Biblio

With the proliferation of data in Internet-related applications, incidences of cyber security have increased manyfold. Energy management, which is one of the smart city layers, has also been experiencing cyberattacks. Furthermore, the Distributed Energy Resources (DER), which depend on different controllers to provide energy to the main physical smart grid of a smart city, is prone to cyberattacks. The increased cyber-attacks on DER systems are mainly because of its dependency on digital communication and controls as there is an increase in the number of devices owned and controlled by consumers and third parties. This paper analyzes the major cyber security and privacy challenges that might inflict, damage or compromise the DER and related controllers in smart cities. These challenges highlight that the security and privacy on the Internet of Things (IoT), big data, artificial intelligence, and smart grid, which are the building blocks of a smart city, must be addressed in the DER sector. It is observed that the security and privacy challenges in smart cities can be solved through the distributed framework, by identifying and classifying stakeholders, using appropriate model, and by incorporating fault-tolerance techniques.

Cyber Physical Systems (CPS) are widely deployed and employed in many recent real applications such as automobiles with sensing technology for crashes to protect passengers, automated homes with various smart appliances and control units, and medical instruments with sensing capability of glucose levels in blood to keep track of normal body function. In spite of their significance, CPS infrastructures are vulnerable to cyberattacks due to the limitations in the computing, processing, memory, power, and transmission capabilities for their endpoint/edge appliances. In this paper, we consider a short systematic investigation for the models and techniques of cyberattacks and threats rate against Cyber Physical Systems with multiple subsystems and redundant elements such as, network of computing devices or storage modules. The cyberattacks are assumed to be externally launched against the Cyber Physical System during a prescribed operational time unit following stochastic distribution models such as Poisson probability distribution, negative-binomial probability distribution and other that have been extensively employed in the literature and proved their efficiency in modeling system attacks and threats.

Currently and particularly with remote working scenarios during COVID-19, phishing attack has become one of the most significant threats faced by internet users, organizations, and service providers. In a phishing attack, the attacker tries to steal client sensitive data (such as login, passwords, and credit card details) using spoofed emails and fake websites. Cybercriminals, hacktivists, and nation-state spy agencies have now got a fertilized ground to deploy their latest innovative phishing attacks. Timely detection of phishing attacks has become most crucial than ever. Machine learning algorithms can be used to accurately detect phishing attacks before a user is harmed. This paper presents a novel ensemble model to detect phishing attacks on the website. We select three machine learning classifiers: Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Decision Tree (C4.5) to use in an ensemble method with Random Forest Classifier (RFC). This ensemble method effectively detects website phishing attacks with better accuracy than existing studies. Experimental results demonstrate that the ensemble of KNN and RFC detects phishing attacks with 97.33% accuracy.

The widespread utilization of smart grids is due to their flexibility to support the two-way flow of electricity and data. The critical nature of smart grids evokes traditional network attacks. Due to the advantages of blockchains in terms of ensuring trustworthiness and security, a significant body of literature has been recently developed to secure smart grid operations. We categorize the blockchain applications in smart grid into three categories: energy trading, infrastructure management, and smart-grid operations management. This paper provides an extensive survey of these works and the different ways to utilize blockchains in smart grid in general. We propose an abstract system to overcome a critical cyberattack; namely, the fake data injection, as previous works did not consider such an attack.

Transient simulation is a critical task of validating the dynamic model of the power grid. We propose an off-line method for validating dynamic grid models and assessing the dynamic security of the grid in the presence of cyberattacks. Simulations are executed in PowerWorld and PSCAD/EMTDC to compare the impact on the grid of cyber-attacks. Generators in the IEEE 14-bus system have been modified to match the need of adjustment in modern power system operation. To get effective measurements for state estimation, SCADA polling model is reproduced in PSCAD/EMTDC by providing controlled sampling frequency. The results of a tripped line case and injecting false data to the loads caused by cyberattacks is presented and analyzed.

