Cybersecurity: Past, Present and Future

The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-

Advances in Information Security and Privacy

With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue

Adversarial Activity Detection and Prediction Using Behavioral Biometrics

Behavioral biometrics can be used in different security applications like authentication, identification, etc. One of the trending applications is predicting future activities of people and guessing whether they will engage in malicious activities in the future. In this research, we study the possibility of predicting future activities and propose novel methods for near-future activity prediction. First, we study gait signals captured using smartphone accelerometer sensor and build a model to predict a future gait signal. Activity recognition using body movements captured from mobile phone sensors has been a major point of interest in recent research. Data that is being continuously read from mobile sensors can be used to recognize user activity. We propose a model for predicting human body movements based on the previous activity that has been read from sensors and continuously updating our prediction as new data becomes available. Our results show that our model can predict the future movement signal with a high accuracy that can contribute to several applications in the area. Second, we study keystroke acoustics and build a model for predicting future activities of the users by recording their keystrokes audio. Using keystroke acoustics to predict typed text has significant advantages, such as being recorded covertly from a distance and requiring no physical access to the computer system. Recently, some studies have been done on keystroke acoustics, however, to the best of our knowledge none have used them to predict adversarial activities. On a dataset of two million keystrokes consisting of seven adversarial and one benign activity, we use a signal processing approach to extract keystrokes from the audio and a clustering method to recover the typed letters followed by a text recovery module to regenerate the typed words. Furthermore, we use a neural network model to classify the benign and adversarial activities and achieve significant results: (1) we extract individual keystroke sounds from the raw audio with 91% accuracy and recover words from audio recordings in a noisy environment with 71% average top-10 accuracy. (2) We classify adversarial activities with 93% to 98% average accuracy under different operating scenarios. Third, we study the correlation between the personality traits of users with their keystroke and mouse dynamics. Even with the availability of multiple interfaces, such as voice, touch, etc., keyboard and mouse remain the primary interfaces to a computer. Any insights on the relation between keyboard and mouse dynamics with the personality type of the users can provide foundations for various applications, such as advertisement, social media, etc. We use a dataset of keystroke and mouse dynamics collected from 104 users together with their responses to two personality tests to analyze how their interaction with the computer relates to their personality. Our findings show that there are considerable trends and patterns in keystroke and mouse dynamics that are correlated with each personality type

On Security and Privacy for Networked Information Society : Observations and Solutions for Security Engineering and Trust Building in Advanced Societal Processes

