Biblio

The goal of this work was to identify and try to solve some of the vulnerabilities present in the AR Drone 2.0 by Parrot. The approach was to identify how the system worked, find and analyze vulnerabilities and flaws in the system as a whole and in the software, and find solutions to those problems. Analyzing the results of some tests showed that the system has an open WiFi network and the communication between the controller and the drone are unencrypted. Analyzing the Linux operating system that the drone uses, we see that "Pairing Mode" is the only way the system protects itself from unauthorized control. This is a feature that can be easily bypassed. Port scans reveal that the system has all the ports for its services open and exposed. This makes it susceptible to attacks like DoS and takeover. This research also focuses on some of the software vulnerabilities, such as Busybox that the drone runs. Lastly, this paper discuses some of the possible methods that can be used to secure the drone. These methods include securing the messages via SSH Tunnel, closing unused ports, and re-implementing the software used by the drone and the controller.

Privacy and security are the most important issues to the popularity of cloud computing service. In recent years, there are many research schemes of cloud computing privacy protection based on access control, attribute-based encryption (ABE), trust and reputation, but they are scattered and lack unified logic. In this paper, we systematically review and analyze relevant research achievements. First, we discuss the architecture, concepts and several shortcomings of cloud computing, and propose a framework of privacy protection; second, we discuss and analyze basic ABE, KP-ABE (key policy attribute-based encryption), CP-ABE (ciphertext policy attribute-based encryption), access structure, revocation mechanism, multi-authority, fine-grained, trace mechanism, proxy re-encryption (PRE), hierarchical encryption, searchable encryption (SE), trust, reputation, extension of tradition access control and hierarchical key; third, we propose the research challenge and future direction of the privacy protection in the cloud computing; finally, we point out corresponding privacy protection laws to make up for the technical deficiencies.

With the vast increase in data transmission due to a large number of information collected by devices, data management, and security has been a challenge for organizations. Many data owners (DOs) outsource their data to cloud repositories due to several economic advantages cloud service providers present. However, DOs, after their data are outsourced, do not have complete control of the data, and therefore, external systems are incorporated to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data but prove to be deficient in providing suitable solutions to the problem. In this article, we propose a secure fine-grain access control system for outsourced data, which supports read and write operations to the data. We make use of an attribute-based encryption (ABE) scheme, which is regarded as a suitable scheme to achieve access control for security and privacy (confidentiality) of outsourced data. This article considers different categories of data users, and make provisions for distinct access roles and permissible actions on the outsourced data with dynamic and efficient policy updates to the corresponding ciphertext in cloud repositories. We adopt blockchain technologies to enhance traceability and visibility to enable control over outsourced data by a DO. The security analysis presented demonstrates that the security properties of the system are not compromised. Results based on extensive experiments illustrate the efficiency and scalability of our system.

Research into the established area of ITS is evolving into the Internet of Vehicles (IoV), itself a fast-moving research area, fuelled in part by rapid changes in computing and communication technologies. Using pseudonym certificate is a popular way to address privacy issues in IoV. Therefore, the certificate management scheme is considered as a feasible technique to manage system and maintain the lifecycle of certificate. In this paper, we propose an efficient pseudonym certificate management scheme in IoV. The Blockchain concept is introduced to simplify the network structure and distributed maintenance of the Certificate Revocation List (CRL). The proposed scheme embeds part of the certificate revocation functions within the security and privacy applications, aiming to reduce the communication overhead and shorten the processing time cost. Extensive simulations and analysis show the effectiveness and efficiency of the proposed scheme, in which the Blockchain structure costs fewer network resources and gives a more economic solution to against further cybercrime attacks.

