Biblio

Cyber threats have increased extensively during the last decade, especially in smart grids. Cybercriminals have become more sophisticated. Current security controls are not enough to defend networks from the number of highly skilled cybercriminals. Cybercriminals have learned how to evade the most sophisticated tools, such as Intrusion Detection and Prevention Systems (IDPS), and Advanced Persistent Threat (APT) is almost invisible to current tools. Fortunately, the application of Artificial Intelligence (AI) may increase the detection rate of IDPS systems, and Machine Learning (ML) techniques can mine data to detect different attack stages of APT. However, the implementation of AI may bring other risks, and cybersecurity experts need to find a balance between risk and benefits.

The huge volume, variety, and velocity of big data have empowered Machine Learning (ML) techniques and Artificial Intelligence (AI) systems. However, the vast portion of data used to train AI systems is sensitive information. Hence, any vulnerability has a potentially disastrous impact on privacy aspects and security issues. Nevertheless, the increased demands for high-quality AI from governments and companies require the utilization of big data in the systems. Several studies have highlighted the threats of big data on different platforms and the countermeasures to reduce the risks caused by attacks. In this paper, we provide an overview of the existing threats which violate privacy aspects and security issues inflicted by big data as a primary driving force within the AI/ML workflow. We define an adversarial model to investigate the attacks. Additionally, we analyze and summarize the defense strategies and countermeasures of these attacks. Furthermore, due to the impact of AI systems in the market and the vast majority of business sectors, we also investigate Standards Developing Organizations (SDOs) that are actively involved in providing guidelines to protect the privacy and ensure the security of big data and AI systems. Our far-reaching goal is to bridge the research and standardization frame to increase the consistency and efficiency of AI systems developments guaranteeing customer satisfaction while transferring a high degree of trustworthiness.

Artificial Intelligence (AI) has attracted a large amount of attention in recent years. However, several new problems, such as privacy violations, security issues, or effectiveness, have been emerging. Differential privacy has several attractive properties that make it quite valuable for AI, such as privacy preservation, security, randomization, composition, and stability. Therefore, this paper presents differential privacy mechanisms for multi-agent systems, reinforcement learning, and knowledge transfer based on those properties, which proves that current AI can benefit from differential privacy mechanisms. In addition, the previous usage of differential privacy mechanisms in private machine learning, distributed machine learning, and fairness in models is discussed, bringing several possible avenues to use differential privacy mechanisms in AI. The purpose of this paper is to deliver the initial idea of how to integrate AI with differential privacy mechanisms and to explore more possibilities to improve AIs performance.

Artificial Intelligence (AI) can and does use individual's data to make predictions about their wants, their needs, their influences on them and predict what they could do. The use of individual's data naturally raises privacy concerns. This article focuses on AI, the privacy issue against the backdrop of the endless growth of the Digital Universe where Big Data, AI, Data Analytics and 5G Technology live and grow in The Internet of Things (IoT).

The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases.

With the development of artificial intelligence (AI), multimedia privacy issues have become more challenging than ever. AI-assisted malicious entities can steal private information from multimedia data more easily than humans. Traditional multimedia privacy protection only considers the situation when humans are the adversaries, therefore they are ineffective against AI-assisted attackers. In this paper, we develop a new framework and new algorithms that can protect image privacy from both humans and AI. We combine the idea of adversarial image perturbation which is effective against AI and the obfuscation technique for human adversaries. Experiments show that our proposed methods work well for all types of attackers.

In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis.

Recently in the vast advancement of Artificial Intelligence, Machine learning and Deep Neural Network (DNN) driven us to the robust applications. Such as Image processing, speech recognition, and natural language processing, DNN Algorithms has succeeded in many drawbacks; especially the trained DNN models have made easy to the researchers to produces state-of-art results. However, sharing these trained models are always a challenging task, i.e. security, and protection. We performed extensive experiments to present some analysis of watermark in DNN. We proposed a DNN model for Digital watermarking which investigate the intellectual property of Deep Neural Network, Embedding watermarks, and owner verification. This model can generate the watermarks to deal with possible attacks (fine tuning and train to embed). This approach is tested on the standard dataset. Hence this model is robust to above counter-watermark attacks. Our model accurately and instantly verifies the ownership of all the remotely expanded deep learning models without affecting the model accuracy for standard information data.

