Biblio

Finding a free path without obstacles or situation that pose minimal risk is critical for safe navigation. People who are sighted and people who are blind or visually impaired require navigation safety while walking on a sidewalk. In this paper we develop assistive navigation on a sidewalk by integrating sensory inputs using reinforcement learning. We train the reinforcement model in a simulated robotic environment which is used to avoid sidewalk obstacles. A conversational agent is built by training with real conversation data. The reinforcement learning model along with a conversational agent improved the obstacle avoidance experience about 2.5% from the base case which is 78.75%.

Attack graph is an effective way to analyze the vulnerabilities for industrial control networks. We develop a vulnerability correlation method and a practical visualization technology for industrial control network. First of all, we give a complete attack graph analysis for industrial control network, which focuses on network model and vulnerability context. Particularly, a practical attack graph algorithm is proposed, including preparing environments and vulnerability classification and correlation. Finally, we implement a three-dimensional interactive attack graph visualization tool. The experimental results show validation and verification of the proposed method.

Moving target defense (MTD) has emerged as a proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing the attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. Recently, the significant advance of software-defined networking (SDN) technology has enabled several complex system operations to be highly flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. In this paper, by leveraging the advanced SDN technology, we developed an attack graph-based MTD technique that shuffles a host's network configurations (e.g., MAC/IP/port addresses) based on its criticality, which is highly exploitable by attackers when the host is on the attack path(s). To this end, we developed a hierarchical attack graph model that provides a network's vulnerability and network topology, which can be utilized for the MTD shuffling decisions in selecting highly exploitable hosts in a given network, and determining the frequency of shuffling the hosts' network configurations. The MTD shuffling with a high priority on more exploitable, critical hosts contributes to providing adaptive, proactive, and affordable defense services aiming to minimize attack success probability with minimum MTD cost. We validated the out performance of the proposed MTD in attack success probability and MTD cost via both simulation and real SDN testbed experiments.

With current IoT architectures, once a single device in a network is compromised, it can be used to disrupt the behavior of other devices on the same network. Even though system administrators can secure critical devices in the network using best practices and state-of-the-art technology, a single vulnerable device can undermine the security of the entire network. The goal of this work is to limit the ability of an attacker to exploit a vulnerable device on an IoT network and fabricate deceitful messages to co-opt other devices. The approach is to limit attackers by using device proxies that are used to retransmit and control network communications. We present an architecture that prevents deceitful messages generated by compromised devices from affecting the rest of the network. The design assumes a centralized and trustworthy machine that can observe the behavior of all devices on the network. The central machine collects application layer data, as opposed to low-level network traffic, from each IoT device. The collected data is used to train models that capture the normal behavior of each individual IoT device. The normal behavioral data is then used to monitor the IoT devices and detect anomalous behavior. This paper reports on our experiments using both a binary classifier and a density-based clustering algorithm to model benign IoT device behavior with a realistic test-bed, designed to capture normal behavior in an IoT-monitored environment. Results from the IoT testbed show that both the classifier and the clustering algorithms are promising and encourage the use of application-level data for detecting compromised IoT devices.

Protocol detection is the process of determining the application layer protocol in the context of network security monitoring, which requires a timely and precise decision to enable protocol-specific deep packet inspection. This task has proven to be complex, as isolated characteristics, like port numbers, are not sufficient to reliably determine the application layer protocol. In this paper, we analyze the Dynamic Protocol Detection mechanisms employed by popular and widespread open-source network monitoring tools. On the example of HTTP, we show that all analyzed detection mechanisms are vulnerable to evasion attacks. This poses a serious threat to real-world monitoring operations. We find that the underlying fundamental problem of protocol disambiguation is not adequately addressed in two of three monitoring systems that we analyzed. To enable adequate operational decisions, this paper highlights the inherent trade-offs within Dynamic Protocol Detection.