With the transformation of connected vehicles into the Internet of Vehicles (IoV), the time is now ripe for paving the way for the next generation of connected vehicles with novel applications and innovative security measures. The connected vehicles are experiencing prenominal growth in the auto industry, but are still studded with many security and privacy vulnerabilities. Today's IoV applications are part of cyber physical communication systems that collect useful information from thousands of smart sensors associated with the connected vehicles. The technology advancement has paved the way for connected vehicles to share significant information among drivers, auto manufacturers, auto insurance companies and operational and maintenance service providers for various applications. The critical issues in engineering the IoV applications are effective to use of the available spectrum and effective allocation of good channels an opportunistic manner to establish connectivity among vehicles, and the effective utilization of the infrastructure under various traffic conditions. Security and privacy in information sharing are the main concerns in a connected vehicle communication network. Blockchain technology facilitates secured communication among users in a connected vehicles network. Originally, blockchain technology was developed and employed with the cryptocurrency. Bitcoin, to provide increased trust, reliability, and security among users based on peer-to-peer networks for transaction sharing. In this paper, we propose to integrate blockchain technology into ad hoc vehicular networking so that the vehicles can share network resources with increased trust, reliability, and security using distributed access control system and can benefit a wider scope of scalable IoV applications scenarios for decision making. The proposed architecture is the faithful environment for information sharing among connected vehicles. Blockchain technology allows multiple copies of data storage at the distribution cloud. Distributed access control system is significantly more secure than a traditional centralized system. This paper also describes how important of ad hoc vehicular networking in human life, possibilities in real-world implementation and its future trends. The ad hoc vehicular networking may become one of the most trendy networking concepts in the future that has the perspective to bring out much ease human beneficial and secured applications.

Detecting process-based attacks on industrial control systems (ICS) is challenging. These cyber-attacks are designed to disrupt the industrial process by changing the state of a system, while keeping the system's behaviour close to the expected behaviour. Such anomalous behaviour can be effectively detected by an event-driven approach. Petri Net (PN) model identification has proved to be an effective method for event-driven system analysis and anomaly detection. However, PN identification-based anomaly detection methods require ICS device logs to be converted into event logs (sequence of events). Therefore, in this paper we present a formalised method for pre-processing and transforming ICS device logs into event logs. The proposed approach outperforms the previous methods of device logs processing in terms of anomaly detection. We have demonstrated the results using two published datasets.

With the proposal of the national industrial 4.0 strategy, the integration of industrial control network and Internet technology is getting higher and higher. At the same time, the closeness of industrial control networks has been broken to a certain extent, making the problem of industrial control network security increasingly serious. S7 protocol is a private protocol of Siemens Company in Germany, which is widely used in the communication process of industrial control network. In this paper, an industrial control intrusion detection model based on S7 protocol is proposed. Traditional protocol parsing technology cannot resolve private industrial control protocols, so, this model uses deep analysis algorithm to realize the analysis of S7 data packets. At the same time, in order to overcome the complexity and portability of static white list configuration, this model dynamically builds a white list through white list self-learning algorithm. Finally, a composite intrusion detection method combining white list detection and abnormal behavior detection is used to detect anomalies. The experiment proves that the method can effectively detect the abnormal S7 protocol packet in the industrial control network.

"Good Governance" - may it be corporate or governmental, is a badly needed focus area in the world today where the companies and governments are struggling to survive the political and economical turmoil around the globe. All governments around the world have a tendency of expanding the size of their government, but eventually they would be forced to think reducing the size by incorporating information technology as a way to provide services to the citizens effectively and efficiently. Hence our attempt is to offer a complete solution from birth of a citizen till death encompassing all the necessary services related to the well being of a person living in a society. Our research and analysis would explore the pros and cons of using IT as a solution to our problems and ways to implement them for a best outcome in e-Governance occasionally comparing with the present scenario when relevant.

The low attention to security and privacy causes some problems on data and information that can lead to a lack of public trust in e-Gov service. Security threats are not only included in technical issues but also non-technical issues and therefore, it needs the implementation of inclusive security. The application of inclusive security to e-Gov needs to develop a model involving security and privacy requirements as a trusted security solution. The method used is the elicitation of security and privacy requirements in a security perspective. Identification is carried out on security and privacy properties, then security and privacy relationships are determined. The next step is developing the design of an inclusive security model on e-Gov. The last step is doing an analysis of e-Gov service activities and the role of inclusive security. The results of this study identified security and privacy requirements for building inclusive security. Identification of security requirements involves properties such as confidentiality (C), integrity (I), availability (A). Meanwhile, privacy requirement involves authentication (Au), authorization (Az), and Non-repudiation (Nr) properties. Furthermore, an inclusive security design model on e-Gov requires trust of internet (ToI) and trust of government (ToG) as an e-Gov service provider. Access control is needed to provide solutions to e-Gov service activities.

With the development of new technologies in the world, governments have tendency to make a communications with people and business with the help of such technologies. Electronic government (e-government) is defined as utilizing information technologies such as electronic networks, Internet and mobile phones by organizations and state institutions in order to making wide communication between citizens, business and different state institutions. Development of e-government starts with making website in order to share information with users and is considered as the main infrastructure for further development. Website assessment is considered as a way for improving service quality. Different international researches have introduced various indexes for website assessment, they only see some dimensions of website in their research. In this paper, the most important indexes for website quality assessment based on accurate review of previous studies are "Web design", "navigation", services", "maintenance and Support", "Citizens Participation", "Information Quality", "Privacy and Security", "Responsiveness", "Usability". Considering mentioned indexes in designing the website facilitates user interaction with the e-government websites.

e-Government is needed to actualize clean, effective, transparent and accountable governance as well as quality and reliable public services. The implementation of e-Government is currently constrained because there is no derivative regulation, one of which is the regulation for e-Government Security. To answer this need, this study aims to provide input on performance management and governance systems for e-Government Security with the hope that the control design for e-Government Security can be met. The results of this study are the e-Government Security Governance System taken from 28 core models of COBIT 2019. The 28 core models were taken using CSF and risk. Furthermore, performance management for this governance system consists of capability and maturity levels which is an extension of the evaluation process in the e-Government Evaluation Guidelines issued by the Ministry of PAN & RB. The evaluation of the design carried out by determining the current condition of capability and maturity level in Badan XYZ. The result of the evaluation shows that the design possible to be implemented and needed.

Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system.

This paper presents a data-driven and physics-based method for detection of false data injection (FDI) in Smart Grids (SG). As the power grid transitions to the use of SG technology, it becomes more vulnerable to cyber-attacks like FDI. Current strategies for the detection of bad data in the grid rely on the physics based State Estimation (SE) process and statistical tests. This strategy is naturally vulnerable to undetected bad data as well as false positive scenarios, which means it can be exploited by an intelligent FDI attack. In order to enhance the robustness of bad data detection, the paper proposes the use of data-driven Machine Intelligence (MI) working together with current bad data detection via a combined Chi-squared test. Since MI learns over time and uses past data, it provides a different perspective on the data than the SE, which analyzes only the current data and relies on the physics based model of the system. This combined bad data detection strategy is tested on the IEEE 118 bus system.

Smart grids are vulnerable to cyber-attacks, which can cause significant damage and huge economic losses. Generally, state estimation (SE) is used to observe the operation of the grid. State estimation of the grid is vulnerable to false data injection attack (FDIA), so diagnosing this type of malicious attack has a major impact on ensuring reliable operation of the power system. In this paper, we present an effective FDIA detection method based on residual recurrent neural network (R2N2) prediction model and adaptive judgment threshold. Specifically, considering the data contains both linear and nonlinear components, the R2N2 model divides the prediction process into two parts: the first part uses the linear model to fit the state data; the second part predicts the nonlinearity of the residuals of the linear prediction model. The adaptive judgment threshold is inferred through fitting the Weibull distribution with the sum of squared errors between the predicted values and observed values. The thorough simulation results demonstrate that our scheme performs better than other prediction based FDIA detection schemes.

Due to the increased diversity and complexity of cyberattacks, innovative and effective analytics are needed in order to identify critical cyber incidents on a corporate network even if no ground truth data is available. This paper develops an automated system which processes a set of intrusion alerts to create behavior aggregates and then classifies these aggregates into empirical attack models through a dynamic Bayesian approach with innovative feature engineering methods. Each attack model represents a unique collective attack behavior that helps to identify critical activities on the network. Using 2017 National Collegiate Penetration Testing Competition data, it is demonstrated that the developed system is capable of generating and refining unique attack models that make sense to human, without a priori knowledge.

