Biblio

Recently, it is predicted that interworking between heterogeneous devices will be accelerated due to the openness of the IoT (Internet of Things) platform, but various security threats are also expected to increase. However, most IoT open platforms remain at the level that utilizes existing security technologies. Therefore, a more secure security technology is required to prevent illegal copying and leakage of important data through stealing, theft, and hacking of IoT devices. In addition, a technique capable of ensuring interoperability with existing standard technologies is required. This paper proposes an IoT device authentication method based on PUF (Physical Unclonable Function) that operates on an IoT open platform. By utilizing PUF technology, the proposed method can effectively respond to the threat of exposure of the authentication key of the existing IoT open platform. Above all, the proposed method can contribute to compatibility and interoperability with existing technologies by providing a device authentication method that can be effectively applied to the OCF Iotivity standard specification, which is a representative IoT open platform.

Smart phones and mobile apps have become an essential part of our daily lives. It is necessary to ensure the quality of these apps. Two important aspects of code quality are maintainability and security. The goals of my PhD project are (1) to study code smells, security issues and their evolution in iOS apps and frameworks, (2) to enhance training and teaching using visualisation support, and (3) to support developers in automatically detecting dependencies to vulnerable library elements in their apps. For each of the three tools, dedicated tool support will be provided, i.e., GraphifyEvolution, VisualiseEvolution, and DependencyEvolution respectively. The tool GraphifyEvolution exists and has been applied to analyse code smells in iOS apps written in Swift. The tool has a modular architecture and can be extended to add support for additional languages and external analysis tools. In the remaining two years of my PhD studies, I will complete the other two tools and apply them in case studies with developers in industry as well as in university teaching.

Increasing incidents of cyber attacks and evolution of quantum computing poses challenges to secure existing information and communication technologies infrastructure. In recent years, quantum key distribution (QKD) is being extensively researched, and is widely accepted as a promising technology to realize secure networks. Optical fiber networks carry a huge amount of information, and are widely deployed around the world in the backbone terrestrial, submarine, metro, and access networks. Thus, instead of using separate dark fibers for quantum communication, integration of QKD with the existing classical optical networks has been proposed as a cost-efficient solution, however, this integration introduces new research challenges. In this paper, we do a comprehensive survey of the state-of-the-art QKD secured optical networks, which is going to shape communication networks in the coming decades. We elucidate the methods and protocols used in QKD secured optical networks, and describe the process of key establishment. Various methods proposed in the literature to address the networking challenges in QKD secured optical networks, specifically, routing, wavelength and time-slot allocation (RWTA), resiliency, trusted repeater node (TRN) placement, QKD for multicast service, and quantum key recycling are described and compared in detail. This survey begins with the introduction to QKD and its advantages over conventional encryption methods. Thereafter, an overview of QKD is given including quantum bits, basic QKD system, QKD schemes and protocol families along with the detailed description of QKD process based on the Bennett and Brassard-84 (BB84) protocol as it is the most widely used QKD protocol in the literature. QKD system are also prone to some specific types of attacks, hence, we describe the types of quantum hacking attacks on the QKD system along with the methods used to prevent them. Subsequently, the process of point-to-point mechanism of QKD over an optical fiber link is described in detail using the BB84 protocol. Different architectures of QKD secured optical networks are described next. Finally, major findings from this comprehensive survey are summarized with highlighting open issues and challenges in QKD secured optical networks.

A Quick Response (QR) Code is a type of barcode that can be read by the digital devices and which stores the information in a square-shaped. The QR Code readers can extract data from the patterns which are presented in the QR Code matrix. A QR Code can be acting as an attack vector that can harm indirectly. In such case a QR Code can carry malicious or phishing URLs and redirect users to a site which is well conceived by the attacker and pretends to be an authorized one. Once the QR Code is decoded the commands are triggered and executed, causing damage to information, operating system and other possible sequence the attacker expects to gain. In this paper, a new model for QR Code authentication and phishing detection has been presented. The proposed model will be able to detect the phishing and malicious URLs in the process of the QR Code validation as well as to prevent the user from validating it. The development of this application will help to prevent users from being tricked by the harmful QR Codes.

