Biblio

Mining of data is used to analyze facts to discover formerly unknown patterns, classifying and grouping the records. There are several crucial scalable statistics mining platforms that have been developed in latest years. RapidMiner is a famous open source software which can be used for advanced analytics, Weka and Orange are important tools of machine learning for classifying patterns with techniques of clustering and regression, whilst Knime is often used for facts preprocessing like information extraction, transformation and loading. This article encapsulates the most important and robust platforms.

Wireless network sensors are outsized number of pocket sized sensors deployed in the area under surveillance. The sensor network is very sensitive to unattended and remote Environment with a wide variety of applications in the agriculture, health, industry there a lot of challenges being faced with respect to the energy, mobility, security. The paper presents with regard to the context based surrounding information which has location privacy to the source node against an adversary who sees the network at a whole so a correlation strategy is proposed for providing the privacy.

In this letter, we proposed a novel scheme for the realization of scalable and flexible semi-quantum secret sharing between a boss and multiple dynamic agent groups. In our scheme, the boss Alice can not only distribute her secret messages to multiple users, but also can dynamically adjust the number of users and user groups based on the actual situation. Furthermore, security analysis demonstrates that our protocol is secure against both external attack and participant attack. Compared with previous schemes, our protocol is more flexible and practical. In addition, since our protocol involving only single qubit measurement that greatly weakens the hardware requirements of each user.

This paper presents a study on detecting cyber attacks on industrial control systems (ICS) using convolutional neural networks. The study was performed on a Secure Water Treatment testbed (SWaT) dataset, which represents a scaled-down version of a real-world industrial water treatment plant. We suggest a method for anomaly detection based on measuring the statistical deviation of the predicted value from the observed value. We applied the proposed method by using a variety of deep neural network architectures including different variants of convolutional and recurrent networks. The test dataset included 36 different cyber attacks. The proposed method successfully detected 31 attacks with three false positives thus improving on previous research based on this dataset. The results of the study show that 1D convolutional networks can be successfully used for anomaly detection in industrial control systems and outperform recurrent networks in this setting. The findings also suggest that 1D convolutional networks are effective at time series prediction tasks which are traditionally considered to be best solved using recurrent neural networks. This observation is a promising one, as 1D convolutional neural networks are simpler, smaller, and faster than the recurrent neural networks.

The safety of industrial control systems (ICS) depends not only on comprehensive solutions for protecting information, but also on the timing and closure of vulnerabilities in the software of the ICS. The investigation of security incidents in the ICS is often greatly complicated by the fact that malicious software functions only within the computer's volatile memory. Obtaining the contents of the volatile memory of an attacked computer is difficult to perform with a guaranteed reliability, since the data collection procedure must be based on a reliable code (the operating system or applications running in its environment). The paper proposes a new instrumental method for obtaining the contents of volatile memory, general rules for implementing the means of collecting information stored in memory. Unlike software methods, the proposed method has two advantages: firstly, there is no problem in terms of reading the parts of memory, blocked by the operating system, and secondly, the resulting contents are not compromised by such malicious software. The proposed method is relevant for investigating security incidents of ICS and can be used in continuous monitoring systems for the security of ICS.

Industrial control systems are the fundamental infrastructures of a country. Since the intrusion attack methods for industrial control systems have become complex and concealed, the traditional protection methods, such as vulnerability database, virus database and rule matching cannot cope with the attacks hidden inside the terminals of industrial control systems. In this work, we propose a control flow anomaly detection algorithm based on the control flow of the business programs. First, a basic group partition method based on key paths is proposed to reduce the performance burden caused by tabbed-assert control flow analysis method through expanding basic research units. Second, the algorithm phases of standard path set acquisition and path matching are introduced. By judging whether the current control flow path is deviating from the standard set or not, the abnormal operating conditions of industrial control can be detected. Finally, the effectiveness of a control flow anomaly detection (checking) algorithm based on Path Matching (CFCPM) is demonstrated by anomaly detection ability analysis and experiments.

The paper is devoted to analysis of condition of executing devices and sensors of Industrial Control Systems information security. The work contains structures of industrial control systems divided into groups depending on system's layer. The article contains the analysis of analog interfaces work and work features of data transmission protocols in industrial control system field layer. Questions about relevance of industrial control systems information security, both from the point of view of the information security occurring incidents, and from the point of view of regulators' reaction in the form of normative legal acts, are described. During the analysis of the information security systems of industrial control systems a possibility of leakage through technical channels of information leakage at the field layer was found. Potential vectors of the attacks on devices of field layer and data transmission network of an industrial control system are outlined in the article. The relevance analysis of the threats connected with the attacks at the field layer of an industrial control system is carried out, feature of this layer and attractiveness of this kind of attacks is observed.

The authors have proposed the Fallback Control System (FCS) as a countermeasure after cyber-attacks happen in Industrial Control Systems (ICSs). For increased robustness against cyber-attacks, introducing multiple countermeasures is desirable. Then, an appropriate collaboration is essential. This paper introduces two FCSs in ICS: field network signal is driven FCS and analog signal driven FCS. This paper also implements a collaborative FCS by a collaboration function of the two FCSs. The collaboration function is that the analog signal driven FCS estimates the state of the other FCS. The collaborative FCS decides the countermeasure based on the result of the estimation after cyber-attacks happen. Finally, we show practical experiment results to analyze the effectiveness of the proposed method.

Aiming at the problem that there is little research on firmware vulnerability mining and the traditional method of vulnerability mining based on fuzzing test is inefficient, this paper proposed a new method of mining vulnerabilities in industrial control system firmware. Based on taint analysis technology, this method can construct test cases specifically for the variables that may trigger vulnerabilities, thus reducing the number of invalid test cases and improving the test efficiency. Experiment result shows that this method can reduce about 23 % of test cases and can effectively improve test efficiency.

With the increasing inter-connection of operation technology to the IT network, the security threat to the Industrial Control System (ICS) is increasing daily. Therefore, it is critical to utilize formal verification technique such as model checking to mathematically prove the correctness of security and safety requirements in the controller logic before it is deployed on the field. However, model checking requires considerable effort for regular ICS users and control technician to verify properties. This paper, provides a simpler approach to the model checking of temperature process control system by first starting with the control module design without formal verification. Second, identifying possible vulnerabilities in such design. Third, verifying the safety and security properties with a formal method. 

Industrial control systems (ICS) are key enabling systems that drive the productivity and efficiency of omnipresent industries such as power, gas, water treatment, transportation, and manufacturing. These systems consist of interconnected components that communicate over industrial networks using industrial protocols such as the Common Industrial Protocol (CIP). CIP is one of the most commonly used network-based process control protocols, and utilizes an object-oriented communication structure for device to device interaction. Due to this object-oriented structure, CIP communication reveals detailed information about the devices, the communication patterns, and the system, providing an in-depth view of the system. The details from this in-depth system perspective can be utilized as part of a system cybersecurity or discovery approach. However, due to the variety of commands, corresponding parameters, and variable layer structure of the CIP network layer, processing this layer is a challenging task. This paper presents a tool, Advanced CIP Evaluator (ACE), which passively processes the CIP communication layer and automatically extracts device, communication, and system information from observed network traffic. ACE was tested and verified using a representative ICS power generation testbed. Since ACE operates passively, without generating any network traffic of its own, system operations are not disturbed. This novel tool provides ICS information, such as networked devices, communication patterns, and system operation, at a depth and breadth that is unique compared with other known tools.

Most forensic investigators are trained to recognize abusive network behavior in conventional information systems, but they may not know how to detect anomalous traffic patterns in industrial control systems (ICS) that manage critical infrastructure services. We have developed and laboratory-tested hands-on teaching material to introduce students to forensics investigation of intrusions on an industrial network. Rather than using prototypes of ICS components, our approach utilizes commercial industrial products to provide students a more realistic simulation of an ICS network. The lessons cover four different types of attacks and the corresponding post-incident network data analysis.

Due to the wide implementation of communication networks, industrial control systems are vulnerable to malicious attacks, which could cause potentially devastating results. Adversaries launch integrity attacks by injecting false data into systems to create fake events or cover up the plan of damaging the systems. In addition, the complexity and nonlinearity of control systems make it more difficult to detect attacks and defense it. Therefore, a novel security situation awareness framework based on particle filtering, which has good ability in estimating state for nonlinear systems, is proposed to provide an accuracy understanding of system situation. First, a system state estimation based on particle filtering is presented to estimate nodes state. Then, a voting scheme is introduced into hazard situation detection to identify the malicious nodes and a local estimator is constructed to estimate the actual system state by removing the identified malicious nodes. Finally, based on the estimated actual state, the actual measurements of the compromised nodes are predicted by using the situation prediction algorithm. At the end of this paper, a simulation of a continuous stirred tank is conducted to verify the efficiency of the proposed framework and algorithms.

Digital twins open up new possibilities in terms of monitoring, simulating, optimizing and predicting the state of cyber-physical systems (CPSs). Furthermore, we argue that a fully functional, virtual replica of a CPS can also play an important role in securing the system. In this work, we present a framework that allows users to create and execute digital twins, closely matching their physical counterparts. We focus on a novel approach to automatically generate the virtual environment from specification, taking advantage of engineering data exchange formats. From a security perspective, an identical (in terms of the system's specification), simulated environment can be freely explored and tested by security professionals, without risking negative impacts on live systems. Going a step further, security modules on top of the framework support security analysts in monitoring the current state of CPSs. We demonstrate the viability of the framework in a proof of concept, including the automated generation of digital twins and the monitoring of security and safety rules.

This paper studies the set similarity join problem with overlap constraints which, given two collections of sets and a constant c, finds all the set pairs in the datasets that share at least c common elements. This is a fundamental operation in many fields, such as information retrieval, data mining, and machine learning. The time complexity of all existing methods is O(n2) where n is the total size of all the sets. In this paper, we present a size-aware algorithm with the time complexity of O(n2-over 1 c k1 over 2c)=o(n2)+O(k), where k is the number of results. The size-aware algorithm divides all the sets into small and large ones based on their sizes and processes them separately. We can use existing methods to process the large sets and focus on the small sets in this paper. We develop several optimization heuristics for the small sets to improve the practical performance significantly. As the size boundary between the small sets and the large sets is crucial to the efficiency, we propose an effective size boundary selection algorithm to judiciously choose an appropriate size boundary, which works very well in practice. Experimental results on real-world datasets show that our methods achieve high performance and outperform the state-of-the-art approaches by up to an order of magnitude.

The blockchain emerges as an innovative tool that has the potential to positively impact the way we design a number of online applications today. In many ways, the blockchain technology is, however, still not mature enough to cater for industrial standards. Namely, existing Byzantine tolerant permission-based blockchain deployments can only scale to a limited number of nodes. These systems typically require that all transactions (and their order of execution) are publicly available to all nodes in the system, which comes at odds with common data sharing practices in the industry, and prevents a centralized regulator from overseeing the full blockchain system. In this paper, we propose a novel blockchain architecture devised specifically to meet industrial standards. Our proposal leverages the notion of satellite chains that can privately run different consensus protocols in parallel - thereby considerably boosting the scalability premises of the system. Our solution also accounts for a hands-off regulator that oversees the entire network, enforces specific policies by means of smart contracts, etc. We implemented our solution and integrated it with Hyperledger Fabric v0.6.

This paper considers the problem of running a long-term on-demand service for executing actively-secure computations. We examined state-of-the-art tools and implementations for actively-secure computation and identified a set of key features indispensable to offer meaningful service like this. Since no satisfactory tools exist for the purpose, we developed Pool, a new tool for building and executing actively-secure computation protocols at extreme scales with nearly zero offline delay. With Pool, we are able to obliviously execute, for the first time, reactive computations like ORAM in the malicious threat model. Many technical benefits of Pool can be attributed to the concept of pool-based cut-and-choose. We show with experiments that this idea has significantly improved the scalability and usability of JIMU, a state-of-the-art LEGO protocol.

In this position paper we describe how mutation testing can be used to evaluate the quality of test suites from a security viewpoint. Our focus is on measuring the quality of the test suite associated with the Java Development Kit (JDK) because it provides the core security properties for all applications. We describe the challenges associated with identifying security-specific mutation operators that are specific to the Java model and ensuring that our solution can be automated for large code-bases like the JDK.

Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. Although SMC is instrumental in such distributed settings, it does not provide any guarantees not to leak any information about individuals to adversaries. Differential privacy (DP) can be utilized to address this; however, achieving SMC with DP is not a trivial task, either. In this paper, we propose a novel Secure Multiparty Distributed Differentially Private (SM-DDP) protocol to achieve secure and private computations in a multiparty environment. Specifically, with our protocol, we simultaneously achieve SMC and DP in distributed settings focusing on linear regression on horizontally distributed data. That is, parties do not see each others’ data and further, can not infer information about individuals from the final constructed statistical model. Any statistical model function that allows independent calculation of local statistics can be computed through our protocol. The protocol implements homomorphic encryption for SMC and functional mechanism for DP to achieve the desired security and privacy guarantees. In this work, we first introduce the theoretical foundation for the SM-DDP protocol and then evaluate its efficacy and performance on two different datasets. Our results show that one can achieve individual-level privacy through the proposed protocol with distributed DP, which is independently applied by each party in a distributed fashion. Moreover, our results also show that the SM-DDP protocol incurs minimal computational overhead, is scalable, and provides security and privacy guarantees.

As smart grid systems become increasingly reliant on networks of control devices, attacks on their inherent security vulnerabilities could lead to catastrophic system failures. Network Intrusion Detection Systems(NIDS) detect such attacks by learning traffic patterns and finding anomalies in them. However, availability of data for robust training and evaluation of NIDS is rare due to associated operational and security risks of sharing such data. Consequently, we present Melody, a scalable framework for synthesizing such datasets. Melody models both, the cyber and physical components of the smart grid by integrating a simulated physical network with an emulated cyber network while using virtual time for high temporal fidelity. We present a systematic approach to generate traffic representing multi-stage attacks, where each stage is either emulated or recreated with a mechanism to replay arbitrary packet traces. We describe and evaluate the suitability of Melodys datasets for intrusion detection, by analyzing the extent to which temporal accuracy of pertinent features is maintained.

Security issues in vehicular communication have become a huge concern to safeguard increasing applications. A group signature is one of the popular authentication approaches for VANETs (Vehicular ad hoc networks) which can be implemented to secure the vehicular communication. However, securely distributing group keys to fast-moving vehicular nodes is still a challenging problem. In this paper, we propose an efficient key management protocol for group signature based authentication, where a group is extended to a domain with multiple road side units. Our scheme not only provides a secure way to deliver group keys to vehicular nodes, but also ensures security features. The experiment results show that our key distribution scheme is a scalable, efficient and secure solution to vehicular networking.

In today's world, we are surrounded by variety of computer vision applications e.g. medical imaging, bio-metrics, security, surveillance and robotics. Most of these applications require real time processing of a single image or sequence of images. This real time image/video processing requires high computational power and specialized hardware architecture and can't be achieved using general purpose CPUs. In this paper, a FPGA based generic canny edge detector is introduced. Edge detection is one of the basic steps in image processing, image analysis, image pattern recognition, and computer vision. We have implemented a re-sizable canny edge detector IP on programmable logic (PL) of PYNQ-Platform. The IP is integrated with HDMI input/output blocks and can process 1080p input video stream at 60 frames per second. As mentioned the canny edge detection IP is scalable with respect to frame size i.e. depending on the input frame size, the hardware architecture can be scaled up or down by changing the template parameters. The offloading of canny edge detection from PS to PL causes the CPU usage to drop from about 100% to 0%. Moreover, hardware based edge detector runs about 14 times faster than the software based edge detector running on Cortex-A9 ARM processor.

Security challenges are the most important obstacles for the advancement of IT-based on-demand services and cloud computing as an emerging technology. Lack of coincidence in identity management models based on defined policies and various security levels in different cloud servers is one of the most challenging issues in clouds. In this paper, a policy- based user authentication model has been presented to provide a reliable and scalable identity management and to map cloud users' access requests with defined polices of cloud servers. In the proposed schema several components are provided to define access policies by cloud servers, to apply policies based on a structural and reliable ontology, to manage user identities and to semantically map access requests by cloud users with defined polices. Finally, the reliability and efficiency of this policy-based authentication schema have been evaluated by scientific performance, security and competitive analysis. Overall, the results show that this model has met defined demands of the research to enhance the reliability and efficiency of identity management in cloud computing environments.

Graph analysis can capture relationships between network entities and can be used to identify and rank anomalous hosts, users, or applications from various types of cyber logs. It is often the case that the data in the logs can be represented as a bipartite graph (e.g. internal IP-external IP, user-application, or client-server). State-of-the-art graph based anomaly detection often generalizes across all types of graphs — namely bipartite and non-bipartite. This confounds the interpretation and use of specific graph features such as degree, page rank, and eigencentrality that can provide a security analyst with situational awareness and even insights to potential attacks on enterprise scale networks. Furthermore, graph algorithms applied to data collected from large, distributed enterprise scale networks require accompanying methods that allow them to scale to the data collected. In this paper, we provide a novel, scalable, directional graph projection framework that operates on cyber logs that can be represented as bipartite graphs. We also present methodologies to further narrow returned results to anomalous/outlier cases that may be indicative of a cyber security event. This framework computes directional graph projections and identifies a set of interpretable graph features that describe anomalies within each partite.

The Internet of Things (IoT) devices perform security-critical operations and deal with sensitive information in the IoT-based systems. Therefore, the increased deployment of smart devices will make them targets for cyber attacks. Adversaries can perform malicious actions, leak private information, and track devices' and their owners' location by gaining unauthorized access to IoT devices and networks. However, conventional security protocols are not primarily designed for resource constrained devices and therefore cannot be applied directly to IoT systems. In this paper, we propose Boot-IoT - a privacy-preserving, lightweight, and scalable security scheme for limited resource devices. Boot-IoT prevents a malicious device from joining an IoT network. Boot-IoT enables a device to compute a unique identity for authentication each time the device enters a network. Moreover, during device to device communication, Boot-IoT provides a lightweight mutual authentication scheme that ensures privacy-preserving identity usages. We present a detailed analysis of the security strength of BootIoT. We implemented a prototype of Boot-IoT on IoT devices powered by Contiki OS and provided an extensive comparative analysis of Boot-IoT with contemporary authentication methods. Our results show that Boot-IoT is resource efficient and provides better scalability compared to current solutions.