The last decade has witnessed an increase in online human interactions, covering all aspects of personal and professional activities. Identification of people based on their behavior rather than physical traits is a growing industry, spanning diverse spheres such as online education, e-commerce and cyber security. One prominent behavior is the expression of opinions, commonly as a reaction to images posted online. Visual aesthetic is a soft, behavioral biometric that refers to a person's sense of fondness to a certain image. Identifying individuals using their visual aesthetics as discriminatory features is an emerging domain of research. This paper introduces a new method for aesthetic feature dimensionality reduction using gene expression programming. The advantage of this method is that the resulting system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40000 images demonstrates a 94% accuracy of identity recognition based solely on users' aesthetic preferences. This outperforms the best-known method by 13.5%.

This study was conducted in order to assess the E-security behavior of students in a large higher educational institutions in the United Arab Emirates (UAE). Specifically, it sought to determine the current state of students' E-security behavior in the aspects of malware, password usage, data handling, phishing, social engineering, and online scam. An E- Security Behavior Survey Instrument (EBSI) was used to determine the status of security behavior of the participants in doing their computing activities. To complement the survey tool, focus group discussions were conducted to elicit specific experiences and insights of the participants relative to E-security. The results of the study shows that the overall E-security behavior among students in higher education in the United Arab Emirates (UAE) is moderately favorable. Specifically, the investigation reveals that the students favorably behave when it comes to phishing, social engineering, and online scam. However, they uncertainly behave on malware issues, password usage, and data handling.

We propose a novel visualization technique (Eagle-Eye) for intrusion detection, which visualizes a host as a commu- nity of system call traces in two-dimensional space. The goal of EagleEye is to visually cluster the system call traces. Although human eyes can easily perceive anomalies using EagleEye view, we propose two different methods called SAM and CPM that use the concept of data depth to help administrators distinguish between normal and abnormal behaviors. Our experimental results conducted on Australian Defence Force Academy Linux Dataset (ADFA-LD), which is a modern system calls dataset that includes new exploits and attacks on various programs, show EagleEye's efficiency in detecting diverse exploits and attacks.

Financial fraud is commonly represented by the use of illegal practices where they can intervene from senior managers until payroll employees, becoming a crime punishable by law. There are many techniques developed to analyze, detect and prevent this behavior, being the most important the fraud triangle theory associated with the classic financial audit model. In order to perform this research, a survey of the related works in the existing literature was carried out, with the purpose of establishing our own framework. In this context, this paper presents FraudFind, a conceptual framework that allows to identify and outline a group of people inside an banking organization who commit fraud, supported by the fraud triangle theory. FraudFind works in the approach of continuous audit that will be in charge of collecting information of agents installed in user's equipment. It is based on semantic techniques applied through the collection of phrases typed by the users under study for later being transferred to a repository for later analysis. This proposal encourages to contribute with the field of cybersecurity, in the reduction of cases of financial fraud.

Cybersecurity is a growing concern for private individuals and professional entities. Thereby, reports have shown that the majority of cybersecurity incidents occur because users fail to behave securely. Research on human cybersecurity (HCS) behavior suggests that time pressure is one of the important driving factors behind insecure HCS behavior. However, as our review reveals, studies on the role of time pressure in HCS are scant and there is no framework that can inform researchers and practitioners on this matter. In this paper, we present a conceptual framework consisting of contexts, psychological constructs, and boundary conditions pertaining to the role time pressure plays on HCS behavior. The framework is also validated and extended by findings from semi-structured interviews of different stakeholder groups comprising of cybersecurity experts, professionals, and general users. The framework will serve as a guideline for future studies exploring different aspects of time pressure in cybersecurity contexts and also to identify potential countermeasures for the detrimental impact of time pressure on HCS behavior.

