Biblio

Insider threats pose a challenge to all companies and organizations. Identification of culprit after an attack is often too late and result in detrimental consequences for the organization. Majority of past research on insider threat has focused on post-hoc personality analysis of known insider threats to identify personality vulnerabilities. It has been proposed that certain personality vulnerabilities place individuals to be at risk to perpetuating insider threats should the environment and opportunity arise. To that end, this study utilizes a game-based approach to simulate a scenario of intellectual property theft and investigate behavioral and personality differences of individuals who exhibit insider-threat related behavior. Features were extracted from games, text collected through implicit and explicit measures, simultaneous facial expression recordings, and personality variables (HEXACO, Dark Triad and Entitlement Attitudes) calculated from questionnaire. We applied ensemble machine learning algorithms and show that they produce an acceptable balance of precision and recall. Our results showcase the possibility of harnessing personality variables, facial expressions and linguistic features in the modeling and prediction of insider-threat.

In this paper, we consider ways of organizing group authentication, as well as the features of constructing the isogeny of elliptic curves. The work includes the study of isogeny graphs and their application in postquantum systems. A hierarchical group authentication scheme has been developed using transformations based on the search for isogeny of elliptic curves.

Text-based CAPTCHAs are still commonly used to attempt to prevent automated access to web services. By displaying an image of distorted text, they attempt to create a challenge image that OCR software can not interpret correctly, but a human user can easily determine the correct response to. This work focuses on a CAPTCHA used by a popular Chinese language question-and-answer website and how resilient it is to modern machine learning methods. While the majority of text-based CAPTCHAs focus on transcription tasks, the CAPTCHA solved in this work is based on localization of inverted symbols in a distorted image. A convolutional neural network (CNN) was created to evaluate the likelihood of a region in the image belonging to an inverted character. It is used with a feature map and clustering to identify potential locations of inverted characters. Training of the CNN was performed using curriculum learning and compared to other potential training methods. The proposed method was able to determine the correct response in 95.2% of cases of a simulated CAPTCHA and 67.6% on a set of real CAPTCHAs. Potential methods to increase difficulty of the CAPTCHA and the success rate of the automated solver are considered.

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for website to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA, improve CAPTCHA technology, it can also promote the technologies of license plate recognition and handwriting recognition. This paper proposed a method based on Convolutional Neural Network (CNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over-fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion.

Software agents represent an assured computing paradigm that tends to emerge to be an elegant technology to solve present day problems. The eminent Scientific Community has proved us with the usage or implementation of software agent's usage approach that simplifies the proposed solution in various types to solve the traditional computing problems arise. The proof of the same is implemented in several applications that exist based on this area of technology where the software agents have maximum benefits but on the same hand absence of the suitable security mechanisms that endures for systems that are based on representation of barriers exists in the paradigm with respect to present day industry. As the application proposing present security mechanisms is not a trivial one as the agent based system builders or developers who are not often security experts as they subsequently do not count on the area of expertise. This paper presents a novel approach for protecting the infrastructure for solving the issues considered to be malicious host in mobile agent system by implementing a secure protocol to migrate agents from host to host relying in various elements based on the enhanced Trusted Platforms Modules (TPM) for processing data. We use enhanced extension to the Java Agent Development framework (JADE) in our proposed system and a migrating protocol is used to validate the proposed framework (AVASPA).

A Network consists of many nodes among which there may be a presence of misbehavior nodes. Delay Tolerant Network (DTN) is a network where the disconnections occur frequently. Store, carry and forward method is followed in DTN. The serious threat against routing in DTN is the selfish behavior. The main intention of selfish node is to save its own energy. Detecting the selfish node in DTN is very difficult. In this paper, a probabilistic misbehavior detection scheme called MAXTRUST has been proposed. Trusted Authority (TA) has been introduced in order to detect the behavior of the nodes periodically based on the task, forwarding history and contact history evidence. After collecting all the evidences from the nodes, the TA would check the inspection node about its behavior. The actions such as punishment or compensation would be given to that particular node based on its behavior. The TA performs probabilistic checking, in order to ensure security at a reduced cost. To further improve the efficiency, dynamic probabilistic inspection has been demonstrated using game theory analysis. The simulation results show the effectiveness and efficiency of the MAXTRUST scheme.

