Biblio

The security of an organization lies not only in physical buildings, but also in its information assets. Safeguarding information assets requires further study to establish optimal security mitigation steps. In determining the appropriate mitigation of information assets, both an information security risk assessment and a clear and measurable rating are required. Most risk management methods do not provide the right focus on ranking the critical information assets of an organization. This paper proposes a framework approach for ranking critical information assets. The proposed framework uses the OCTAVE Allegro method, which focuses on profiling information assets by combining ranking priority measurements using decision support system methods, such as Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP). The combined OCTAVE Allegro-SAW and OCTAVE Allegro-AHP methods are expected to better address risk priority as an input to making mitigation decisions for critical information assets. These combinations will help management to avoid missteps in adjusting budget needs allocation or time duration by selecting asset information mitigation using the ranking results of the framework.

As chips become more and more connected, they are more exposed (both to network and to physical attacks). Therefore one shall ensure they enjoy a sufficient protection level. Security within chips is accordingly becoming a hot topic. Incident detection and reporting is one novel function expected from chips. In this talk, we explain why it is worthwhile to resort to Artificial Intelligence (AI) for security event handling. Drivers are the need to aggregate multiple and heterogeneous security sensors, the need to digest this information quickly to produce exploitable information, and so while maintaining a low false positive detection rate. Key features are adequate learning procedures and fast and secure classification accelerated by hardware. A challenge is to embed such security-oriented AI logic, while not compromising chip power budget and silicon area. This talk accounts for the opportunities permitted by the symbiotic encounter between chip security and AI.

The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological and sociological theory.

Video games normally use Artificial Intelligence techniques to improve Non-Player Character (NPC) behavior, creating a more realistic experience for their players. However, rational behavior in general does not consider social interactions between player and bots. Because of that, a new framework for NPCs was proposed, which uses a social bias to mix the default strategy of finding the best possible plays to win with a analysis to decide if other players should be categorized as allies or foes. Trust and reputation models were used together to implement this demeanor. In this paper we discuss an implementation of this framework inside the game Settlers of Catan. New NPC agents are created to this implementation. We also analyze the results obtained from simulations among agents and players to conclude how the use of trust and reputation in NPCs can create a better gaming experience.

The domain name resolution system provides support service for website visits as the basic service of the Internet. With the increase of DNS attacks, it has brought copious challenges to network security. The paper studies on the key defense technologies against DNS attacks based on the DNS principle. The multi-core customized to the DNS is adopted to analyze hardware kernel, while AI algorithms being realized for malicious flow cleaning and intelligent routing running on the cloud system established specifically for DNS. The designed DNS intelligent cloud system can provide high-efficiency domain name resolution in practice, while ensuring the network security.

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose a deep neural network architecture aimed to discriminate between different users using a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we propose a feature model able to capture the typing style that is specific to each given user. The proposed approach is evaluated on a large dataset derived by integrating two real-world datasets from existing studies. The merged dataset contains a total of 1530 different users each writing a set of different typing samples. Several deep neural networks, with an increasing number of hidden layers and two different sets of features, are tested with the aim to find the best configuration. The final best classifier scores a precision equal to 0.997, a recall equal to 0.99 and an accuracy equal to 99% using an MLP deep neural network with 9 hidden layers. Finally, the performances obtained by using the deep learning approach are also compared with the performance of traditional decision-trees machine learning algorithm, attesting the effectiveness of the deep learning-based classifiers in the domain of keystroke analysis.

Recent work proposed the concept of backdoor attacks on deep neural networks (DNNs), where misclassification rules are hidden inside normal models, only to be triggered by very specific inputs. However, these "traditional" backdoors assume a context where users train their own models from scratch, which rarely occurs in practice. Instead, users typically customize "Teacher" models already pretrained by providers like Google, through a process called transfer learning. This customization process introduces significant changes to models and disrupts hidden backdoors, greatly reducing the actual impact of backdoors in practice. In this paper, we describe latent backdoors, a more powerful and stealthy variant of backdoor attacks that functions under transfer learning. Latent backdoors are incomplete backdoors embedded into a "Teacher" model, and automatically inherited by multiple "Student" models through transfer learning. If any Student models include the label targeted by the backdoor, then its customization process completes the backdoor and makes it active. We show that latent backdoors can be quite effective in a variety of application contexts, and validate its practicality through real-world attacks against traffic sign recognition, iris identification of volunteers, and facial recognition of public figures (politicians). Finally, we evaluate 4 potential defenses, and find that only one is effective in disrupting latent backdoors, but might incur a cost in classification accuracy as tradeoff.