The prevalence of Internet of Things (IoT) allows heterogeneous and lightweight smart devices to collaboratively provide services with or without human intervention. With an ever-increasing presence of IoT-based smart applications and their ubiquitous visibility from the Internet, user data generated by highly connected smart IoT devices also incur more concerns on security and privacy. While a lot of efforts are reported to develop lightweight information assurance approaches that are affordable to resource-constrained IoT devices, there is not sufficient attention paid from the aspect of security solutions against hardware-oriented attacks, i.e. side channel attacks. In this paper, a COTS (commercial off-the-shelf) based Randomized Switched-Mode Voltage Regulation System (RSMVRS) is proposed to prevent power analysis based side channel attacks (P-SCA) on bare metal IoT edge device. The RSMVRS is implemented to direct power to IoT edge devices. The power is supplied to the target device by randomly activating power stages with random time delays. Therefore, the cryptography algorithm executing on the IoT device will not correlate to a predictable power profile, if an adversary performs a SCA by measuring the power traces. The RSMVRS leverages COTS components and experimental study has verified the correctness and effectiveness of the proposed solution.

Security of physical layer key generation is based on the randomness and reciprocity of wireless fading channel, which has attracted more and more attention in recent years. This paper proposes a rate adaptive key agreement scheme and utilizes the received signal strength (RSS) of the channel between two wireless devices to generate the key. In conventional information reconciliation process, the bit inconsistency rate is usually eliminated by using the filter method, which increases the possibility of exposing the generated key bit string. Building on the strengths of existing secret key extraction approaches, this paper develops a scheme that uses Reed-Solomon (RS) codes, one of forward error correction channel codes, for information reconciliation. Owing to strong error correction performance of RS codes, the proposed scheme can solve the problem of inconsistent key bit string in the process of channel sensing. At the same time, the composition of RS codes can help the scheme realize rate adaptation well due to the construction principle of error correction code, which can freely control the code rate and achieve the reconciliation method of different key bit string length. Through experiments, we find that when the number of inconsistent key bits is not greater than the maximum error correction number of RS codes, it can well meet the purpose of reconciliation.

Internet of Things (IoT) networks are often attacked to compromise the security and privacy of application data and disrupt the services offered by them. The attacks are being launched at different layers of IoT protocol stack by exploiting their inherent weaknesses. Forensic investigations need substantial artifacts and datasets to support the decisions taken during analysis and while attributing the attack to the adversary. Network provenance plays a crucial role in establishing the relationships between network entities. Hence IoT networks can be made forensic ready so that network provenance may be collected to help in constructing these artifacts. The paper proposes Ready-IoT, a novel forensic readiness model for IoT environment to collect provenance from the network which comprises of both network parameters and traffic. A link layer dataset, Link-IoT Dataset is also generated by querying provenance graphs. Finally, Link-IoT dataset is compared with other IoT datasets to draw a line of difference and applicability to IoT environments. We believe that the proposed features have the potential to detect the attacks performed on the IoT network.

In recent times, attackers are continuously developing advanced techniques for evading security, stealing personal financial data, Intellectual Property (IP) and sensitive information. These attacks often employ multiple attack vectors for gaining initial access to the systems. Analysts are often challenged to identify malware objective, initial attack vectors, attack propagation, evading techniques, protective mechanisms and unseen techniques. Most of these attacks are frequently referred to as Multi stage attacks and pose a grave threat to organizations, individuals and the government. Early multistage attack detection is a crucial measure to counter malware and deactivate it. Most traditional security solutions use signature-based detection, which frequently fails to thwart zero-day attacks. Manual analysis of these samples requires enormous effort for effectively counter exponential growth of malware samples. In this paper, we present a novel approach leveraging Machine Learning and MITRE Adversary Tactic Technique and Common knowledge (ATT&CK) framework for early multistage attack detection in real time. Firstly, we have developed a run-time engine that receives notification while malicious executable is downloaded via browser or a launch of a new process in the system. Upon notification, the engine extracts the features from static executable for learning if the executable is malicious. Secondly, we use the MITRE ATT&CK framework, evolved based on the real-world observations of the cyber attacks, that best describes the multistage attack with respect to the adversary Tactics, Techniques and Procedure (TTP) for detecting the malicious executable as well as predict the stages that the malware executes during the attack. Lastly, we propose a real-time system that combines both these techniques for early multistage attack detection. The proposed model has been tested on 6000 unpacked malware samples and it achieves 98 % accuracy. The other major contribution in this paper is identifying the Windows API calls for each of the adversary techniques based on the MITRE ATT&CK.

Modern power grids are dependent on communication systems for data collection, visualization, and control. Distributed Network Protocol 3 (DNP3) is commonly used in supervisory control and data acquisition (SCADA) systems in power systems to allow control system software and hardware to communicate. To study the dependencies between communication network security, power system data collection, and industrial hardware, it is important to enable communication capabilities with real-time power system simulation. In this paper, we present the integration of new functionality of a power systems dynamic simulation package into our cyber-physical power system testbed that supports real-time power system data transfer using DNP3, demonstrated with an industrial real-time automation controller (RTAC). The usage and configuration of DNP3 with real-world equipment in to achieve power system monitoring and control of a large-scale synthetic electric grid via this DNP3 communication is presented. Then, an exemplar of DNP3 data collection and control is achieved in software and hardware using the 2000-bus Texas synthetic grid.

Internet of Battlefield Things (IoBT) is the application of Internet of Things (IoT) to a battlefield environment. IoBT networks operate in difficult conditions due to high mobility and unpredictable nature of battle fields and securing them is a challenge. There is increasing interest to use deception techniques to enhance the security of IoBT networks. A honeypot is a system installed on a network as a trap to attract the attention of an attacker and it does not store any valuable data. In this work, we introduce IoBT dual sensor gateways. We propose a Reinforcement Learning (RL)-assisted scheme, in which the IoBT dual sensor gateways intelligently switch between honeypot and real function based on a threshold. The optimal threshold is determined using reinforcement learning approach that adapts to nodes reputation. To focus on the impact of the mobile and uncertain behavior of IoBT networks on the proposed scheme, we consider the nodes as moving vehicles. We statistically analyze the results of our RL-based scheme obtained using ns-3 network simulation, and optimize value of the threshold.

Service workers boost the user experience of modern web applications by taking advantage of the Cache API to improve responsiveness and support offline usage. In this paper, we present the first security analysis of the threats posed by this programming practice, identifying an attack with major security implications. In particular, we show how a traditional XSS attack can abuse the Cache API to escalate into a personin-the-middle attack against cached content, thus compromising its confidentiality and integrity. Remarkably, this attack enables new threats which are beyond the scope of traditional XSS. After defining the attack, we study its prevalence in the wild, finding that the large majority of the sites which register service workers using the Cache API are vulnerable as long as a single webpage in the same origin of the service worker is affected by an XSS. Finally, we propose a browser-side countermeasure against this attack, and we analyze its effectiveness and practicality in terms of security benefits and backward compatibility with existing web applications.

Service workers boost the user experience of modern web applications by taking advantage of the Cache API to improve responsiveness and support offline usage. In this paper, we present the first security analysis of the threats posed by this programming practice, identifying an attack with major security implications. In particular, we show how a traditional XSS attack can abuse the Cache API to escalate into a personin-the-middle attack against cached content, thus compromising its confidentiality and integrity. Remarkably, this attack enables new threats which are beyond the scope of traditional XSS. After defining the attack, we study its prevalence in the wild, finding that the large majority of the sites which register service workers using the Cache API are vulnerable as long as a single webpage in the same origin of the service worker is affected by an XSS. Finally, we propose a browser-side countermeasure against this attack, and we analyze its effectiveness and practicality in terms of security benefits and backward compatibility with existing web applications.

Vulnerabilities in the source code of software are critical issues in the realm of software engineering. Coping with vulnerabilities in software source code is becoming more challenging due to several aspects of complexity and volume. Deep learning has gained popularity throughout the years as a means of addressing such issues. In this paper, we propose an evaluation of vulnerability detection performance on source code representations and evaluate how Machine Learning (ML) strategies can improve them. The structure of our experiment consists of 3 Deep Neural Networks (DNNs) in conjunction with five different source code representations; Abstract Syntax Trees (ASTs), Code Gadgets (CGs), Semantics-based Vulnerability Candidates (SeVCs), Lexed Code Representations (LCRs), and Composite Code Representations (CCRs). Experimental results show that employing different ML strategies in conjunction with the base model structure influences the performance results to a varying degree. However, ML-based techniques suffer from poor performance on class imbalance handling when used in conjunction with source code representations for software vulnerability detection.

With the rapid development of the Internet, network traffic is becoming more complex and diverse. At the same time, malicious traffic is growing. This seriously threatens the security of networks and information. However, the current DPI (Deep Packet Inspect) engine based on x86 architecture is slow in monitoring speed, which cannot meet the needs. Generally, two factors affect the detection rate: CPU and memory; The efficiency of data packet acquisition, and multi regular expression matching. Under these circumstances, this paper presents an efficient implementation of the DPI engine based on a generic x86 platform. DPDK is used as the platform of network data packets acquisition and processing. Using the multi-queue of the NIC (network interface controller) and the customized symmetric RSS key, the network traffic is divided and reorganized in the form of conversation. The core of traffic identification is hyperscan, which uses a flow pattern to match the packets load of a single conversation efficiently. It greatly reduces memory requirements. The method makes full use of the system resources and takes into account the advantages of high efficiency of hardware implementation. And it has a remarkable improvement in the efficiency of recognition.

The intelligent connected vehicle industry has entered a period of opportunity, industry data is accumulating rapidly, and the formulation of industry standards to regulate big data management and application is imminent. As the basis of data security, data classification has received unprecedented attention. By combing through the research and development status of data classification in various industries, this article combines industry characteristics and re-examines the framework of industry data classification from the aspects of information security and data assetization, and tries to find the balance point between data security and data value. The intelligent networked automobile industry provides support for big data applications, this article combines the characteristics of the connected vehicle industry, re-examines the data characteristics of the intelligent connected vehicle industry from the 2 aspects as information security and data assetization, and eventually proposes a scene-based hierarchical framework. The framework includes the complete classification process, model, and quantifiable parameters, which provides a solution and theoretical endorsement for the construction of a big data automatic classification system for the intelligent connected vehicle industry and safe data open applications.

With the development of openness, sharing and interconnection of computer network, the architecture of enterprise network becomes more and more complex, and various network security problems appear. Threat Intelligence(TI) Analysis and situation awareness(SA) are the prediction and analysis technology of enterprise security risk, while intrusion detection technology belongs to active defense technology. In order to ensure the safe operation of computer network system, we must establish a multi-level and comprehensive security system. This paper analyzes many security risks faced by enterprise computer network, and integrates threat intelligence analysis, security situation assessment, intrusion detection and other technologies to build a comprehensive enterprise security system to ensure the security of large enterprise network.

Current power sensitive data security protection adopts classification and grading protection. Company classification and grading are mainly in formulating specifications. Data classification and grading processing is carried out manually, which is heavy and time-consuming, while traditional data identification mainly relies on rules for data identification, the level of automation and intelligence is low, and there are many problems in recognition accuracy. Data classification and classification is the basis of data security protection. Sensitive data identification is the key to data classification and classification, and it is also the first step to achieve accurate data security protection. This paper proposes an intelligent identification and tracking technology of sensitive data for electric power big data, which can improve the ability of data classification and classification, help the realization of data classification and classification, and provide support for the accurate implementation of data security capabilities.

With the increasing scale of network, the scale of attack graph has been becoming larger and larger, and the number of nodes in attack graph is also increasing, which can not directly reflect the impact of nodes on the whole system. Therefore, in this paper, a method was proposed to determine the key nodes of network attack graph of power information physical system to solve the problem of uncertain emphasis of security protection of attack graph.

The application of intelligent video terminal has spread in all aspects of production and life, such as urban transportation, enterprises, hospitals, banks, and families. In recent years, intelligent video terminals, video recorders and other video monitoring system components are frequently exposed to high risks of security vulnerabilities, which is likely to threaten the privacy of users and data security. Therefore, it is necessary to strengthen the security research and testing of intelligent video terminals, and formulate reinforcement and protection strategies based on the evaluation results, in order to ensure the confidentiality, integrity and availability of data collected and transmitted by intelligent video terminals.

As an important part of the intelligent measurement system, IOT terminal is in the “edge” layer of the intelligent measurement system architecture. It is the key node of power grid management and cloud fog integration. Its information security is the key to the construction of the security system of intelligent measurement, and the security link between the cloud and sensor measurement. With the in-depth integration of energy flow, information flow and business flow, and the in-depth application of digital technologies such as cloud computing, big data, internet of things, mobile Internet and artificial intelligence, the transformation and development of power system to digital and high-quality digital power grid has been accelerated. As a typical multi-dimensional complex system combining physical space and information space, the security threats and risks faced by the digital grid are more complex. The security risks in the information space will transfer the hazards to the power system and physical space. The Internet of things terminal is facing a more complex situation in the security field than before. This paper studies the feasibility of the security monitoring technology of the Internet of things terminal, in order to reduce the potential risks, improve the safe operation environment of the Internet of things terminal and improve the level of the security protection of the Internet of things terminal. One is to study the potential security problems of Internet of things terminal, and put forward the technical specification of security protection of Internet of things terminal. The second is to study the Internet of things terminal security detection technology, research and develop terminal security detection platform, and realize the unified detection of terminal security protection. The third is to study the security monitoring technology of the Internet of things terminal, develop the security monitoring system of the Internet of things terminal, realize the terminal security situation awareness and threat identification, timely discover the terminal security vulnerabilities, and ensure the stable and safe operation of the terminal and related business master station.

Performance measures commonly used in systems security engineering tend to be static, linear, and have limited utility in addressing challenges to security performance from increasingly complex risk environments, adversary innovation, and disruptive technologies. Leveraging key concepts from resilience science offers an opportunity to advance next-generation systems security engineering to better describe the complexities, dynamism, and nonlinearity observed in security performance—particularly in response to these challenges. This article introduces a multilayer network model and modified Continuous Time Markov Chain model that explicitly captures interdependencies in systems security engineering. The results and insights from a multilayer network model of security for a hypothetical nuclear power plant introduce how network-based metrics can incorporate resilience concepts into performance metrics for next generation systems security engineering.

Federated Learning (FL) is a multiparty learning computing approach that can aid privacy-preservation machine learning. However, FL has several potential security and privacy threats. First, the existing FL requires a central coordinator for the learning process which brings a single point of failure and trust issues for the shared trained model. Second, during the learning process, intentionally unreliable model updates performed by Byzantine colluding parties can lower the quality and convergence of the shared ML models. Therefore, discovering verifiable local model updates (i.e., integrity or correctness) and trusted parties in FL becomes crucial. In this paper, we propose a resilient and verifiable FL algorithm based on a reputation scheme to cope with unreliable parties. We develop a selection algorithm for task publisher and blockchain-based multiparty learning architecture approach where local model updates are securely exchanged and verified without the central party. We also proposed a novel auditing scheme to ensure our proposed approach is resilient up to 50% Byzantine colluding attack in a malicious scenario.

Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This article is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this article, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

Cognitive radio (CR) as a key technology of solving the problem of low spectrum utilization has attracted wide attention in recent years. However, due to the open nature of the radio, the communication links can be eavesdropped by illegal user, resulting to severe security threat. Unmanned aerial vehicle (UAV) equipped with signal sensing and data transmission module, can access to the unoccupied channel to improve network security performance by transmitting artificial noise (AN) in CR networks. In this paper, we propose a resource allocation scheme for UAV-assisted overlay CR network. Based on the result of spectrum sensing, the UAV decides to play the role of jammer or secondary transmitter. The power splitting ratio for transmitting secondary signal and AN is introduced to allocate the UAV's transmission power. Particularly, we jointly optimize the spectrum sensing time, the power splitting ratio and the hovering position of the UAV to maximize the total secrecy rate of primary and secondary users. The optimization problem is highly intractable, and we adopt an adaptive inertia coefficient particle swarm optimization (A-PSO) algorithm to solve this problem. Simulation results show that the proposed scheme can significantly improve the total secrecy rate in CR network.

{On considering workplace thefts as a major problem, there is a requirement of designing a vandal proof door hardware and locking mechanism for ensuring the security of our property. So the door lock system with extra security features with a user friendly cost is suggested in this paper. When a stranger comes at the door, he/she has to pass three security levels for unlocking the solenoid locks present at the door and if he fails to do so, the door will remain locked. These three levels are of three extraordinary security features as one of them is using Fingerprint sensor, second is using a knocking pattern, and the last lock is unlocked by the preset pin/pattern entered by the user. Since, in addition to these features, there is one more option for the case of appearing of guest at the door and that is the Image capturing using web-camera present at the door and here the owner of the house is able to unlock all the locks if he wants the guest to enter the home. This all will be monitored by Node MCU}.