Smart grid employs intelligent transmission and distribution networks for effective and reliable delivery of electricity. It uses fine-grained electrical measurements to attain optimized reliability and stability by sharing these measurements among different entities of energy management systems of the grid. There are many stakeholders like users, phasor measurement units (PMU), and other entities, with changing requirements involved in the sharing of the data. Therefore, data security plays a vital role in the correct functioning of a power grid network. In this paper, we propose an attribute-based encryption (ABE) for secure data sharing in Smart Grid architectures as ABE enables efficient and secure access control. Also, the access policy is obfuscated to preserve privacy. We use Linear Secret Sharing (LSS) Scheme for supporting any monotone access structures, thereby enhancing the expressiveness of access policies. Finally, we also analyze the security, access policy privacy and collusion resistance properties along with efficiency analysis of our cryptosystem.

In order to realize the sharing of data by multiple users on the blockchain, this paper proposes an attribute-based searchable encryption with verifiable ciphertext scheme via blockchain. The scheme uses the public key algorithm to encrypt the keyword, the attribute-based encryption algorithm to encrypt the symmetric key, and the symmetric key to encrypt the file. The keyword index is stored on the blockchain, and the ciphertext of the symmetric key and file are stored on the cloud server. The scheme uses searchable encryption technology to achieve secure search on the blockchain, uses the immutability of the blockchain to ensure the security of the keyword ciphertext, uses verify algorithm guarantees the integrity of the data on the cloud. When the user's attributes need to be changed or the ciphertext access structure is changed, the scheme uses proxy re-encryption technology to implement the user's attribute revocation, and the authority center is responsible for the whole attribute revocation process. The security proof shows that the scheme can achieve ciphertext security, keyword security and anti-collusion. In addition, the numerical results show that the proposed scheme is effective.

We study the attribution problem for deep networks applied to perception tasks. For vision tasks, attribution techniques attribute the prediction of a network to the pixels of the input image. We propose a new technique called Blur Integrated Gradients (Blur IG). This technique has several advantages over other methods. First, it can tell at what scale a network recognizes an object. It produces scores in the scale/frequency dimension, that we find captures interesting phenomena. Second, it satisfies the scale-space axioms, which imply that it employs perturbations that are free of artifact. We therefore produce explanations that are cleaner and consistent with the operation of deep networks. Third, it eliminates the need for baseline parameter for Integrated Gradients for perception tasks. This is desirable because the choice of baseline has a significant effect on the explanations. We compare the proposed technique against previous techniques and demonstrate application on three tasks: ImageNet object recognition, Diabetic Retinopathy prediction, and AudioSet audio event identification. Code and examples are at https://github.com/PAIR-code/saliency.

Authentication is a challenging and emerging issue for Fog-IoT security paradigms. The fog nodes toward large-scale end-users offer various interacted IoT services. The authentication process usually involves expressing users' personal information such as username, email, and password to the Authentication Server (AS). However, users are not intended to express their identities or information over the fog or cloud servers. Hence, we have proposed an Anonymous User Authentication Scheme for Fog-IoT (AUASF) to keep the anonymity existence of the IoT users and detect the intruders. To provide anonymity, the user can send encrypted credentials such as username, email, and mobile number through the Cloud Service Provider (CSP) for registration. IoT user receives the response with a default password and a secret Id from the CSP. After that, the IoT user submits the default password for first-time access to Fog Service Provider (FSP). The FSP assigns a One Time Password (OTP) to each user for further access. The developed scheme is equipped with hash functions, symmetric encryptions, and decryptions for security perceptions across fog that serves better than the existing anonymity schemes.

Consensus-based distributed microgrid energy management system is one of the most used distributed control strategies in the microgrid area. To improve its cybersecurity, the system needs to evaluate the trustworthiness of the participating agents in addition to the conventional cryptography efforts. This paper proposes a novel augmented reputation metric to evaluate the agents' trustworthiness in a distributed fashion. The proposed metric adopts a novel augmentation method to substantially improve the trust evaluation and attack detection performance under three typical difficult-to-detect attack patterns. The proposed metric is implemented and validated on a real-time HIL microgrid testbed.