Transitioning to more open architectures has been making Cyber-Physical Systems (CPS) vulnerable to malicious attacks that are beyond the conventional cyber attacks. This paper studies attack-resilience enhancement for a system under emerging attacks in the environment of the controller. An effective way to address this problem is to make system state estimation accurate enough for control regardless of the compromised components. This work follows this way and develops a procedure named CPS checkpointing and recovery, which leverages historical data to recover failed system states. Specially, we first propose a new concept of physical-state recovery. The essential operation is defined as rolling the system forward starting from a consistent historical system state. Second, we design a checkpointing protocol that defines how to record system states for the recovery. The protocol introduces a sliding window that accommodates attack-detection delay to improve the correctness of stored states. Third, we present a use case of CPS checkpointing and recovery that deals with compromised sensor measurements. At last, we evaluate our design through conducting simulator-based experiments and illustrating the use of our design with an unmanned vehicle case study.

The growing pervasiveness of Internet of Things (IoT) expands the attack surface by connecting more and more attractive attack targets, i.e. embedded devices, to the Internet. One key component in securing these devices is software integrity checking, which typically attained with Remote Attestation (RA). RA is realized as an interactive protocol, whereby a trusted party, verifier, verifies the software integrity of a potentially compromised remote device, prover. In the vast majority of IoT applications, smart devices operate in swarms, thus triggering the need for efficient swarm attestation schemes.In this paper, we present WISE, the first intelligent swarm attestation protocol that aims to minimize the communication overhead while preserving an adequate level of security. WISE depends on a resource-efficient smart broadcast authentication scheme where devices are organized in fine-grained multi-clusters, and whenever needed, the most likely compromised devices are attested. The candidate devices are selected intelligently taking into account the attestation history and the diverse characteristics (and constraints) of each device in the swarm. We show that WISE is very suitable for resource-constrained embedded devices, highly efficient and scalable in heterogenous IoT networks, and offers an adjustable level of security.

Among the threats to information systems of state institutions, enterprises and financial organizations of particular importance are those originating from organized criminal groups that specialize in obtaining unauthorized access to the computer information protected by law. Criminal groups often possess a material base including financial, technical, human and other resources that allow to perform targeted attacks on information resources as secretly as possible. The principal features of such targeted attacks are the use of software created or modified specifically for use in illegal purposes with respect to specific organizations. Due to these circumstances, the detection of such attacks is quite difficult, and their prevention is even more complicated. In this regard, the task of identifying and analyzing such threats is very relevant. One effective way to solve it is to implement the Honeypot system, which allows to research the strategy and tactics of the attackers. In the present article, there is proposed the original architecture of the Honeypot system designed to study targeted attacks on information systems of criminogenic objects. The architectural design includes such basic elements as the functional component, the registrar of events occurring in the system and the protector. The key features of the proposed Honeypot system are considered, and the functional purpose of its main components is described. The proposed system can find its application in providing information security of institutions, organizations and enterprises, it can be used in the development of information security systems.

eAssessment uses technology to support online evaluation of students' knowledge and skills. However, challenging problems must be addressed such as trustworthiness among students and teachers in blended and online settings. The TeSLA system proposes an innovative solution to guarantee correct authentication of students and to prove the authorship of their assessment tasks. Technologically, the system is based on the integration of five instruments: face recognition, voice recognition, keystroke dynamics, forensic analysis, and plagiarism. The paper aims to analyze and compare the results achieved after the second pilot performed in an online and a blended university revealing the realization of trust-driven solutions for eAssessment.

Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cybersecurity threats and attacks by utilizing data mining techniques in the field of Artificial Intelligence. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection utilizing machine learning algorithms for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.

Current methods for producing and disseminating analytic products contribute to the latency of relaying actionable information and analysis to the U.S. Intelligence Community's (IC's) principal customers, U.S. policymakers and warfighters. To circumvent these methods, which can often serve as a bottleneck, we report on the results of a public prize challenge that explored the potential for artificial intelligence techniques to generate useful analytic products. The challenge tasked solvers to develop algorithms capable of searching and processing nearly 15,000 unstructured text files into a 1-2 page analytic product without human intervention; these analytic products were subsequently evaluated and scored using established IC methodologies and criteria. Experimental results from this challenge demonstrate the promise for the ma-chine generation of analytic products to ensure that the IC warns and informs in a more timely fashion.

Wireless communication based on optical spectrum has been a promising technology to support increasing bandwidth demand in the recent years. Light fidelity, optical camera communication, visible light communication, underwater optical wireless communication, free space optical communication are such technologies those have been already deployed to support the challenges in wireless communications. Those technologies create massive data traffic as lots of infrastructures and servers are connected with the internet. Software defined optical wireless networks have been introduced in this paper as a solution to this phenomenon. An architecture has been designed where we provide the general software defined networking (SDN) structure and describe the possible tasks which can be performed by the SDN for optical wireless communication.