Model validation, though a process that's continuous and complex, establishes confidence in the soundness and usefulness of a model. Making sure that the model behaves similar to the modes of behavior seen in real systems, allows the builder of said model to assure accumulation of confidence in the model and thus validating the model. While doing this, the model builder is also required to build confidence from a target audience in the model through communicating to the bases. The basis of the system dynamics model validation, both in general and in the field of cyber security, relies on a casual loop diagram of the system being agreed upon by a group of experts. Model validation also uses formal quantitative and informal qualitative tools in addition to the validation techniques used by system dynamics. Amongst others, the usefulness of a model, in a user's eyes, is a valid standard by which we can evaluate them. To validate our system dynamics cyber security model, we used empirical structural and behavior tests. This paper describes tests of model structure and model behavior, which includes each test's purpose, the ways the tests were conducted, and empirical validation results using a proof-of-concept cyber security model.

The use of biometric-based authentication systems spread over daily life consumer electronics. Over the years, researchers' interest shifted from hard (such as fingerprints, voice and keystroke dynamics) to soft biometrics (such as age, ethnicity and gender), mainly by using the latter to improve the authentication systems effectiveness. While newer approaches are constantly being proposed by domain experts, in the last years Deep Learning has raised in many computer vision tasks, also becoming the current state-of-art for several biometric approaches. However, since the automatic processing of data rich in sensitive information could expose users to privacy threats associated to their unfair use (i.e. gender or ethnicity), in the last years researchers started to focus on the development of defensive strategies in the view of a more secure and private AI. The aim of this work is to exploit Adversarial Perturbation, namely approaches able to mislead state-of-the-art CNNs by injecting a suitable small perturbation over the input image, to protect subjects against unwanted soft biometrics-based identification by automatic means. In particular, since ethnicity is one of the most critical soft biometrics, as a case of study we will focus on the generation of adversarial stickers that, once printed, can hide subjects ethnicity in a real-world scenario.

Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do not consider affective aspects to aid in trust computation. Researchers of Psychology, Neurology, Anthropology, and Computer Science argue that affective aspects are essential to human's decision-making processes. So far, there is a lack of understanding about how these aspects impact user's trust, particularly when they are inserted in an evaluation system. In this paper, we propose a trust model that accounts for personality using three personality models: Big Five, Needs, and Values. We tested our approach by extracting personality aspects from texts provided by two online human-fed evaluation systems and correlating them to reputation values. The empirical experiments show statistically significant better results in comparison to non-personality-wise approaches.

Healthcare experts and researchers have been promoting the need for IoT-based remote health monitoring systems that take care of the health of elderly people. However, such systems may generate large amounts of data, which makes the security and privacy of such data to become imperative. This paper studies the security and privacy concerns of the existing Healthcare Monitoring System (HMS) and proposes a reference architecture (security integration framework) for managing IoT-based healthcare monitoring systems that ensures security, privacy, and reliable service delivery for patients and elderly people to reduce and avoid health related risks. Our proposed framework will be in the form of state-of-the-art Security Platform, for HMS, using the emerging Software Defined Network (SDN) networking paradigm. Our proposed integration framework eliminates the dependency on specific Software or vendor for different security systems, and allows for the benefits from the functional and secure applications, and services provided by the SDN platform.

Understanding the privacy relevant characteristics of data sets, such as reidentifiability and joinability, is crucial for data governance, yet can be difficult for large data sets. While computing the data characteristics by brute force is straightforward, the scale of systems and data collected by large organizations demands an efficient approach. We present KHyperLogLog (KHLL), an algorithm based on approximate counting techniques that can estimate the reidentifiability and joinability risks of very large databases using linear runtime and minimal memory. KHLL enables one to measure reidentifiability of data quantitatively, rather than based on expert judgement or manual reviews. Meanwhile, joinability analysis using KHLL helps ensure the separation of pseudonymous and identified data sets. We describe how organizations can use KHLL to improve protection of user privacy. The efficiency of KHLL allows one to schedule periodic analyses that detect any deviations from the expected risks over time as a regression test for privacy. We validate the performance and accuracy of KHLL through experiments using proprietary and publicly available data sets.