To increase the possibility of patent entitled of artificial intelligence related inventions at the Japanese patent office, this paper analyzes the Japanese patent act and patent examination guidelines. The approach for assessing whether a computer related invention belongs to a eligible subject-matter includes two steps. The first step is whether a computer related invention meets the definition of an "invention" that is "creation of a technical idea utilizing the laws of nature" . The second step is whether a computer related invention meets "idea based on the standpoint of software" . From the perspective of patent analysis, Japan's artificial intelligence technology is leading the world, second only to the United States. In this field, the Japanese patent office is one of the most important intellectual property offices, and its legislation and practice of patent eligibility review for artificial intelligence related inventions have an important impact on the world.

In internet based communication, various types of attacks have been evolved. Hence, attacker easily breaches the securities. Traditional intrusion detection techniques to observe these attacks have failed and thus hefty systems are required to remove these attacks before they expose entire network. With the ability of artificial intelligence systems to adapt high computational speed, boost fault tolerance, and error resilience against noisy information, a hybrid particle swarm optimization(PSO) fuzzy rule based inference engine has been designed in this paper. The fuzzy logic based on degree of truth while the PSO algorithm based on population stochastic technique helps in learning from the scenario, thus their combination will increase the toughness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal as well as anomalism behaviour in the network. DARPA-KDD99 dataset examined on this system to address the behaviour of each connection on network and compared with existing system. This approach improves the result on the basis of precision, recall and F1-score.

The National Airspace System (NAS), as a portion of the US' transportation system, has not yet begun to model or adopt integration of Artificial Intelligence (AI) technology. However, users of the NAS, i.e., Air transport operators, UAS operators, etc. are beginning to use this technology throughout their operations. At issue within the broader aviation marketplace, is the continued search for a solution set to the persistent daily delays and schedule perturbations that occur within the NAS. Despite billions invested through the NAS Modernization Program, the delays persist in the face of reduced demand for commercial routings. Every delay represents an economic loss to commercial transport operators, passengers, freighters, and any business depending on the transportation performance. Therefore, the FAA needs to begin to address from an advanced concepts perspective, what this wave of new technology will affect as it is brought to bear on various operations performance parameters, including safety, security, efficiency, and resiliency solution sets. This paper is the first in a series of papers we are developing to explore the application of AI in the National Airspace System (NAS). This first paper is meant to get everyone in the aviation community on the same page, a primer if you will, to start the technical discussions. This paper will define AI; the capabilities associated with AI; current use cases within the aviation ecosystem; and how to prepare for insertion of AI in the NAS. The next series of papers will look at NAS Operations Theory utilizing AI capabilities and eventually leading to a future intelligent NAS (iNAS) environment.

Over the past few years, we have been witnessing the rise of misinformation on the Internet. People fall victims of fake news continuously, and contribute to their propagation knowingly or inadvertently. Many recent efforts seek to reduce the damage caused by fake news by identifying them automatically with artificial intelligence techniques, using signals from domain flag-lists, online social networks, etc. In this work, we present Check-It, a system that combines a variety of signals into a pipeline for fake news identification. Check-It is developed as a web browser plugin with the objective of efficient and timely fake news detection, while respecting user privacy. In this paper, we present the design, implementation and performance evaluation of Check-It. Experimental results show that it outperforms state-of-the-art methods on commonly-used datasets.

Deep learning is the segment of artificial intelligence which is involved with imitating the learning approach that human beings utilize to get some different types of knowledge. Analyzing videos, a part of deep learning is one of the most basic problems of computer vision and multi-media content analysis for at least 20 years. The job is very challenging as the video contains a lot of information with large differences and difficulties. Human supervision is still required in all surveillance systems. New advancement in computer vision which are observed as an important trend in video surveillance leads to dramatic efficiency gains. We propose a CCTV based theft detection along with tracking of thieves. We use image processing to detect theft and motion of thieves in CCTV footage, without the use of sensors. This system concentrates on object detection. The security personnel can be notified about the suspicious individual committing burglary using Real-time analysis of the movement of any human from CCTV footage and thus gives a chance to avert the same.

System penetration testing is an important measure of discovering information system security issues. This paper summarizes and analyzes the high-risk problems found in the penetration testing of the artificial storm prediction system for power grid storm disasters from four aspects: application security, middleware security, host security and network security. In particular, in order to overcome the blindness of PGRDAIPS current SQL injection penetration test, this paper proposes a SQL blind bug based on improved second-order fragmentation reorganization. By modeling the SQL injection attack behavior and comparing the SQL injection vulnerability test in PGRDAIPS, this method can effectively reduce the blindness of SQL injection penetration test and improve its accuracy. With the prevalence of ubiquitous power internet of things, the electric power information system security defense work has to be taken seriously. This paper can not only guide the design, development and maintenance of disaster prediction information systems, but also provide security for the Energy Internet disaster safety and power meteorological service technology support.

Mobile Ad hoc Networks (MANETs) are wireless networks that are void of fixed infrastructure as the communication between nodes are dependent on the liaison of each node in the network. The efficacy of MANET in critical scenarios like battlefield communications, natural disaster require new security strategies and policies to guarantee the integrity of nodes in the network. Due to the inherent frailty of MANETs, new security measures need to be developed to defend them. Intrusion Detection strategy used in wired networks are unbefitting for wireless networks due to reasons not limited to resource constraints of participating nodes and nature of communication. Nodes in MANET utilize multi hop communication to forward packets and this result in consumption of resources like battery and memory. The intruder or cheat nodes decide to cooperate or non-cooperate with other nodes. The cheat nodes reduce the overall effectiveness of network communications such as reduced packet delivery ratio and sometimes increase the congestion of the network by forwarding the packet to wrong destination and causing packets to take more times to reach the appropriate final destination. In this paper a decision tree based artificial immune system (AIS) strategy is utilized to detect such cheat nodes thereby improving the efficiency of packet delivery.

A fundamental recurring task in many machine learning applications is the search for the Nearest Neighbor in high dimensional metric spaces. Towards answering queries in large scale problems, state-of-the-art methods employ Approximate Nearest Neighbors (ANN) search, a search that returns the nearest neighbor with high probability, as well as techniques that compress the dataset. Product-Quantization (PQ) based ANN search methods have demonstrated state-of-the-art performance in several problems, including classification, regression and information retrieval. The dataset is encoded into a Cartesian product of multiple low-dimensional codebooks, enabling faster search and higher compression. Being intrinsically parallel, PQ-based ANN search approaches are amendable for hardware acceleration. This paper proposes a novel Hierarchical PQ (HPQ) based ANN search method as well as an FPGA-tailored architecture for its implementation that outperforms current state of the art systems. HPQ gradually refines the search space, reducing the number of data compares and enabling a pipelined search. The mapping of the architecture on a Stratix 10 FPGA device demonstrates over ×250 speedups over current state-of-the-art systems, opening the space for addressing larger datasets and/or improving the query times of current systems.

To gain strategic insight into defending against the network reconnaissance stage of advanced persistent threats, we recreate the escalating competition between scans and deceptive views on a Software Defined Network (SDN). Our threat model presumes the defense is a deceptive network view unique for each node on the network. It can be configured in terms of the number of honeypots and subnets, as well as how real nodes are distributed across the subnets. It assumes attacks are NMAP ping scans that can be configured in terms of how many IP addresses are scanned and how they are visited. Higher performing defenses detect the scanner quicker while leaking as little information as possible while higher performing attacks are better at evading detection and discovering real nodes. By using Artificial Intelligence in the form of a competitive coevolutionary genetic algorithm, we can analyze the configurations of high performing static defenses and attacks versus their evolving adversary as well as the optimized configuration of the adversary itself. When attacks and defenses both evolve, we can observe that the extent of evolution influences the best configurations.

One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behavior. The most important component used to detect cyber attacks or malicious activities is the Intrusion Detection System (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In trendy days, artificial intelligence algorithms are rising as a brand new computing technique which will be applied to actual time issues. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defense dataset (CSE-CIC-IDS2018), the very latest Intrusion Detection Dataset created in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC (Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.001. The proposed system using artificial intelligence of botnet attack detection is powerful, more accurate and precise. The novel proposed system can be implemented in n machines to conventional network traffic analysis, cyber-physical system traffic data and also to the real-time network traffic analysis.

In order to better ensure the operation safety of the network, the real-time early warning of network security threats is studied based on the improved ant colony algorithm. Firstly, the network security threat perception algorithm is optimized based on the principle of neural network, and the network security threat detection process is standardized according to the optimized algorithm. Finally, the real-time early warning of network security threats is realized. Finally, the experiment proves that the network security threat real-time warning based on the improved ant colony algorithm has better security and stability than the traditional warning methods, and fully meets the research requirements.

In view of the challenges with Aids to Navigation (AtoN) managements and emergency response, the present study designs and presents an AtoN real-time video surveillance system based on the AIS collision warning. The key technologies regarding with AtoN cradle head control and testing algorithms, video image fusion, system operation and implementation are demonstrated in details. Case study is performed at Guan River (China) to verify the effectiveness of the AtoN real-time video surveillance system for maritime security supervision. The research results indicate that the intellective level of the AtoN maintenance and managements could be significantly improved. The idea of designing modules brings a good flexibility and a high portability for the present surveillance system, therefore provides a guidance for the design of similar maritime surveillance systems.

