence, it can make power systems achieve seven goals, including extensibility, efficiency, robustness, security, personalization, reliability, and effectiveness. The outcomes of this Ph.D. research will enable a more secure, intelligent, and knowledge-transparent future of the next-generation smart grid. The main contributions through this Ph.D. research are as follows: Firstly, the temporal-adaptive data-driven methods are proposed based on optimized hybrid ensemble learning mechanism for online STVS assessment. Compared to the traditional data-driven stability methods, the proposed methods can balance the tradeoff between the stability accuracy and speed and select the most suitable Pareto set. Secondly, in the area of DSA, an online updating DSA method is developed by using incremental BL technique. The incremental BL method only needs to calculate the pseudoinverse of new state matrix without the computations of the whole output weights matrix. In this way, it does not need re-training the whole model to continuously maintain and improve the online DSA accuracy. Besides, the proposed method can achieve under three different increment scenarios, including increment of enhancement hidden nodes, features, and new training instances. Thirdly, in the area of missing phasor measurement unit (PMU) data, two different fully robust data-driven stability methods are developed, respectively for pre-fault DSA and post-fault STVS assessment. The former one is based on GAN without observability-constrained feature subsets to ensure its availability under any PMU missing scenario. The latter one is based on super-resolution perception and incremental BL, which can achieve both satisfactory stability accuracy and speed. Fourthly, in order to accurately quantify the robustness of the ML-based model under adversarial perturbations on safety-critical power system stability problem, an adversarial robustness verification method for data-driven stability models is proposed. Besides, a model-free and attack-independent robust index are defined to evaluate the ability of the ML-based stability models against adversarial examples, which can be used to select the candidate model and provide formal robustness guarantee for stability application in practice. Rigorous mathematic proof is provided for the robust index under both linear and non-linear binary scenarios. Moreover, the proposed robust index can be used to mitigate the adversarial examples via adversarial training. Finally, three different TL-based DSA methods are proposed to use one known fault to evaluate other related but different unknown faults. The first two methods aim to reduce the distribution differences between the trained data and unknown data with the complete unlearned faults. The third one considers the different faults with incomplete data scenario. Through applying the adversarial training process, the DSA model trained by one fault can work for a different but related fault by confusing domain discriminator. With the property of feature extractor network, the proposed integrated TL-based method can solve the missing data issue for the unknown faults. All the methodologies have been tested on benchmark power systems to verify their effectiveness, and comparative and comparative studies with existing methods in the literature are conducted where applicable.

Machine learning is being used for an increasing number of applications with societal impact. In such settings, models must be trusted to be fair, useful, and robust. In many applications, a large amount of training data is collected from a variety of sources, including from private or untrusted individuals. Manually sanitizing datasets is difficult as datasets increase in size, which allows a motivate adversary to adversarially corrupt training data, crafted to impact the model at test time, referred to as a poisoning attack. These poisoning attacks can cause specific users' data to be misclassified, which can be harmful if models are applied to sensitive tasks such as security applications. At the same time, models trained on datasets collected from real people must protect their privacy, preventing unscrupulous onlookers from learning more than they should; in sensitive domains such as personalized medicine, privacy is of utmost importance. In this thesis, we describe integrity and privacy vulnerabilities in these critical settings. We explore the variety of adversarial goals that can be accomplished with poisoning, and how to construct defenses against these attacks (if this is possible at all). Our work will high- light the difficulty of developing generic poisoning defenses; dependence on the adversarial objective appears to be necessary for large enough attacks. Next, we discuss the connection between differential privacy and poisoning attacks, showing that poisoning can be useful for interpreting privacy guarantees, and differential privacy may not serve as a defense from poisoning attacks. Finally, we discuss privacy leakage in the realistic training setting where models are updated repeatedly over time. Our privacy attacks highlight and tackle the cur- rent challenges in deploying private algorithms in real world settings. Overall, this thesis will demonstrate the diversity of types of both poisoning attacks and privacy attacks and the challenges in defending against these attacks and securing machine learning in critical settings.--Author's abstract

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.

Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML in next-generation wireless communications are discussed.

Recursive reasoning-based training-time adversarial machine learning

Adversarial machine learning (AML) research is concerned with robustness of machine learning models and algorithms to malicious tampering. Originating at the intersection between machine learning and cybersecurity, AML has come to have broader research appeal, stretching traditional notions of security to include applications of computer vision, natural language processing, and network science. In addition, the problems of strategic classification, algorithmic recourse, and counterfactual explanations have essentially the same core mathematical structure as AML, despite distinct motivations. I give a simplified overview of the central problems in AML, and then discuss both the security-motivated AML domains, and the problems above unrelated to security. These together span a number of important AI subdisciplines, but can all broadly be viewed as concerned with trustworthy AI. My goal is to clarify both the technical connections among these, as well as the substantive differences, suggesting directions for future research.

Cyber-Security industry has always been a cat and a mouse game - whenever a new technology was developed it was shortly followed by the appearance of several techniques used by malware creators to avoid detection. It is no surprise that the developing of adversarial machine learning algorithms has provided a tool that can be used to avoid machine learning based detection mechanisms available in security products. This paper presents how the same algorithms can also be used to strengthen a security solution by identifying its weak points / features. We will also provide a method that can be used to fight Generative Adversarial Networks (GANs) with GANs, that is effective when a malware writer is using these methods to avoid detection.

This paper proposes an evaluation of the performance of 5G communication between a user equipment (UE), which can be a car, and a 5G base station (gNB), under the influence of different numbers of UEs connected to that base station. There are two approaches to this communication evaluation: i) simulated mode, using Simu5G, which is a simulator for 5G New Radio (NR) networks; ii) a practical way using a hardware setup defined on the basis of a SIM8200EA-M2 module in order to test a real-life scenario. Various performance indicators are tracked and also compared, such as Signal-to-interference-plus-noise ratio (SINR), Channel Quality Indicator (CQI), and End-to-End (E2E) Delay in order to analyze the effect of network load on 5G communication between the UE and gNB.

Untargeted white-box adversarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system