The paper discusses the features of the automated process control system, defines the algorithm for installing critical updates. The main problems in the administration of a critical system have been identified. The paper presents a model for recognizing anomalies in the network traffic of an industrial information system using machine learning methods. The article considers the network intrusion dataset (raw TCP / IP dump data was collected, where the network was subjected to multiple attacks). The main parameters that affect the recognition of abnormal behavior in the system are determined. The basic mathematical models of classification are analyzed, their basic parameters are reviewed and tuned. The mathematical model was trained on the considered (randomly mixed) sample using cross-validation and the response was predicted on the control (test) sample, where the model should determine the anomalous behavior of the system or normal as the output. The main criteria for choosing a mathematical model for the problem to be solved were the number of correctly recognized (accuracy) anomalies, precision and recall of the answers. Based on the study, the optimal algorithm for recognizing anomalies was selected, as well as signs by which this anomaly can be recognized.

Cyber risk assessments are an essential process for analyzing and prioritizing security issues. Unfortunately, many risk assessment methodologies are marred by human subjectivity, resulting in non-repeatable, inconsistent findings. The absence of repeatable and consistent results can lead to suboptimal decision making with respect to cyber risk reduction. There is a pressing need to reduce cyber risk assessment uncertainty by using tools that use well defined inputs, producing well defined results. This paper presents Automated Vulnerability and Risk Analysis (AVRA), an end-to-end process and tool for identifying and exploiting vulnerabilities, designed for use in cyber risk assessments. The approach presented is more comprehensive than traditional vulnerability scans due to its analysis of an entire network, integrating both host and network information. AVRA automatically generates a detailed model of the network and its individual components, which is used to create an attack graph. Then, AVRA follows individual attack paths, automatically launching exploits to reach a particular objective. AVRA was successfully tested within a virtual environment to demonstrate practicality and usability. The presented approach and resulting system enhances the cyber risk assessment process through rigor, repeatability, and objectivity.

In malware behavior analysis, there are limitations in the analysis method of control flow and data flow. Researchers analyzed data flow by dynamic taint analysis tools, however, it cost a lot. In this paper, we proposed a method of generating malware summary based on semantic behavior graphs (SBGs, Semantic Behavior Graphs) to address this issue. In this paper, we considered various situation where behaviors be capable of being associated, thus an algorithm of generating semantic behavior graphs was given firstly. Semantic behavior graphs are composed of behavior nodes and associated data edges. Then, we extracted behaviors and logical relationships between behaviors from semantic behavior graphs, and finally generated a summary of malware behaviors with true intension. Experimental results showed that our approach can effectively identify and describe malicious behaviors and generate accurate behavior summary.

Modern verifiers for cryptographic protocols can analyze sophisticated designs automatically, but require the entire code of the protocol to operate. Compositional techniques, by contrast, allow us to verify each system component separately, against its own guarantees and assumptions about other components and the environment. Compositionality helps protocol design because it explains how the design can evolve and when it can run safely along other protocols and programs. For example, it might say that it is safe to add some functionality to a server without having to patch the client. Unfortunately, while compositional frameworks for protocol verification do exist, they require non-trivial human effort to identify specifications for the components of the system, thus hindering their adoption. To address these shortcomings, we investigate techniques for automated, compositional analysis of authentication protocols, using automata-learning techniques to synthesize assumptions for protocol components. We report preliminary results on the Needham-Schroeder-Lowe protocol, where our synthesized assumption was capable of lowering verification time while also allowing us to verify protocol variants compositionally.

Voice user interfaces can offer intuitive interaction with our devices, but the usability and audio quality could be further improved if multiple devices could collaborate to provide a distributed voice user interface. To ensure that users' voices are not shared with unauthorized devices, it is however necessary to design an access management system that adapts to the users' needs. Prior work has demonstrated that a combination of audio fingerprinting and fuzzy cryptography yields a robust pairing of devices without sharing the information that they record. However, the robustness of these systems is partially based on the extensive duration of the recordings that are required to obtain the fingerprint. This paper analyzes methods for robust generation of acoustic fingerprints in short periods of time to enable the responsive pairing of devices according to changes in the acoustic scenery and can be integrated into other typical speech processing tools.

The rapid deployment of digital systems into all aspects of daily life requires embedding social constructs into the digital world. Because of the complexity of these systems, there is a need for technical support to understand their actions. Social concepts, such as explainability, accountability, and responsibility rely on a notion of actual causality. Encapsulated in the Halpern and Pearl’s (HP) definition, actual causality conveniently integrates into the socio-technical world if operationalized in concrete applications. To the best of our knowledge, theories of actual causality such as the HP definition are either applied in correspondence with domain-specific concepts (e.g., a lineage of a database query) or demonstrated using straightforward philosophical examples. On the other hand, there is a lack of explicit automated actual causality theories and operationalizations for helping understand the actions of systems. Therefore, this paper proposes a unifying framework and an interactive platform (Actual Causality Canvas) to address the problem of operationalizing actual causality for different domains and purposes. We apply this framework in such areas as aircraft accidents, unmanned aerial vehicles, and artificial intelligence (AI) systems for purposes of forensic investigation, fault diagnosis, and explainable AI. We show that with minimal effort, using our general-purpose interactive platform, actual causality reasoning can be integrated into these domains.

Machine learning techniques help to understand underlying patterns in datasets to develop defense mechanisms against cyber attacks. Multilayer Perceptron (MLP) technique is a machine learning technique used in detecting attack vs. benign data. However, it is difficult to construct any effective model when there are imbalances in the dataset that prevent proper classification of attack samples in data. In this research, we use UGR'16 dataset to conduct data wrangling initially. This technique helps to prepare a test set from the original dataset to train the neural network model effectively. We experimented with a series of inputs of varying sizes (i.e. 10000, 50000, 1 million) to observe the performance of the MLP neural network model with distribution of features over accuracy. Later, we use Generative Adversarial Network (GAN) model that produces samples of different attack labels (e.g. blacklist, anomaly spam, ssh scan) for balancing the dataset. These samples are generated based on data from the UGR'16 dataset. Further experiments with MLP neural network model shows that a balanced attack sample dataset, made possible with GAN, produces more accurate results than an imbalanced one.

The 5G network solutions currently standardised and deployed do not yet enable the full potential of pervasive networking and computing envisioned in 5G initial visions: network services and slices with different QoS profiles do not span multiple operators; security, trust and automation is limited. The evolution of 5G towards a truly production-level stage needs to heavily rely on automated end-to-end network operations, use of distributed Artificial Intelligence (AI) for cognitive network orchestration and management and minimal manual interventions (zero-touch automation). All these elements are key to implement highly pervasive network infrastructures. Moreover, Distributed Ledger Technologies (DLT) can be adopted to implement distributed security and trust through Smart Contracts among multiple non-trusted parties. In this paper, we propose an initial concept of a zero-touch security and trust architecture for ubiquitous computing and connectivity in 5G networks. Our architecture aims at cross-domain security & trust orchestration mechanisms by coupling DLTs with AI-driven operations and service lifecycle automation in multi-tenant and multi-stakeholder environments. Three representative use cases are identified through which we will validate the work which will be validated in the test facilities at 5GBarcelona and 5TONIC/Madrid.

A self-governing system consisting of mobile nodes that exchange information within a cellular area and is known as a mobile ad hoc network (MANET). Due to its dynamic nature, it is vulnerable to attacks and there is no fixed infrastructure. To transfer a data packet Ad-hoc On-Demand Distance Vector (AODV) is used and it's another form of a reactive protocol. The black-hole attack is a major attack that drastically decreases the packet delivery ratio during a data transaction in a routing environment. In this attack, the attacker's node acts as the shortest path to the target node itself. If the attacker node receives the data packet from the source node, all obtained data packets are excluded from a routing network. A trust-based routing scheme is suggested to ensure secure routing. This routing scheme is divided into two stages, i.e., the Data retrieval (DR), to identify and preserve each node data transfer mechanism in a routing environment and route development stage, to predict a safe path to transmit a data packet to the target node.

Robotics Mobile Ad-hoc Networks (MANETs) are comprised of stations having mobility with no central authority and control. The stations having mobility in Robotics MANETs work as a host as well as a router. Due to the unique characteristics of Robotics MANETs such type of networks are vulnerable to different security attacks. Ad-hoc On-demand Distance Vector (AODV) is a routing protocol that belongs to the reactive category of routing protocols in Robotics MANETs. However, it is more vulnerable to the Black hole (BH) attack that is one of the most common attacks in the Robotics MANETs environment. In this attack during the route disclosure procedure a malicious station promotes itself as a most brief path to the destination as well as after that drop every one of the data gotten by the malicious station. Meanwhile the packets don't reach to its ideal goal, the BH attack turns out to be progressively escalated when a heap of malicious stations attack the system as a gathering. This research analyzed different BH finding as well as removal mechanisms for AODV routing protocol.

Application programs are a possible source of attacks to databases as attackers might exploit vulnerabilities in a privileged database application. They can perform code injection or code-reuse attack in order to steal sensitive data. However, as such attacks very often result in changes in the program's behavior, program monitoring techniques represent an effective defense to detect on-going attacks. One such technique is monitoring the library/system calls that the application program issues while running. In this paper, we propose AD-PROM, an Anomaly Detection system that aims at protecting relational database systems against malicious/compromised applications PROgraMs aiming at stealing data. AD-PROM tracks calls executed by application programs on data extracted from a database. The system operates in two phases. The first phase statically and dynamically analyzes the behavior of the application in order to build profiles representing the application's normal behavior. AD-PROM analyzes the control and data flow of the application program (i.e., static analysis), and builds a hidden Markov model trained by the program traces (i.e., dynamic analysis). During the second phase, the program execution is monitored in order to detect anomalies that may represent data leakage attempts. We have implemented AD-PROM and carried experimental activities to assess its performance. The results showed that our system is highly accurate in detecting changes in the application programs' behaviors and has very low false positive rates.

With the development of mobile internet technology, GPS technology and social software have been widely used in people's lives. The problem of big data privacy protection related to location trajectory is becoming more and more serious. The traditional location trajectory privacy protection method requires certain background knowledge and it is difficult to adapt to massive mass. Privacy protection of data. differential privacy protection technology protects privacy by attacking data by randomly perturbing raw data. The method used in this paper is to first sample the position trajectory, form the irregular polygons of the high-frequency access points in the sampling points and position data, calculate the center of gravity of the polygon, and then use the differential privacy protection algorithm to add noise to the center of gravity of the polygon to form a new one. The center of gravity, and the new center of gravity are connected to form a new trajectory. The purpose of protecting the position trajectory is well achieved. It is proved that the differential privacy protection algorithm can effectively protect the position trajectory by adding noise.

Aiming at the problem that the traditional intrusion detection method can not effectively deal with the massive and high-dimensional network traffic data of industrial control system (ICS), an ICS intrusion detection strategy based on bidirectional generative adversarial network (BiGAN) is proposed in this paper. In order to improve the applicability of BiGAN model in ICS intrusion detection, the optimal model was obtained through the single variable principle and cross-validation. On this basis, the supervised control and data acquisition (SCADA) standard data set is used for comparative experiments to verify the performance of the optimized model on ICS intrusion detection. The results show that the ICS intrusion detection method based on optimized BiGAN has higher accuracy and shorter detection time than other methods.

The object of research is information security. The tools used for research are artificial neural networks. The goal is to increase the cryptography security. The problems are: the big volume of information, the expenses for neural networks design and training. It is offered to use the neural network for the cryptographic transformation of information.

Modern industrial control systems (ICS) act as victims of cyber attacks more often in last years. These cyber attacks often can not be detected by classical information security methods. Moreover, the consequences of cyber attack's impact can be catastrophic. Since cyber attacks leads to appearance of anomalies in the ICS and technological equipment controlled by it, the task of intrusion detection for ICS can be reformulated as the task of industrial process anomaly detection. This paper considers the applicability of generative adversarial networks (GANs) in the field of industrial processes anomaly detection. Existing approaches for GANs usage in the field of information security (such as anomaly detection in network traffic) were described. It is proposed to use the BiGAN architecture in order to detect anomalies in the industrial processes. The proposed approach has been tested on Secure Water Treatment Dataset (SWaT). The obtained results indicate the prospects of using the examined method in practice.