In this paper, we propose a set of caching strategies for big active data (BAD) systems. BAD is a data management paradigm that allows ingestion of massive amount of data from heterogeneous sources, such as sensor data, social networks, web and crowdsourced data in a large data cluster consisting of many computing and storage nodes, and enables a very large number of end users to subscribe to those data items through declarative subscriptions. A set of distributed broker nodes connect these end users to the backend data cluster, manage their subscriptions and deliver the subscription results to the end users. Unlike the most traditional publish-subscribe systems that match subscriptions against a single stream of publications to generate notifications, BAD can match subscriptions across multiple publications (by leveraging storage in the backend) and thus can enrich notifications with a rich set of diverse contents. As the matched results are delivered to the end users through the brokers, the broker node caches the results for a while so that the subscribers can retrieve them with reduced latency. Interesting research questions arise in this context so as to determine which result objects to cache or drop when the cache becomes full (eviction-based caching) or to admit objects with an explicit expiration time indicating how much time they should reside in the cache (TTL based caching). To this end, we propose a set of caching strategies for the brokers and show that the schemes achieve varying degree of efficiency in terms of notification delivery in the BAD system. We evaluate our schemes via a prototype implementation and through detailed simulation studies.

The software defined networking framework facilitates flexible and reliable internet of things networks by moving the network intelligence to a centralized location while enabling low power wireless network in the edge. In this paper, we present SD-WSN6Lo, a novel software-defined wireless management solution for 6LoWPAN networks that aims to reduce the management complexity in WSN's. As an example of the technique, a simulation of controlling the power consumption of sensor nodes is presented. The results demonstrate improved energy consumption of approximately 15% on average per node compared to the baseline condition.

Automatic speaker verification systems are increasingly used as the primary means to authenticate costumers. Recently, it has been proposed to train speaker verification systems using end-to-end deep neural models. In this paper, we show that such systems are vulnerable to adversarial example attacks. Adversarial examples are generated by adding a peculiar noise to original speaker examples, in such a way that they are almost indistinguishable, by a human listener. Yet, the generated waveforms, which sound as speaker A can be used to fool such a system by claiming as if the waveforms were uttered by speaker B. We present white-box attacks on a deep end-to-end network that was either trained on YOHO or NTIMIT. We also present two black-box attacks. In the first one, we generate adversarial examples with a system trained on NTIMIT and perform the attack on a system that trained on YOHO. In the second one, we generate the adversarial examples with a system trained using Mel-spectrum features and perform the attack on a system trained using MFCCs. Our results show that one can significantly decrease the accuracy of a target system even when the adversarial examples are generated with different system potentially using different features.

Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to a black-box attack, which is a more realistic scenario. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We develop novel scoring strategies to find the most important words to modify such that the deep classifier makes a wrong prediction. Simple character-level transformations are applied to the highest-ranked words in order to minimize the edit distance of the perturbation. We evaluated DeepWordBug on two real-world text datasets: Enron spam emails and IMDB movie reviews. Our experimental results indicate that DeepWordBug can reduce the classification accuracy from 99% to 40% on Enron and from 87% to 26% on IMDB. Our results strongly demonstrate that the generated adversarial sequences from a deep-learning model can similarly evade other deep models.

As a new research hotspot in the field of artificial intelligence, deep reinforcement learning (DRL) has achieved certain success in various fields such as robot control, computer vision, natural language processing and so on. At the same time, the possibility of its application being attacked and whether it have a strong resistance to strike has also become a hot topic in recent years. Therefore, we select the representative Deep Q Network (DQN) algorithm in deep reinforcement learning, and use the robotic automatic pathfinding application as a countermeasure application scenario for the first time, and attack DQN algorithm against the vulnerability of the adversarial samples. In this paper, we first use DQN to find the optimal path, and analyze the rules of DQN pathfinding. Then, we propose a method that can effectively find vulnerable points towards White-Box Q table variation in DQN pathfinding training. Finally, we build a simulation environment as a basic experimental platform to test our method, through multiple experiments, we can successfully find the adversarial examples and the experimental results show that the supervised method we proposed is effective.

As robotic capabilities improve and robots become more capable as team members, a better understanding of effective human-robot teaming is needed. In this paper, we investigate failures by robots in various team configurations in space EVA operations. This paper describes the methodology of extending and the application of Work Models that Compute (WMC), a computational simulation framework, to model robot failures, interruptions, and the resolutions they require. Using these models, we investigate how different team configurations respond to a robot's failure to correctly complete the task and overall mission. We also identify key factors that impact the teamwork metrics for team designers to keep in mind while assembling teams and assigning taskwork to the agents. We highlight different metrics that these failures impact on team performance through varying components of teaming and interaction that occur. Finally, we discuss the future implications of this work and the future work to be done to investigate function allocation in human-robot teams.