The energy sector is facing increasing risks, mainly concerning fraudulent activities and cyberattacks. This paradigm shift in risks would require innovative solutions. This paper proposes an innovative architecture based on Distributed Ledger Technologies (Blockchain) and Triple Entry Accounting (X-Accounting). The proposed architecture focusing on new applications of payment and billing would improve accountability and compliance as well as security and reliability. Future research can extend this architecture to other energy technologies and systems like EMS/SCADA and associated applications.

Cyber resiliency in Cloud computing is one of the most important capability of an enterprise network that provides continues ability to withstand and quick recovery from the adversary conditions. This capability can be measured through cybersecurity risk assessment techniques. However, cybersecurity risk management studies in cloud computing resiliency approaches are deficient. This paper proposes resilient cloud cybersecurity risk assessment tailored specifically to Dropbox with two methods: technical-based solution motivated by a cybersecurity risk assessment of cloud services, and a target personnel-based solution guided by cybersecurity-related survey among employees to identify their knowledge that qualifies them withstand to any cyberattack. The proposed work attempts to identify cloud vulnerabilities, assess threats and detect high risk components, to finally propose appropriate safeguards such as failure predicting and removing, redundancy or load balancing techniques for quick recovery and return to pre-attack state if failure happens.

Both the sophistication and scale of cyberattacks are increasing, revealing the extent of risks at which critical infrastructure and other information and communication systems are exposed. Furthermore, the introduction of IoT devices in a number of different applications, ranging from home automation to the monitoring of critical infrastructure, has created an even more complicated cybersecurity landscape. A large amount of research has been done on detecting these attacks in real time, however mitigation is left to security experts, which is time consuming and may have economic consequences. In addition, there is no public data available for action selection that could enable the use of the latest techniques in machine learning or deep learning for this area. Currently, most systems deploy a rule-based response selection methodology for mitigating detected attacks. In this paper, we introduce a situation calculus-based approach to automated response for IoT cyberattacks. The approach offers explicit semantic-rich cognitive modeling of attacks, effects and actions and supports situation inference for timely and accurate responses. We demonstrate the effectiveness of our approach for modelling and responding to cyberattacks by implementing a use case in a real-world IoT scenario.

Among the different types of malware, botnets are rising as the most genuine risk against cybersecurity as they give a stage to criminal operations (e.g., Distributed Denial of Service (DDOS) attacks, malware dispersal, phishing, and click fraud and identity theft). Existing botnet detection techniques work only on specific botnet Command and Control (C&C) protocols and lack in providing early-stage botnet detection. In this paper, we propose an approach for early-stage botnet detection. The proposed approach first selects the optimal features using feature selection techniques. Next, it feeds these features to machine learning classifiers to evaluate the performance of the botnet detection. Experiments reveals that the proposed approach efficiently classifies normal and malicious traffic at an early stage. The proposed approach achieves the accuracy of 99%, True Positive Rate (TPR) of 0.99 %, and False Positive Rate (FPR) of 0.007 % and provide an efficient detection rate in comparison with the existing approach.

Autonomy in cybersystems depends on their ability to be self-aware by understanding the intent of services and applications that are running on those systems. In case of mission-critical cybersystems that are deployed in dynamic and unpredictable environments, the newly integrated unknown applications or services can either be benign and essential for the mission or they can be cyberattacks. In some cases, these cyberattacks are evasive Advanced Persistent Threats (APTs) where the attackers remain undetected for reconnaissance in order to ascertain system features for an attack e.g. Trojan Laziok. In other cases, the attackers can use the system only for computing e.g. cryptomining malware. APTs such as cryptomining malware neither disrupt normal system functionalities nor trigger any warning signs because they simply perform bitwise and cryptographic operations as any other benign compression or encoding application. Thus, it is difficult for defense mechanisms such as antivirus applications to detect these attacks. In this paper, we propose an Operating Context profiling system based on deep neural networks-Long Short-Term Memory (LSTM) networks-using Windows Performance Counters data for detecting these evasive cryptomining applications. In addition, we propose Deep Cryptomining Profiler (DeCrypto Pro), a detection system with a novel model selection framework containing a utility function that can select a classification model for behavior profiling from both the light-weight machine learning models (Random Forest and k-Nearest Neighbors) and a deep learning model (LSTM), depending on available computing resources. Given data from performance counters, we show that individual models perform with high accuracy and can be trained with limited training data. We also show that the DeCrypto Profiler framework reduces the use of computational resources and accurately detects cryptomining applications by selecting an appropriate model, given the constraints such as data sample size and system configuration.