Most of the swarm optimization techniques are inspired by the characteristics as well as behaviour of flock of birds whereas Artificial Bee Colony is based on the foraging characteristics of the bees. However, certain problems which are solved by ABC do not yield desired results in-terms of performance. ABC is a new devised swarm intelligence algorithm and predominately employed for optimization of numerical problems. The main reason for the success of ABC algorithm is that it consists of feature such as fathomable and flexibility when compared to other swarm optimization algorithms and there are many possible applications of ABC. Cloud computing has their limitation in their application and functionality. The cloud computing environment experiences several security issues such as Dos attack, replay attack, flooding attack. In this paper, an effective classifier is proposed based on Artificial Bee Colony for cloud computing. It is evident in the evaluation results that the proposed classifier achieved a higher accuracy rate.

Hiring seasonal workers in call centers to provide customer service is a common practice in B2C companies. The quality of service delivered by both contracting and employee customer service agents depends heavily on the domain knowledge available to them. When observing the internal group messaging channels used by agents, we found that similar questions are often asked repetitively by different agents, especially from less experienced ones. The goal of our work is to leverage the promising advances in conversational AI to provide a chatbot-like mechanism for assisting agents in promptly resolving a customer's issue. In this paper, we develop a neural-based conversational solution that employs BiLSTM with attention mechanism and demonstrate how our system boosts the effectiveness of customer support agents. In addition, we discuss the design principles and the necessary considerations for our system. We then demonstrate how our system, named "Isa" (Intuit Smart Agent), can help customer service agents provide a high-quality customer experience by reducing customer wait time and by applying the knowledge accumulated from customer interactions in future applications.

Building natural and conversational virtual humans is a task of formidable complexity. We believe that, especially when building agents that affectively interact with biological humans in real-time, a cognitive science-based, multilayered sensing and artificial intelligence (AI) systems approach is needed. For this demo, we show a working version (through human interaction with it) our modular system of natural, conversation 3D virtual human using AI or sensing layers. These including sensing the human user via facial emotion recognition, voice stress, semantic meaning of the words, eye gaze, heart rate, and galvanic skin response. These inputs are combined with AI sensing and recognition of the environment using deep learning natural language captioning or dense captioning. These are all processed by our AI avatar system allowing for an affective and empathetic conversation using an NLP topic-based dialogue capable of using facial expressions, gestures, breath, eye gaze and voice language-based two-way back and forth conversations with a sensed human. Our lab has been building these systems in stages over the years.

The present paper describes some of the results obtained in the Faculty of Computer Systems and Technology at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. Also is made a survey about existing hybrid methods, which are using several artificial intelligent methods for cyber defense. The paper introduces a model for intrusion detection systems where multi agent systems are the bases and artificial intelligence are applicable by the means simple real-time models constructed in laboratory environment.

Computational Intelligence (CI) has a great potential in Security & Defense (S&D) applications. Nevertheless, such potential seems to be still under exploited. In this work we first review CI applications in the maritime domain, done in the past decades by NATO Nations. Then we discuss challenges and opportunities for CI in S&D. Finally we argue that a review of the academic training of military officers is highly recommendable, in order to allow them to understand, model and solve new problems, using CI techniques.

Being able to describe a specific network as consistent is a large step towards resiliency. Next to the importance of security lies the necessity of consistency verification. Attackers are currently focusing on targeting small and crutial goals such as network configurations or flow tables. These types of attacks would defy the whole purpose of a security system when built on top of an inconsistent network. Advances in Artificial Intelligence (AI) are playing a key role in ensuring a fast responce to the large number of evolving threats. Software Defined Networking (SDN), being centralized by design, offers a global overview of the network. Robustness and adaptability are part of a package offered by programmable networking, which drove us to consider the integration between both AI and SDN. The general goal of our series is to achieve an Artificial Intelligence Resiliency System (ARS). The aim of this paper is to propose a new AI-based consistency verification system, which will be part of ARS in our future work. The comparison of different deep learning architectures shows that Convolutional Neural Networks (CNN) give the best results with an accuracy of 99.39% on our dataset and 96% on our consistency test scenario.

Recently, big data and artificial intelligence (AI) have been introduced into medical services. When personal information is stored in a shared database, that data must be encrypted, which, in turn, makes it difficult to extract only the necessary information. Searchable encryption has now been proposed to extract, or search, encrypted data without decrypting it. However, all previous studies regarding searchable encryption are software-based. This paper proposes a searchable encryption system embedded in dedicated hardware and evaluates its circuit size.

Robots are sophisticated form of IoT devices as they are smart devices that scrutinize sensor data from multiple sources and observe events to decide the best procedural actions to supervise and manoeuvre objects in the physical world. In this paper, localization of the robot is addressed by QR code Detection and path optimization is accomplished by Dijkstras algorithm. The robot can navigate automatically in its environment with sensors and shortest path is computed whenever heading measurements are updated with QR code landmark recognition. The proposed approach highly reduces computational burden and deployment complexity as it reflects the use of artificial intelligence to self-correct its course when required. An Encrypted communication channel is established over wireless local area network using SSHv2 protocol to transfer or receive sensor data(or commands) making it an IoT enabled Robot.

In Mobile Ad hoc Networks (MANET) the nodes act as a host as well as a router thereby forming a self-organizing network that does not rely upon fixed infrastructure, other than gateways to other networks. MANET provides a quick to deploy flexible networking capability with a dynamic topology due to node mobility. MANET nodes transmit, relay and receive traffic from neighbor nodes as the network topology changes. Security is important for MANET and trust computation is used to improve collaboration between nodes. MANET trust frameworks utilize real-time trust computations to maintain the trust state for nodes in the network. If the trust computation is not resilient against attack, the trust values computed could be unreliable. This paper proposes an Artificial Immune System based approach to compute trust and thereby provide a resilient reputation mechanism.

The development of Human Action Recognition (HAR) system is getting popular. This project developed a HAR system for the application in the surveillance system to minimize the man-power for providing security to the citizens such as public safety and crime prevention. In this research, deep learning network using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are used to analyze dynamic video motion of sport actions and classify different types of actions and their performance. It could classify different types of human motion with a small number of video frame for efficiency and memory saving. The current accuracy achieved is up to 92.9% but with high potential of further improvement.

SYN flood attack is a very serious cause for disturbing the normal traffic in MANET. SYN flood attack takes advantage of the congestion caused by populating a specific route with unwanted traffic that results in the denial of services. In this paper, we proposed an Adaptive Detection Mechanism using Artificial Intelligence technique named as SYN Flood Attack Detection Based on Bayes Estimator (SFADBE) for Mobile ad hoc Network (MANET). In SFADBE, every node will gather the current information of the available channel and the secure and congested free (Best Path) channel for the traffic is selected. Due to constant congestion, the availability of the data path can be the cause of SYN Flood attack. By using this AI technique, we experienced the SYN Flood detection probability more than the others did. Simulation results show that our proposed SFADBE algorithm is low cost and robust as compared to the other existing approaches.

Recently, IoT, 5G mobile, big data, and artificial intelligence are increasingly used in the real world. These technologies are based on convergenced in Cyber Physical System(Cps). Cps technology requires core technologies to ensure reliability, real-time, safety, autonomy, and security. CPS is the system that can connect between cyberspace and physical space. Cyberspace attacks are confused in the real world and have a lot of damage. The personal information that dealing in CPS has high confidentiality, so the policies and technique will needed to protect the attack in advance. If there is an attack on the CPS, not only personal information but also national confidential data can be leaked. In order to prevent this, the risk is measured using the Factor Analysis of Information Risk (FAIR) Model, which can measure risk by element for situational awareness in CPS environment. To reduce risk by preventing attacks in CPS, this paper measures risk after using the concept of Crime Prevention Through Environmental Design(CPTED).

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.

In the field of Cyber Security there has been a transition from the stage of Cyber Criminality to the stage of Cyber War over the last few years. According to the new challenges, the expert community has two main approaches: to adopt the philosophy and methods of Military Intelligence, and to use Artificial Intelligence methods for counteraction of Cyber Attacks. \cyrchar\CYRThis paper describes some of the results obtained at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. The analysis of the feasibility of various Artificial Intelligence methods has shown that a method that is equally effective for all stages of the Cyber Intelligence cannot be identified. While for Tactical Cyber Threats Intelligence has been selected and experimented a Multi-Agent System, the Recurrent Neural Networks are offered for the needs of Operational Cyber Threats Intelligence.

The field of robotics has matured using artificial intelligence and machine learning such that intelligent robots are being developed in the form of autonomous vehicles. The anticipated widespread use of intelligent robots and their potential to do harm has raised interest in their security. This research evaluates a cyberattack on the machine learning policy of an autonomous vehicle by designing and attacking a robotic vehicle operating in a dynamic environment. The primary contribution of this research is an initial assessment of effective manipulation through an indirect attack on a robotic vehicle using the Q learning algorithm for real-time routing control. Secondly, the research highlights the effectiveness of this attack along with relevant artifact issues.

This research proposes a system for detecting known and unknown Distributed Denial of Service (DDoS) Attacks. The proposed system applies two different intrusion detection approaches anomaly-based distributed artificial neural networks(ANNs) and signature-based approach. The Amazon public cloud was used for running Spark as the fast cluster engine with varying cores of machines. The experiment results achieved the highest detection accuracy and detection rate comparing to signature based or neural networks-based approach.

As drone attracts much interest, the drone industry has opened their market to ordinary people, making drones to be used in daily lives. However, as it got easier for drone to be used by more people, safety and security issues have raised as accidents are much more likely to happen: colliding into people by losing control or invading secured properties. For safety purposes, it is essential for observers and drone to be aware of an approaching drone. In this paper, we introduce a comprehensive drone detection system based on machine learning. This system is designed to be operable on drones with camera. Based on the camera images, the system deduces location on image and vendor model of drone based on machine classification. The system is actually built with OpenCV library. We collected drone imagery and information for learning process. The system's output shows about 89 percent accuracy.

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.