Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

End-to-end anomaly detection in stream data

Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health

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

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Improved Detection for Advanced Polymorphic Malware

Malicious Software (malware) attacks across the internet are increasing at an alarming rate. Cyber-attacks have become increasingly more sophisticated and targeted. These targeted attacks are aimed at compromising networks, stealing personal financial information and removing sensitive data or disrupting operations. Current malware detection approaches work well for previously known signatures. However, malware developers utilize techniques to mutate and change software properties (signatures) to avoid and evade detection. Polymorphic malware is practically undetectable with signature-based defensive technologies. Today’s effective detection rate for polymorphic malware detection ranges from 68.75% to 81.25%. New techniques are needed to improve malware detection rates. Improved detection of polymorphic malware can only be accomplished by extracting features beyond the signature realm. Targeted detection for polymorphic malware must rely upon extracting key features and characteristics for advanced analysis. Traditionally, malware researchers have relied on limited dimensional features such as behavior (dynamic) or source/execution code analysis (static). This study’s focus was to extract and evaluate a limited set of multidimensional topological data in order to improve detection for polymorphic malware. This study used multidimensional analysis (file properties, static and dynamic analysis) with machine learning algorithms to improve malware detection. This research demonstrated improved polymorphic malware detection can be achieved with machine learning. This study conducted a number of experiments using a standard experimental testing protocol. This study utilized three advanced algorithms (Metabagging (MB), Instance Based k-Means (IBk) and Deep Learning Multi-Layer Perceptron) with a limited set of multidimensional data. Experimental results delivered detection results above 99.43%. In addition, the experiments delivered near zero false positives. The study’s approach was based on single case experimental design, a well-accepted protocol for progressive testing. The study constructed a prototype to automate feature extraction, assemble files for analysis, and analyze results through multiple clustering algorithms. The study performed an evaluation of large malware sample datasets to understand effectiveness across a wide range of malware. The study developed an integrated framework which automated feature extraction for multidimensional analysis. The feature extraction framework consisted of four modules: 1) a pre-process module that extracts and generates topological features based on static analysis of machine code and file characteristics, 2) a behavioral analysis module that extracts behavioral characteristics based on file execution (dynamic analysis), 3) an input file construction and submission module, and 4) a machine learning module that employs various advanced algorithms. As with most studies, careful attention was paid to false positive and false negative rates which reduce their overall detection accuracy and effectiveness. This study provided a novel approach to expand the malware body of knowledge and improve the detection for polymorphic malware targeting Microsoft operating systems

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged