Biblio

Antifragile systems enhance their capabilities and become stronger when exposed to adverse conditions, stresses or attacks, making antifragility a desirable property for cyber defence systems that operate in contested military environments. Self-improvement in autonomic systems refers to the improvement of their self-* capabilities, so that they are able to (a) better handle previously known (anticipated) situations, and (b) deal with previously unknown (unanticipated) situations. In this position paper, we present a vision of using self-improvement through learning to achieve antifragility in autonomic cyber defence systems. We first enumerate some of the major challenges associated with realizing distributed self-improvement. We then propose a reference model for middleware frameworks for self-improving autonomic systems and a set of desirable features of such frameworks.

Since the performance of bus interconnects does not scale with the number of processors connected to the bus, chip multiprocessors make use of on-chip networks that implement packet switching and virtual channel flow control to efficiently transport data. In this paper, we consider the test and fault-tolerance aspects of such a network-on-chip (NoC). Past work in this area has addressed the communication efficiency and deadlock-free properties in NoC, but when routing externally received data, aspects of security must be addressed. A malicious denial-of-service attack or a power virus can be launched by a malicious external agent. We propose a two-tier solution to this problem, where a local self-test manager in each processing element runs test algorithms to detect faults in local processing element and its associated physical and virtual channels. At the global level, the health of the NoC is tested using a sorting-based algorithm proposed in this paper. Similarly, we propose to handle fault-tolerance and security concerns in routing at two levels. At the local level, each node is capable of fault-tolerant routing by deflecting packets to an alternate path; when doing so, since a chance of deadlock may be created, the local router must be capable of guestimating a deadlock situation, switch to packet-switching instead of flit-switching and attempt to reroute the packet. At the global level, a routing agent plays the role of gathering fault data and provide the fault-information to nodes that seek this information periodically. Similarly, the agent is capable of detecting malformed packets coming from an external source and prevent injecting such packets into the network, thereby conserving the network bandwidth. The agent also attempts to guess attempts at denial-of-service attacks and power viruses and will reject packets. Use of a two-tier approach helps in keeping the IP modular and reduces their complexity, thereby making them easier to verify.

Reconnaissance phase is where attackers identify their targets and how to collect information from professional social networks which can be used to select and exploit targeted employees to penetrate in an organization. Here, a framework is proposed for the early detection of attackers in the reconnaissance phase, highlighting the common characteristic behavior among attackers in professional social networks. And to create artificial honeypot profiles within the organizational social network which can be used to detect a potential incoming threat. By analyzing the dataset of social Network profiles in combination of machine learning techniques, A DspamRPfast model is proposed for the creation of a classifier system to predict the probabilities of the profiles being fake or malicious and to filter them out using XGBoost and for the faster classification and greater accuracy of 84.8%.

An information system based on real-time file integrations has an important role in today's organizations' work process management. By connecting to the network, file flow and integration between corporate systems have gained a great significance. In addition, network and security issues have emerged depending on the file structure and transfer processes. Thus, there has become a need for an effective and self-learning anomaly detection module for file transfer processes in order to provide the persistence of integration channels, accountability of transfer logs and data integrity. This paper proposes a novel anomaly detection approach that focuses on file size and integration duration of file transfers between enterprise systems. For this purpose, size and time anomalies on transferring files will be detected by a machine learning-based structure. Later, an alarm system is going to be developed in order to inform the authenticated individuals about the anomalies.

Mutriku wave farm is the first commercial plant all around the world. Since July 2011 it has been continuously selling electricity to the grid. It operates with the OWC technology and has 14 operating Wells-type turbines. In the plant there is a SCADA data recording system that collects the most important parameters of the turbines; among them, the pressure in the inlet chamber, the position of the security valve (from fully open to fully closed) and the generated power in the last 5 minutes. There is also an electricity meter which provides information about the amount of electric energy sold to the grid. The 2014 winter (January, February and March), and especially the first fortnight of February, was a stormy winter with rough sea state conditions. This was reflected both in the performance of the turbines (high pressure values, up to 9234.2 Pa; low opening degrees of the security valve, down to 49.4°; and high power generation of about 7681.6 W, all these data being average values) and in the calculated capacity factor (CF = 0.265 in winter and CF = 0.294 in February 2014). This capacity factor is a good tool for the comparison of different WEC technologies or different locations and shows an important seasonal behavior.

Open-source honeypots are a vital component in the protection of networks and the observation of trends in the threat landscape. Their open nature also enables adversaries to identify the characteristics of these honeypots in order to detect and avoid them. In this study, we investigate the prevalence of 14 open- source honeypots running more or less default configurations, making them easily detectable by attackers. We deploy 20 simple signatures and test them for false positives against servers for domains in the Alexa top 10,000, official FTP mirrors, mail servers in real operation, and real IoT devices running telnet. We find no matches, suggesting good accuracy. We then measure the Internet-wide prevalence of default open-source honeypots by matching the signatures with Censys scan data and our own scans. We discovered 19,208 honeypots across 637 Autonomous Systems that are trivially easy to identify. Concentrations are found in research networks, but also in enterprise, cloud and hosting networks. While some of these honeypots probably have no operational relevance, e.g., they are student projects, this explanation does not fit the wider population. One cluster of honeypots was confirmed to belong to a well-known security center and was in use for ongoing attack monitoring. Concentrations in an another cluster appear to be the result of government incentives. We contacted 11 honeypot operators and received response from 4 operators, suggesting the problem of lack of network hygiene. Finally, we find that some honeypots are actively abused by attackers for hosting malicious binaries. We notified the owners of the detected honeypots via their network operators and provided recommendations for customization to avoid simple signature-based detection. We also shared our results with the honeypot developers.

The integration of Internet-of-Things and pervasive computing in medical devices have made the modern healthcare system “smart.” Today, the function of the healthcare system is not limited to treat the patients only. With the help of implantable medical devices and wearables, Smart Healthcare System (SHS) can continuously monitor different vital signs of a patient and automatically detect and prevent critical medical conditions. However, these increasing functionalities of SHS raise several security concerns and attackers can exploit the SHS in numerous ways: they can impede normal function of the SHS, inject false data to change vital signs, and tamper a medical device to change the outcome of a medical emergency. In this paper, we propose HealthGuard, a novel machine learning-based security framework to detect malicious activities in a SHS. HealthGuard observes the vital signs of different connected devices of a SHS and correlates the vitals to understand the changes in body functions of the patient to distinguish benign and malicious activities. HealthGuard utilizes four different machine learning-based detection techniques (Artificial Neural Network, Decision Tree, Random Forest, k-Nearest Neighbor) to detect malicious activities in a SHS. We trained HealthGuard with data collected for eight different smart medical devices for twelve benign events including seven normal user activities and five disease-affected events. Furthermore, we evaluated the performance of HealthGuard against three different malicious threats. Our extensive evaluation shows that HealthGuard is an effective security framework for SHS with an accuracy of 91 % and an F1 score of 90 %.

In this research paper author surveys the need of data protection from intelligent systems in the private and public sectors. For this, she identifies that the Smart Information Security Intel processes needs to be the suggestive key policy for both sectors of governance either public or private. The information is very sensitive for any organization. When the government offices are concerned, information needs to be abstracted and encapsulated so that there is no information stealing. For this purposes, the art of skill set and new optimized technology needs to be stationed. Author identifies that digital bar-coded air port like security using conveyor belts and digital bar-coded conveyor boxes to scan switched ON articles like internet of things needs to be placed. As otherwise, there can potentially be data, articles or information stealing from the operational sites where access is unauthorized. Such activities shall need to be scrutinized, minutely. The biometric such as fingerprints, iris, voice and face recognition pattern updates in the virtual data tables must be taken to keep data entry-exit log up to-date. The information technicians of the sentinel systems must help catch the anomalies in the professional working time in private and public sectors if there is red flag as indicator. The author in this research paper shall discuss in detail what we shall station, how we shall station and what all measures we might need to undertake to safeguard the stealing of sensitive information from the organizations like administration buildings, government buildings, educational schools, hospitals, courts, private buildings, banks and all other offices nation-wide. The TO-BE new processes shall make the AS-IS office system more information secured, data protected and personnel security stronger.