Our society has developed into a networked information society, in which all aspects of human life are interconnected via the Internet — the backbone through which a significant part of communications traffic is routed. This makes the Internet arguably the most important piece of critical infrastructure in the world. Securing Internet communications for everyone using it is extremely important, as the continuing growth of the networked information society relies upon fast, reliable and secure communications. A prominent threat to the security and privacy of Internet users is mass surveillance of Internet communications. The methods and tools used to implement mass surveillance capabilities on the Internet pose a danger to the security of all communications, not just the intended targets. When we continue to further build the networked information upon the unreliable foundation of the Internet we encounter increasingly complex problems,which are the main focus of this dissertation. As the reliance on communication technology grows in a society, so does the importance of information security. At this stage, information security issues become separated from the purely technological domain and begin to affect everyone in society. The approach taken in this thesis is therefore both technical and socio-technical. The research presented in this PhD thesis builds security in to the networked information society and provides parameters for further development of a safe and secure networked information society. This is achieved by proposing improvements on a multitude of layers. In the technical domain we present an efficient design flow for secure embedded devices that use cryptographic primitives in a resource-constrained environment, examine and analyze threats to biometric passport and electronic voting systems, observe techniques used to conduct mass Internet surveillance, and analyze the security of Finnish web user passwords. In the socio-technical domain we examine surveillance and how it affects the citizens of a networked information society, study methods for delivering efficient security education, examine what is essential security knowledge for citizens, advocate mastery over surveillance data by the targeted citizens in the networked information society, and examine the concept of forced trust that permeates all topics examined in this work.Yhteiskunta, jossa elämme, on muovautunut teknologian kehityksen myötä todelliseksi tietoyhteiskunnaksi. Monet verkottuneen tietoyhteiskunnan osa-alueet ovat kokeneet muutoksen tämän kehityksen seurauksena. Tämän muutoksen keskiössä on Internet: maailmanlaajuinen tietoverkko, joka mahdollistaa verkottuneiden laitteiden keskenäisen viestinnän ennennäkemättömässä mittakaavassa. Internet on muovautunut ehkä keskeisimmäksi osaksi globaalia viestintäinfrastruktuuria, ja siksi myös globaalin viestinnän turvaaminen korostuu tulevaisuudessa yhä enemmän. Verkottuneen tietoyhteiskunnan kasvu ja kehitys edellyttävät vakaan, turvallisen ja nopean viestintäjärjestelmän olemassaoloa. Laajamittainen tietoverkkojen joukkovalvonta muodostaa merkittävän uhan tämän järjestelmän vakaudelle ja turvallisuudelle. Verkkovalvonnan toteuttamiseen käytetyt menetelmät ja työkalut eivät vain anna mahdollisuutta tarkastella valvonnan kohteena olevaa viestiliikennettä, vaan myös vaarantavat kaiken Internet-liikenteen ja siitä riippuvaisen toiminnan turvallisuuden. Kun verkottunutta tietoyhteiskuntaa rakennetaan tämän kaltaisia valuvikoja ja haavoittuvuuksia sisältävän järjestelmän varaan, keskeinen uhkatekijä on, että yhteiskunnan ydintoiminnot ovat alttiina ulkopuoliselle vaikuttamiselle. Näiden uhkatekijöiden ja niiden taustalla vaikuttavien mekanismien tarkastelu on tämän väitöskirjatyön keskiössä. Koska työssä on teknisen sisällön lisäksi vahva yhteiskunnallinen elementti, tarkastellaan tiukan teknisen tarkastelun sijaan aihepiirä laajemmin myös yhteiskunnallisesta näkökulmasta. Tässä väitöskirjassa pyritään rakentamaan kokonaiskuvaa verkottuneen tietoyhteiskunnan turvallisuuteen, toimintaan ja vakauteen vaikuttavista tekijöistä, sekä tuomaan esiin uusia ratkaisuja ja avauksia eri näkökulmista. Työn tavoitteena on osaltaan mahdollistaa entistä turvallisemman verkottuneen tietoyhteiskunnan rakentaminen tulevaisuudessa. Teknisestä näkökulmasta työssä esitetään suunnitteluvuo kryptografisia primitiivejä tehokkaasti hyödyntäville rajallisen laskentatehon sulautetuviiille järjestelmille, analysoidaan biometrisiin passeihin, kansainväliseen passijärjestelmään, sekä sähköiseen äänestykseen kohdistuvia uhkia, tarkastellaan joukkovalvontaan käytettyjen tekniikoiden toimintaperiaatteita ja niiden aiheuttamia uhkia, sekä tutkitaan suomalaisten Internet-käyttäjien salasanatottumuksia verkkosovelluksissa. Teknis-yhteiskunnallisesta näkökulmasta työssä tarkastellaan valvonnan teoriaa ja perehdytään siihen, miten valvonta vaikuttaa verkottuneen tietoyhteiskunnan kansalaisiin. Lisäksi kehitetään menetelmiä parempaan tietoturvaopetukseen kaikilla koulutusasteilla, määritellään keskeiset tietoturvatietouden käsitteet, tarkastellaan mahdollisuutta soveltaa tiedon herruuden periaatetta verkottuneen tietoyhteiskunnan kansalaisistaan keräämän tiedon hallintaan ja käyttöön, sekä tutkitaan luottamuksen merkitystä yhteiskunnan ydintoimintojen turvallisuudelle ja toiminnalle, keskittyen erityisesti pakotetun luottamuksen vaikutuksiin

Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid

The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models

Intrusion detection in IoT networks using machine learning

The exponential growth of Internet of Things (IoT) infrastructure has introduced significant security challenges due to the large-scale deployment of interconnected devices. IoT devices are present in every aspect of our modern life; they are essential components of Industry 4.0, smart cities, and critical infrastructures. Therefore, the detection of attacks on this platform becomes necessary through an Intrusion Detection Systems (IDS). These tools are dedicated hardware devices or software that monitors a network to detect and automatically alert the presence of malicious activity. This study aimed to assess the viability of Machine Learning Models for IDS within IoT infrastructures. Five classifiers, encompassing a spectrum from linear models like Logistic Regression, Decision Trees from Trees Algorithms, Gaussian Naïve Bayes from Probabilistic models, Random Forest from ensemble family and Multi-Layer Perceptron from Artificial Neural Networks, were analysed. These models were trained using supervised methods on a public IoT attacks dataset, with three tasks ranging from binary classification (determining if a sample was part of an attack) to multiclassification of 8 groups of attack categories and the multiclassification of 33 individual attacks. Various metrics were considered, from performance to execution times and all models were trained and tuned using cross-validation of 10 k-folds. On the three classification tasks, Random Forest was found to be the model with best performance, at expenses of time consumption. Gaussian Naïve Bayes was the fastest algorithm in all classification¿s tasks, but with a lower performance detecting attacks. Whereas Decision Trees shows a good balance between performance and processing speed. Classifying among 8 attack categories, most models showed vulnerabilities to specific attack types, especially those in minority classes due to dataset imbalances. In more granular 33 attack type classifications, all models generally faced challenges, but Random Forest remained the most reliable, despite vulnerabilities. In conclusion, Machine Learning algorithms proves to be effective for IDS in IoT infrastructure, with Random Forest model being the most robust, but with Decision Trees offering a good balance between speed and performance.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur

Enhancing Online Security with Image-based Captchas

Given the data loss, productivity, and financial risks posed by security breaches, there is a great need to protect online systems from automated attacks. Completely Automated Public Turing Tests to Tell Computers and Humans Apart, known as CAPTCHAs, are commonly used as one layer in providing online security. These tests are intended to be easily solvable by legitimate human users while being challenging for automated attackers to successfully complete. Traditionally, CAPTCHAs have asked users to perform tasks based on text recognition or categorization of discrete images to prove whether or not they are legitimate human users. Over time, the efficacy of these CAPTCHAs has been eroded by improved optical character recognition, image classification, and machine learning techniques that can accurately solve many CAPTCHAs at rates approaching those of humans. These CAPTCHAs can also be difficult to complete using the touch-based input methods found on widely used tablets and smartphones.;This research proposes the design of CAPTCHAs that address the shortcomings of existing implementations. These CAPTCHAs require users to perform different image-based tasks including face detection, face recognition, multimodal biometrics recognition, and object recognition to prove they are human. These are tasks that humans excel at but which remain difficult for computers to complete successfully. They can also be readily performed using click- or touch-based input methods, facilitating their use on both traditional computers and mobile devices.;Several strategies are utilized by the CAPTCHAs developed in this research to enable high human success rates while ensuring negligible automated attack success rates. One such technique, used by fgCAPTCHA, employs image quality metrics and face detection algorithms to calculate a fitness value representing the simulated performance of human users and automated attackers, respectively, at solving each generated CAPTCHA image. A genetic learning algorithm uses these fitness values to determine customized generation parameters for each CAPTCHA image. Other approaches, including gradient descent learning, artificial immune systems, and multi-stage performance-based filtering processes, are also proposed in this research to optimize the generated CAPTCHA images.;An extensive RESTful web service-based evaluation platform was developed to facilitate the testing and analysis of the CAPTCHAs developed in this research. Users recorded over 180,000 attempts at solving these CAPTCHAs using a variety of devices. The results show the designs created in this research offer high human success rates, up to 94.6\% in the case of aiCAPTCHA, while ensuring resilience against automated attacks