When building the architecture of cyber defense systems, one of the important tasks is to create a methodology for current diagnostics of cybersecurity status of information systems and objects of information activity. The complexity of this procedure is that having a strong security level of the object at the software level does not mean that such power is available at the hardware level or at the cryptographic level. There are always weaknesses in all levels of information security that criminals are constantly looking for. Therefore, the task of promptly calculating the likelihood of possible negative consequences from the successful implementation of cyberattacks is an urgent task today. This paper proposes an approach of obtaining an instantaneous calculation of the probabilities of negative consequences from the successful implementation of cyberattacks on objects of information activity on the basis of delayed differential equation theory and the mechanism of constructing a logical Fuzzy function. This makes it possible to diagnose the security status of the information system.

Situation awareness consists of "the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future". Being aware of the security situation is then mandatory to launch proper security reactions in response to cybersecurity attacks. Security Incident and Event Management solutions are deployed within Security Operation Centers. Some vendors propose machine learning based approaches to detect intrusions by analysing networks behaviours. But cyberattacks like Wannacry and NotPetya, which shut down hundreds of thousands of computers, demonstrated that networks monitoring and surveillance solutions remain insufficient. Detecting these complex attacks (a.k.a. Advanced Persistent Threats) requires security administrators to retain a large number of logs just in case problems are detected and involve the investigation of past security events. This approach generates massive data that have to be analysed at the right time in order to detect any accidental or caused incident. In the same time, security administrators are not yet seasoned to such a task and lack the desired skills in data science. As a consequence, a large amount of data is available and still remains unexplored which leaves number of indicators of compromise under the radar. Building on the concept of situation awareness, we developed a situation-driven framework, called dynSMAUG, for dynamic security management. This approach simplifies the security management of dynamic systems and allows the specification of security policies at a high-level of abstraction (close to security requirements). This invited paper aims at exposing real security situations elicitation, coming from networks security experts, and showing the results of exploratory analysis techniques using complex event processing techniques to identify and extract security situations from a large volume of logs. The results contributed to the extension of the dynSMAUG solution.

With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.

Capturing the patterns in adversarial movement can provide valuable information regarding how the adversaries progress through cyberattacks. This information can be further employed for making comparisons and interpretations of decision making of the adversaries. In this study, we propose a framework based on concepts of social networks to characterize and compare the patterns, variations and shifts in the movements made by an adversarial team during a real-time cybersecurity exercise. We also explore the possibility of movement association with the skill sets using topological sort networks. This research provides preliminary insight on adversarial movement complexity and linearity and decision-making as cyberattacks unfold.

While cyberattacks pose a relatively new challenge for power grid control systems, commercial cloud systems have needed to address similar threats for many years. However, technology and approaches developed for cloud systems do not necessarily transfer directly to the power grid, due to important differences between the two domains. We discuss our experience adapting intrusion-tolerant cloud technologies to the power domain and describe the challenges we have encountered and potential directions for overcoming those obstacles.

Recent years have seen an increase in the data usage in cars, particularly as they become more autonomous and connected. With the rise in data use have come concerns about automotive cyber-security. An in-vehicle network shown to be particularly vulnerable is the Controller Area Network (CAN), which is the communication bus used by the car's safety critical and performance critical components. Cyber attacks on the CAN have been demonstrated, leading to research to develop attack detection and attack prevention systems. Such research requires representative attack demonstrations and data for testing. Obtaining this data is problematical due to the expense, danger and impracticality of using real cars on roads or tracks for example attacks. Whilst CAN simulators are available, these tend to be configured for testing conformance and functionality, rather than analysing security and cyber vulnerability. We therefore adapt a leading, industry-standard, CAN simulator to incorporate a core set of cyber attacks that are representative of those proposed by other researchers. Our adaptation allows the user to configure the attacks, and can be added easily to the free version of the simulator. Here we describe the simulator and, after reviewing the attacks that have been demonstrated and discussing their commonalities, we outline the attacks that we have incorporated into the simulator.