Increasingly organizations are collecting ever larger amounts of data to build complex data analytics, machine learning and AI models. Furthermore, the data needed for building such models may be unstructured (e.g., text, image, and video). Hence such data may be stored in different data management systems ranging from relational databases to newer NoSQL databases tailored for storing unstructured data. Furthermore, data scientists are increasingly using programming languages such as Python, R etc. to process data using many existing libraries. In some cases, the developed code will be automatically executed by the NoSQL system on the stored data. These developments indicate the need for a data security and privacy solution that can uniformly protect data stored in many different data management systems and enforce security policies even if sensitive data is processed using a data scientist submitted complex program. In this paper, we introduce our vision for building such a solution for protecting big data. Specifically, our proposed system system allows organizations to 1) enforce policies that control access to sensitive data, 2) keep necessary audit logs automatically for data governance and regulatory compliance, 3) sanitize and redact sensitive data on-the-fly based on the data sensitivity and AI model needs, 4) detect potentially unauthorized or anomalous access to sensitive data, 5) automatically create attribute-based access control policies based on data sensitivity and data type.

Searchable encryption will become more important as medical services intensify their use of big data and artificial intelligence. To use searchable encryption safely, the resistance of terminals with embedded searchable encryption to illegal attacks (tamper resistance) is extremely important. This study proposes a searchable encryption system embedded in terminals and evaluate the tamper resistance of the proposed system. This study also proposes attack scenarios and quantitatively evaluates the tamper resistance of the proposed system by performing experiments following the proposed attack scenarios.

The systematic integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into the supply chain to increase operational efficiency and quality has also introduced new complexities to the threat landscape. The myriad of sensors could increase data collection capabilities for businesses to facilitate process automation aided by Artificial Intelligence (AI) but without adopting an appropriate Security-by-Design framework, threat detection and response are destined to fail. The emerging concept of Smart Workplace incorporates many CPS (e.g. Robots and Drones) to execute tasks alongside Employees both of which can be exploited as Insider Threats. We introduce and discuss forensic-readiness, liability attribution and the ability to track moving Smart SPS Objects to support modern Digital Forensics and Incident Response (DFIR) within a defence-in-depth strategy. We present a framework to facilitate the tracking of object behaviour within Smart Controlled Business Environments (SCBE) to support resilience by enabling proactive insider threat detection. Several components of the framework were piloted in a company to discuss a real-life case study and demonstrate anomaly detection and the emerging of behavioural patterns according to objects' movement with relation to their job role, workspace position and nearest entry or exit. The empirical data was collected from a Bluetooth-based Proximity Monitoring Solution. Furthermore, a key strength of the framework is a federated Blockchain (BC) model to achieve forensic-readiness by establishing a digital Chain-of-Custody (CoC) and a collaborative environment for CPS to qualify as Digital Witnesses (DW) to support post-incident investigations.

CISE stands for Common Information Sharing Environment and refers to an architecture and set of protocols, procedures and services for the exchange of data and information across Maritime Authorities of EU (European Union) Member States (MS's). In the context of enabling the implementation and adoption of CISE by different MS's, EU has funded a number of projects that enable the development of subsystems and adaptors intended to allow MS's to connect and make use of CISE. In this context, the Integrated Systems Laboratory (ISL) has led the development of the corresponding Hellenic and Cypriot CISE by developing a Control, Command & Information (C2I) system that unifies all partial maritime surveillance systems into one National Situational Picture Management (NSPM) system, and adaptors that allow the interconnection of the corresponding national legacy systems to CISE and the exchange of data, information and requests between the two MS's. Furthermore, a set of forensics tools that allow geospatial & time filtering and detection of anomalies, risk incidents, fake MMSIs, suspicious speed changes, collision paths, and gaps in AIS (Automatic Identification System), have been developed by combining motion models, AI, deep learning and fusion algorithms using data from different databases through CISE. This paper briefly discusses these developments within the EU CISE-2020, Hellenic CISE and CY-CISE projects and the benefits from the sharing of maritime data across CISE for both maritime surveillance and security. The prospect of using CISE for the creation of a considerably rich database that could be used for forensics analysis and detection of suspicious maritime traffic and maritime surveillance is discussed.

The main purpose of this paper is to solve the problem of quantitative and qualitative evaluation of network security. Referring to the relevant network security situation assessment algorithms, and by means of advanced artificial intelligence deep learning technology, to build a network security Indication System based on Cyber Range, and optimize the guidance model of deep learning technology.