To improve customer experience, datacenter operators offer support for simplifying application and resource management. For example, running workloads of workflows on behalf of customers is desirable, but requires increasingly more sophisticated autoscaling policies, that is, policies that dynamically provision resources for the customer. Although selecting and tuning autoscaling policies is a challenging task for datacenter operators, so far relatively few studies investigate the performance of autoscaling for workloads of workflows. Complementing previous knowledge, in this work we propose the first comprehensive performance study in the field. Using trace-based simulation, we compare state-of-the-art autoscaling policies across multiple application domains, workload arrival patterns (e.g., burstiness), and system utilization levels. We further investigate the interplay between autoscaling and regular allocation policies, and the complexity cost of autoscaling. Our quantitative study focuses not only on traditional performance metrics and on state-of-the-art elasticity metrics, but also on time-and memory-related autoscaling-complexity metrics. Our main results give strong and quantitative evidence about previously unreported operational behavior, for example, that autoscaling policies perform differently across application domains and allocation and provisioning policies should be co-designed.

Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on real users. In this vision paper, we propose a new research area of eXplainable AI for Designers (XAID), specifically for game designers. By focusing on a specific user group, their needs and tasks, we propose a human-centered approach for facilitating game designers to co-create with AI/ML techniques through XAID. We illustrate our initial XAID framework through three use cases, which require an understanding both of the innate properties of the AI techniques and users' needs, and we identify key open challenges.

Recently, the armed forces want to bring the Internet of Things technology to improve the effectiveness of military operations in battlefield. So the Internet of Battlefield Things (IoBT) has entered our view. And due to the high processing latency and low reliability of the “combat cloud” network for IoBT in the battlefield environment, in this paper , a novel “combat cloud-fog” network architecture for IoBT is proposed. The novel architecture adds a fog computing layer which consists of edge network equipment close to the users in the “combat-cloud” network to reduce latency and enhance reliability. Meanwhile, since the computing capability of the fog equipment are weak, it is necessary to implement distributed computing in the “combat cloud-fog” architecture. Therefore, the distributed computing load balancing problem of the fog computing layer is researched. Moreover, a distributed generalized diffusion strategy is proposed to decrease latency and enhance the stability and survivability of the “combat cloud-fog” network system. The simulation result indicates that the load balancing strategy based on generalized diffusion algorithm could decrease the task response latency and support the efficient processing of battlefield information effectively, which is suitable for the “combat cloud- fog” network architecture.

With all data services of cloud, it's not only stored the data, although shared the data among the multiple users or clients, which make doubt in its integrity due to the existence of software/hardware error along with human error too. There is an existence of several mechanisms to allow data holders and public verifiers to precisely, efficiently and effectively audit integrity of cloud data without accessing the whole data from server. After all, public auditing on the integrity of shared data with pervious extant mechanisms will somehow affirm the confidential information and its identity privacy to the public verifiers. In this paper, to achieve the privacy preserving public for auditing, we intended an explanation for TPA using three way handshaking protocol through the Extensible Authentication Protocol (EAP) with liberated encryption standard. Appropriately, from the cloud, we use the VerifyProof execute by TPA to audit to certify. In addition to this mechanism, the identity of each segment in the shared data is kept private from the public verifiers. Moreover, rather than verifying the auditing task one by one, this will capable to perform, the various auditing tasks simultaneously.

This article examines Usage of Game Theory in The Internet Wide Scan. There is compiled model of “Network Scanning” game. There is described process of players interaction in the coalition antagonistic and network games. The concept of target system's cost is suggested. Moreover, there is suggested its application in network scanning, particularly, when detecting honeypot/honeynet systems.

In typical Wireless Sensor Network (WSN) applications, the sensor nodes deployed are constrained both in computational and energy resources. For this reason, simple communication protocols are usually employed along with shortrange multi-hop topologies. In this paper, we challenge this notion and propose a structure that employs more robust (and naturally more complex) forward-error correction schemes in multi-hop extended star topologies. We demonstrate using simulation and real-world data based on popular WSN platforms that this approach can actually reduce the overall energy consumption of the nodes by significant margins (from 40 to 70%) compared to traditional WSN schemes that do not support sophisticated communication mechanisms and it is feasible to implement it economically without relying on expensive hardware.