The popularity of Web-based computing has given increase to browser-based cyberattacks. These cyberattacks use websites that exploit various web browser vulnerabilities. To help regular users avoid exploit websites and engage in safe online activities, we propose a methodology of building a machine learning-powered predictive analytical model that will measure the risk of attacks and privacy breaches associated with visiting different websites and performing online activities using web browsers. The model will learn risk levels from historical data and metadata scraped from web browsers.

One of the effective ways of detecting malicious traffic in computer networks is intrusion detection systems (IDS). Though IDS identify malicious activities in a network, it might be difficult to detect distributed or coordinated attacks because they only have single vantage point. To combat this problem, cooperative intrusion detection system was proposed. In this detection system, nodes exchange attack features or signatures with a view of detecting an attack that has previously been detected by one of the other nodes in the system. Exchanging of attack features is necessary because a zero-day attacks (attacks without known signature) experienced in different locations are not the same. Although this solution enhanced the ability of a single IDS to respond to attacks that have been previously identified by cooperating nodes, malicious activities such as fake data injection, data manipulation or deletion and data consistency are problems threatening this approach. In this paper, we propose a solution that leverages blockchain's distributive technology, tamper-proof ability and data immutability to detect and prevent malicious activities and solve data consistency problems facing cooperative intrusion detection. Focusing on extraction, storage and distribution stages of cooperative intrusion detection, we develop a blockchain-based solution that securely extracts features or signatures, adds extra verification step, makes storage of these signatures and features distributive and data sharing secured. Performance evaluation of the system with respect to its response time and resistance to the features/signatures injection is presented. The result shows that the proposed solution prevents stored attack features or signature against malicious data injection, manipulation or deletion and has low latency.

The question of whether strategic deterrence in cyberspace is achievable given the challenges of detection, attribution and credible retaliation is a topic of contention among military and civilian defense strategists. This paper examines the traditional strategic deterrence theory and its application to deterrence in cyberspace (the newly defined 5th battlespace domain, following land, air, sea and space domains), which is being used increasingly by nation-states and their proxies to achieve information dominance and to gain tactical and strategic economic and military advantage. It presents a taxonomy of cyberattacks that identifies which types of threats in the confidentiality, integrity, availability cybersecurity model triad present the greatest risk to nation-state economic and military security, including their political and social facets. The argument is presented that attacks on confidentiality cannot be subject to deterrence in the current international legal framework and that the focus of strategy needs to be applied to integrity and availability attacks. A potential cyberdeterrence strategy is put forth that can enhance national security against devastating cyberattacks through a credible declaratory retaliation capability that establishes red lines which may trigger a counter-strike against all identifiable responsible parties. The author believes such strategy can credibly influence nation-state threat actors who themselves exhibit serious vulnerabilities to cyber attacks from launching a devastating cyber first strike.

To add more functionality and enhance usability of web applications, JavaScript (JS) is frequently used. Even with many advantages and usefulness of JS, an annoying fact is that many recent cyberattacks such as drive-by-download attacks exploit vulnerability of JS codes. In general, malicious JS codes are not easy to detect, because they sneakily exploit vulnerabilities of browsers and plugin software, and attack visitors of a web site unknowingly. To protect users from such threads, the development of an accurate detection system for malicious JS is soliciting. Conventional approaches often employ signature and heuristic-based methods, which are prone to suffer from zero-day attacks, i.e., causing many false negatives and/or false positives. For this problem, this paper adopts a machine-learning approach to feature learning called Doc2Vec, which is a neural network model that can learn context information of texts. The extracted features are given to a classifier model (e.g., SVM and neural networks) and it judges the maliciousness of a JS code. In the performance evaluation, we use the D3M Dataset (Drive-by-Download Data by Marionette) for malicious JS codes and JSUPACK for benign ones for both training and test purposes. We then compare the performance to other feature learning methods. Our experimental results show that the proposed Doc2Vec features provide better accuracy and fast classification in malicious JS code detection compared to conventional approaches.