Based on the Indonesian of Statistics the level of society people in 2019 is grow up. Based on data, the bank conducted a community to simple transaction payment in the market. Bank just used a debit card or credit card for the transaction, but the banks need more investment for infrastructure and very expensive. Based on that cause the bank needs another solution for low-cost infrastructure. Obtained from solutions that, the bank implementation QR Code Biometric authentication Payment Online is one solution that fulfills. This application used for payment in online merchant. The transaction permits in this study lie in the biometric encryption, or decryption transaction permission and QR Code Scan to improve communication security and transaction data. The test results of implementation Biometric Cloud Authentication Platform show that AES 256 agents can be implemented for face biometric encryption and decryption. Code Scan QR to carry out transaction permits with Face verification transaction permits gets the accuracy rate of 95% for 10 sample people and transaction process gets time speed of 53.21 seconds per transaction with a transaction sample of 100 times.

QoS-based connectivity coordinated by the 5GEx Multi-domain Orchestrator exploiting novel stateful BRPC is demonstrated for the first time over a multi-operator multi-technology transport network within the European 5GEx Sandbox, including Segment Routing and optical domains.

The condition of road surface has a significant role in land transportation. Due to poor road conditions, the logistics and supply chain industry face a drastic loss in their business. Unmaintained roads can cause damage to goods and accidents. The existing routing techniques do not consider factors like shock, temperature and tilt of goods etc. but these factors have to be considered for the logistics and supply chain industry. This paper proposes a recommender system which target management of goods in logistics. A 3 axis accelerometer is used to measure the road surface conditions. The pothole location is obtained using Global Positioning System (GPS). Using these details a hybrid recommender system is built. Hybrid recommender system combines multiple recommendation techniques to develop an effective recommender system. Here content-based and collaborative-based techniques is combined to build a hybrid recommender system. One of the popular Multiple Criteria Decision Making (MCDM) method, The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for content based filtering and normalised Euclidean distance and KNN algorithm is used for collaborative filtering. The best route recommended by the system will be displayed to the user using a map application.

Cloud computing is a rapid growing advanced technology which is Internet based, providing various ways for storage, resource sharing, and various features. It has brought a new way to securely store and share information and data with multiple users and groups. The cloud environment deals with many problems, and one of the most important problems in recent days is the security issues. Sharing the data in a group, in cloud conditions has turned into a blazing theme in up and coming decades. Thus the blasting interest in cloud computing, ways and measures to accomplish secure and effective information and data sharing in the cloud is a flourishing point to be engaged. In this way, the venture centers around empowering information sharing and capacity for a similar gathering inside the cloud with high security and intensity. Therefore, Honey Encryption and Advanced Encryption Standard is used for providing security for the data shared within the group by the crew members in cloud environment. In addition, an access key is provided by the Group Manager to enable access to the documents and files stored in cloud by the users for specific time period.

Continuously growing amount of research experiments using High Performance Computing (HPC) leads to the questions of research data management and in particular how to preserve a scientific experiment including all related data for long term for its future reproduction. This paper covers some challenges and possible solutions related to the preservation of scientific experiments on HPC systems and represents a concept of the preservation system for HPC computations. Storage of the experiment itself with some related data is not only enough for its future reproduction, especially in the long term. For that case preservation of the whole experiment's environment (operating system, used libraries, environment variables, input data, etc.) via containerization technology (e.g. using Docker, Singularity) is proposed. This approach allows to preserve the entire environment, but is not always possible on every HPC system because of security issues. And it also leaves a question, how to deal with commercial software that was used within the experiment. As a possible solution we propose to run a preservation process outside of the computing system on the web-server and to replace all commercial software inside the created experiment's image with open source analogues that should allow future reproduction of the experiment without any legal issues. The prototype of such a system was developed, the paper provides the scheme of the system, its main features and describes the first experimental results and further research steps.

Big Data Cyber Security Analytics (BDCA) leverages big data technologies for collecting, storing, and analyzing a large volume of security events data to detect cyber-attacks. Accuracy and response time, being the most important quality concerns for BDCA, are impacted by changes in security events data. Whilst it is promising to adapt a BDCA system's architecture to the changes in security events data for optimizing accuracy and response time, it is important to consider large search space of architectural configurations. Searching a large space of configurations for potential adaptation incurs an overwhelming adaptation time, which may cancel the benefits of adaptation. We present an adaptation approach, QuickAdapt, to enable quick adaptation of a BDCA system. QuickAdapt uses descriptive statistics (e.g., mean and variance) of security events data and fuzzy rules to (re) compose a system with a set of components to ensure optimal accuracy and response time. We have evaluated QuickAdapt for a distributed BDCA system using four datasets. Our evaluation shows that on average QuickAdapt reduces adaptation time by 105× with a competitive adaptation accuracy of 70% as compared to an existing solution.

Introduction of IoT is a big step towards the convergence of physical and virtual world as everyday objects are connected to the internet nowadays. But due to its diversity and resource constraint nature, the security of these devices in the real world has become a major challenge. Although a number of security frameworks have been suggested to ensure the security of IoT devices, frameworks for auditing this security are rare. We propose an open-source framework to audit the security of IoT devices covering hardware, firmware and communication vulnerabilities. Using existing open-source tools, we formulate a modular approach towards the implementation of the proposed framework. Standout features in the suggested framework are its modular design, extensibility, scalability, tools integration and primarily autonomous nature. The principal focus of the framework is to automate the process of auditing. The paper further mentions some tools that can be incorporated in different modules of the framework. Finally, we validate the feasibility of our framework by auditing an IoT device using proposed toolchain.

Future networks require fast information response time, scalable content distribution, security and mobility. In order to enable future Internet many key enabling technologies have been proposed such as Edge computing (EC) and Named Data Networking (NDN). In EC substantial compute and storage resources are placed at the edge of the network, in close proximity to end users. Similarly, NDN provides an alternative to traditional host centric IP architecture which seems a perfect candidate for distributed computation. Although NDN with EC seems a promising approach for enabling future Internet, it can cause various challenges such as expiry time of the Pending Interest Table (PIT) and non-trivial computation of the edge node. In this paper we discuss the expiry time and non-trivial computation in NDN based EC. We argue that if NDN is integrated in EC, then the PIT expiry time will be affected in relation with the processing time on the edge node. Our analysis shows that integrating NDN in EC without considering PIT expiry time may result in the degradation of network performance in terms of Interest Satisfaction Rate.

Travelling Ionospheric Disturbances (TIDs) are ionospheric manifestations of internal atmospheric gravity waves (AGW) in the neutral atmosphere driven by near-Earth space dynamics and by lower atmosphere phenomena. They constitute a threat for operational systems such as precise navigation (e.g., EGNOS and NRTK) and high frequency geolocation as they can impose disturbances with amplitudes of up to 20% of the ambient electron density, and Doppler frequency shifts of the order of 0.5 Hz on HF signals. The Horizon 2020 Project TechTIDE (http://techtide.space.noa.gr/) funded by the European Commission aims at designing and testing new viable TID impact mitigation strategies for the technologies affected by developing a system able to calculate in real-time the main TID characteristics (velocity, amplitude, propagation drection), to realistically specify background ionospheric conditions and to specify those ionospheric characteristics whose perturbation, because of TIDs, cause the impact in each specific technology. The TechTIDE system will contribute new understanding of the physical processes resulting in the formation of TIDs, and will consequently help to identify the drivers in the interplanetary medium, the magnetosphere and the atmosphere. This paper will provide a description of the instrumentation involved and outline the project methodologies for the identification and tracking of TIDs based on the exploitation of real-time observations from networks of Digisonde, GNSS receivers and Continuous Doppler Sounding Systems.

In this paper we propose a two-level hybrid anomalous activity detection model for intrusion detection in IoT networks. The level-1 model uses flow-based anomaly detection, which is capable of classifying the network traffic as normal or anomalous. The flow-based features are extracted from the CICIDS2017 and UNSW-15 datasets. If an anomaly activity is detected then the flow is forwarded to the level-2 model to find the category of the anomaly by deeply examining the contents of the packet. The level-2 model uses Recursive Feature Elimination (RFE) to select significant features and Synthetic Minority Over-Sampling Technique (SMOTE) for oversampling and Edited Nearest Neighbors (ENN) for cleaning the CICIDS2017 and UNSW-15 datasets. Our proposed model precision, recall and F score for level-1 were measured 100% for the CICIDS2017 dataset and 99% for the UNSW-15 dataset, while the level-2 model precision, recall, and F score were measured at 100 % for the CICIDS2017 dataset and 97 % for the UNSW-15 dataset. The predictor we introduce in this paper provides a solid framework for the development of malicious activity detection in IoT networks.

Smart Grid (SG) is regarded as complex electrical power system due to massive penetration of Renewable Energy Resources and Distribution Generations. The implementation of adjustable speed drives, advance power electronic devices, and electric arc furnaces are incorporated in SG (the transition from conventional power system). Moreover, SG is an advance, automated, controlled, efficient, digital, and intelligent system that ensures pertinent benefits, such as: (a) consumer empowerment, (b) advanced communication infrastructure, (c) user-friendly system, and (d) supports bi-directional power flow. Digital Signal Processing (DSP) is key tool for SG deployment and provides key solutions to a vast array of complex SG challenges. This research provides a comprehensive study on DSP interactions within SG. The prominent challenges posed by conventional grid, such as: (a) monitoring and control, (b) Electric Vehicles infrastructure, (c) cyber data injection attack, (d) Demand Response management and (e) cyber data injection attack are thoroughly investigated in this research.

Modern Energy Management Systems (EMS) are becoming increasingly complex in order to address the urgent issue of global energy consumption. These systems retrieve vital information from various Internet-connected resources in a smart grid to function effectively. However, relying on such resources results in them being susceptible to cyber attacks. Malicious actors can exploit the interconnections between the resources to perform nefarious tasks such as modifying critical firmware, sending bogus sensor data, or stealing sensitive information. To address this issue, we propose a novel framework that integrates PowerWatch, a solution that detects compromised devices in the smart grid with Cyber-secure Power Router (CSPR), a smart energy management system. The goal is to ascertain whether or not such a device has operated maliciously. To achieve this, PowerWatch utilizes a machine learning model that analyzes information from system and library call lists extracted from CSPR in order to detect malicious activity in the EMS. To test the efficacy of our framework, a number of unique attack scenarios were performed on a realistic testbed that comprises functional versions of CSPR and PowerWatch to monitor the electrical environment for suspicious activity. Our performance evaluation investigates the effectiveness of this first-of-its-kind merger and provides insight into the feasibility of developing future cybersecure EMS. The results of our experimental procedures yielded 100% accuracy for each of the attack scenarios. Finally, our implementation demonstrates that the integration of PowerWatch and CSPR is effective and yields minimal overhead to the EMS.

We propose a new spam detection approach based solely on meta data features gained from email headers. The approach achieves above 99 % classification accuracy on the CSDMC2010 dataset, which matches or surpasses state-of-the-art spam classifiers. We utilize a static set of engineered features, supplemented with automatically extracted features. The approach is just as effective for spam detection in end-to-end encryption, as our feature set remains unchanged for encrypted emails. In contrast to most established spam detectors, we disregard the email body completely and can therefore deliver very high classification speeds, as computationally expensive text preprocessing is not necessary.

Motivated by ubiquitous surveillance cameras in a smart city, a monitoring service can be provided to citizens. However, the rise of privacy concerns may disrupt this advanced service. Yet, the existing cloud-based services have not clearly proven that they can preserve Wth-privacy in which the relationship of three types of information, i.e., who requests the service, what the target is and where the camera is, does not leak. We address this problem by proposing a content-centric privacy platform (C2P2) that enables the construction of a Wth-privacy-preserving monitoring service without cloud dependency. C2P2 uses an image classification model of a target serving as the key to access the monitoring service specific to the target. In C2P2, communication is based on information-centric networking (ICN) that enables privacy preservation to be centered on the content itself rather than relying on a centralized system. Moreover, to preserve the privacy of bystanders, C2P2 separates the sensitive information (e.g., human faces) from the non-sensitive information (e.g., image background), while the privacy-aware forwarding strategies in C2P2 enable data aggregation and prevent privacy leakage resulting from false positive of image recognition. We evaluate the privacy leakage of C2P2 compared to that of the cloud-based system. The privacy analysis shows that, compared to the cloud-based system, C2P2 achieves a lower privacy loss ratio while reducing the communication cost significantly.

The evaluation of fault attacks on security-critical hardware implementations of cryptographic primitives is an important concern. In such regards, we have created a framework for automated construction of fault attacks on hardware realization of ciphers. The framework can be used to quickly evaluate any cipher implementations, including any optimisations. It takes the circuit description of the cipher and the fault model as input. The output of the framework is a set of algebraic equations, such as conjunctive normal form (CNF) clauses, which is then fed to a SAT solver. We consider both attacking an actual implementation of a cipher on an field-programmable gate array (FPGA) platform using a fault injector and the evaluation of an early design of the cipher using idealized fault models. We report the successful application of our hardware-oriented framework to a collection of ciphers, including the advanced encryption standard (AES), and the lightweight block ciphers LED and PRESENT. The corresponding results and a discussion of the impact to different fault models on our framework are shown. Moreover, we report significant improvements compared to similar frameworks, such as speedups or more advanced features. Our framework is the first algebraic fault attack (AFA) tool to evaluate the state-of-the art cipher LED-64, PRESENT and full-scale AES using only hardware-oriented structural cipher descriptions.

Intrusion detection system is described as a data monitoring, network activity study and data on possible vulnerabilities and attacks in advance. One of the main limitations of the present intrusion detection technology is the need to take out fake alarms so that the user can confound with the data. This paper deals with the different types of IDS their behaviour, response time and other important factors. This paper also demonstrates and brings out the advantages and disadvantages of six latest intrusion detection techniques and gives a clear picture of the recent advancements available in the field of IDS based on the factors detection rate, accuracy, average running time and false alarm rate.

This paper presents an implementation of a Graphical User Interface (GUI) for the OpenThread software. OpenThread is a software package for Thread. Thread is a networking protocol for Internet of Things (IoT) designed for home automation. OpenThread package was released by Nest Labs as an open source implementation of the Thread specification v1.1.1. The OpenThread includes IPv6, 6LoWPAN, IEEE 802.15.4 with MAC security, Mesh Link Establishment, and Mesh Routing. OpenThread includes all Thread supported device types and supports both SOC and NCP implementations. OpenThread runs on Linux and allows the users to use it as a simulator with a command line interface. This research is focused on adding a Graphical User Interface (GUI) to the OpenThread. The GUI package is implemented in TCL/Tk (Tool Control Language). OpenThread with a GUI makes working with OpenThread much easier for researchers and students. The GUI also makes it easier to visualize the Thread network and its operations.