The work proposes and justifies a processing algorithm of computer security incidents based on the author's signatures of cyberattacks. Attention is also paid to the design pattern SOPKA based on the Russian ViPNet technology. Recommendations are made regarding the establishment of the corporate segment SOPKA, which meets the requirements of Presidential Decree of January 15, 2013 number 31c “On the establishment of the state system of detection, prevention and elimination of the consequences of cyber-attacks on information resources of the Russian Federation” and “Concept of the state system of detection, prevention and elimination of the consequences of cyber-attacks on information resources of the Russian Federation” approved by the President of the Russian Federation on December 12, 2014, No K 1274.

The U.S Government has been the target for cyberattacks from all over the world. Just recently, former President Obama accused the Russian government of the leaking emails to Wikileaks and declared that the U.S. might be forced to respond. While Russia denied involvement, it is clear that the U.S. has to take some defensive measures to protect its data infrastructure. Insider threats have been the cause of other sensitive information leaks too, including the infamous Edward Snowden incident. Most of the recent leaks were in the form of text. Due to the nature of text data, security classifications are assigned manually. In an adversarial environment, insiders can leak texts through E-mail, printers, or any untrusted channels. The optimal defense is to automatically detect the unstructured text security class and enforce the appropriate protection mechanism without degrading services or daily tasks. Unfortunately, existing Data Leak Prevention (DLP) systems are not well suited for detecting unstructured texts. In this paper, we compare two recent approaches in the literature for text security classification, evaluating them on actual sensitive text data from the WikiLeaks dataset.

As the use of wireless technologies increases significantly due to ease of deployment, cost-effectiveness and the increase in bandwidth, there is a critical need to make the wireless communications secure, and resilient to attacks or faults (malicious or natural). Wireless communications are inherently prone to cyberattacks due to the open access to the medium. While current wireless protocols have addressed the privacy issues, they have failed to provide effective solutions against denial of service attacks, session hijacking and jamming attacks. In this paper, we present a resilient wireless communication architecture based on Moving Target Defense, and Software Defined Radios (SDRs). The approach achieves its resilient operations by randomly changing the runtime characteristics of the wireless communications channels between different wireless nodes to make it extremely difficult to succeed in launching attacks. The runtime characteristics that can be changed include packet size, network address, modulation type, and the operating frequency of the channel. In addition, the lifespan for each configuration will be random. To reduce the overhead in switching between two consecutive configurations, we use two radio channels that are selected at random from a finite set of potential channels, one will be designated as an active channel while the second acts as a standby channel. This will harden the wireless communications attacks because the attackers have no clue on what channels are currently being used to exploit existing vulnerability and launch an attack. The experimental results and evaluation show that our approach can tolerate a wide range of attacks (Jamming, DOS and session attacks) against wireless networks.

Taiwan has become the frontline in an emerging cyberspace battle. Cyberattacks from different countries are constantly reported during past decades. The incident of Advanced Persistent Threat (APT) is analyzed from the golden triangle components (people, process and technology) to ensure the application of digital forensics. This study presents a novel People-Process-Technology-Strategy (PPTS) model by implementing a triage investigative step to identify evidence dynamics in digital data and essential information in auditing logs. The result of this study is expected to improve APT investigation. The investigation scenario of this proposed methodology is illustrated by applying to some APT incidents in Taiwan.

Taiwan has become the frontline in an emerging cyberspace battle. Cyberattacks from different countries are constantly reported during past decades. The incident of Advanced Persistent Threat (APT) is analyzed from the golden triangle components (people, process and technology) to ensure the application of digital forensics. This study presents a novel People-Process-Technology-Strategy (PPTS) model by implementing a triage investigative step to identify evidence dynamics in digital data and essential information in auditing logs. The result of this study is expected to improve APT investigation. The investigation scenario of this proposed methodology is illustrated by applying to some APT incidents in Taiwan.