Biblio

Nowadays, smart cities (SCs) use technologies and different types of data collected to improve the lifestyles of their citizens. Indeed, connected smart vehicles are technologies used for an SC’s intelligent traffic monitoring systems (ITMSs). However, most proposed monitoring approaches do not consider realtime monitoring. This paper presents real-time data processing for an intelligent traffic monitoring dashboard using the Hadoop ecosystem dashboard components. Many data are available due to our proposed monitoring approach, such as the total number of vehicles on different routes and data on trucks within a radius (10KM) of a specific point given. Based on our generated data, we can make real-time decisions to improve circulation and optimize traffic flow.

Network attacks are always a nightmare for the network administrators as it eats away a huge wavelength and disturbs the normal working of many critical services in the network. Network behavior based profiling and detection is considered to be an accepted method; but the modeling data and method is always a big concern. The network event-based profiling is getting acceptance as they are sequential in nature and the sequence depicts the behavior of the system. This sequential network events can be analyzed using different techniques to create a profile for anomaly detection. In this paper we examine the possibility of two techniques for sequential event analysis using Modified GSP and IPAM algorithm. We evaluate the performance of these algorithms on the CSE-CIC-IDS 2018 data set to benchmark the performance. This experiment is different from other anomaly-based detection which evaluates the features of the dataset to detect the abnormalities. The performance of the algorithms on the dataset is then confirmed by the pattern evolving from the analysis and the indications it provides for early detection of network attacks.

The Internet of Things is an emerging technology for recent marketplace. In IoT, the heterogeneous devices are connected through the medium of the Internet for seamless communication. The devices used in IoT are resource-constrained in terms of memory, power and processing. Due to that, IoT system is unable to implement hi-end security for malicious cyber-attacks. The recent era is all about connecting IoT devices in various domains like medical, agriculture, transport, power, manufacturing, supply chain, education, etc. and thus need to be prevented from attacks and analyzed after attacks for legal action. The legal analysis of IoT data, devices and communication is called IoT forensics which is highly indispensable for various types of attacks on IoT system. This paper will review types of IoT attacks and its preventive measures in cyber security. It will also help in ascertaining IoT forensics and its challenges in detail. This paper will conclude with the high requirement of cyber security in IoT domains with implementation of standard rules for IoT forensics.

A new privacy Laplace common recognition algorithm is designed to protect users’ privacy data in this paper. This algorithm disturbs state transitions and information generation functions using exponentially decaying Laplace noise to avoid attacks. The mean square consistency and privacy protection performance are further studied. Finally, the theoretical results obtained are verified by performing numerical simulations.

Cyber-Physical Systems (CPSs), a class of complex intelligent systems, are considered the backbone of Industry 4.0. They aim to achieve large-scale, networked control of dynamical systems and processes such as electricity and gas distribution networks and deliver pervasive information services by combining state-of-the-art computing, communication, and control technologies. However, CPSs are often highly nonlinear and uncertain, and their intrinsic reliance on open communication platforms increases their vulnerability to security threats, which entails additional challenges to conventional control design approaches. Indeed, sensor measurements and control command signals, whose integrity plays a critical role in correct controller design, may be interrupted or falsely modified when broadcasted on wireless communication channels due to cyber attacks. This can have a catastrophic impact on CPS performance. In this paper, we first conduct a thorough analysis of recently developed secure and resilient control approaches leveraging the solid foundations of adaptive control theory to achieve security and resilience in networked CPSs against sensor and actuator attacks. Then, we discuss the limitations of current adaptive control strategies and present several future research directions in this field.

This paper presents a novel authentication method based on a distributed version of Kerberos for UAVs. One of the major problems of UAVs in recent years has been cyber-attacks which allow attackers to control the UAV or access its information. The growing use of UAVs has encouraged us to investigate the methods of their protection especially authentication of their users. In the past, the Kerberos system was rarely used for authentication in UAV systems. In our proposed method, based on a distributed version of Kerberos, we can authenticate multiple ground stations, users, and controllers for one or more UAVs. This method considers most of the security aspects to protect UAV systems mainly in the authentication phase and improves the security of UAVs and ground control stations and their communications considerably.

The popularity of portable web browsers is increasing due to its convenient and compact nature along with the benefit of the data being stored and transferred easily using a USB drive. As technology gets updated frequently, developers are working on web browsers that can be portable in nature with additional security features like private mode browsing, built in ad blockers etc. The increased probability of using portable web browsers for carrying out nefarious activities is a result of cybercriminals with the thought that if they use portable web browsers in private mode it won't leave a digital footprint. Hence, the research paper aims at performing a comparative study of four portable web browsers namely Brave, TOR, Vivaldi, and Maxthon along with various memory acquisition tools to understand the quantity and quality of the data that can be recovered from the memory dump in two different conditions that is when the browser tabs were open and when the browser tabs were closed in a system to aid the forensic investigators.

Third party mobile applications are dominating the business strategies of organisations and have become an integral part of personal life of individuals. These applications are used for financial transactions, sharing of sensitive data etc. The recent breaches in Android clearly indicate that use of third party applications have become a serious security threat. By design, Android framework keeps all these applications in untrusted domain. Due to this a common policy of resource control exists for all such applications. Further, user discretion in granting permissions to specific applications is not effective because users are not always aware of deep functionalities, mala fide intentions (in case of spywares) and bugs/flaws in these third-party applications. In this regard, we propose a security scheme to mitigate unauthorised access of resources by third party applications. Our proposed scheme is based on SEAndroid policies and achieves fine grained confinement with respect to access control for the third party applications. To the best of our knowledge, the proposed scheme is unique and first of its kind. The proposed scheme is integrated with Android Oreo 8.1.0 for performance and security analysis. It is compatible with any Android device with AOSP support.

Intrusion Detection System (IDS) is used to identify malicious traffic on the network. Apart from rule-based IDS, machine learning and deep learning based on IDS are also being developed to improve the accuracy of IDS detection. In this study, the public dataset CIC IDS 2017 was used in developing deep learning-based IDS because this dataset contains the new types of attacks. In addition, this dataset also meets the criteria as an intrusion detection dataset. The dataset was split into train data, validation data and test data. We proposed Bidirectional Long-Short Term Memory (LSTM) for building neural network. We created 24 scenarios with various changes in training parameters which were trained for 100 epochs. The training parameters used as research variables are optimizer, activation function, and learning rate. As addition, Dropout layer and L2-regularizer were implemented on every scenario. The result shows that the model used Adam optimizer, Tanh activation function and a learning rate of 0.0001 produced the highest accuracy compared to other scenarios. The accuracy and F1 score reached 97.7264% and 97.7516%. The best model was trained again until 1000 iterations and the performance increased to 98.3448% in accuracy and 98.3793% in F1 score. The result exceeded several previous works on the same dataset.

Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people’s lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection.

The dark web has become a major trading platform for cybercriminals, with its anonymity and encrypted content nature make it possible to exchange hacked information and sell illegal goods without being traced. The types of items traded on the dark web have increased with the number of users and demands. In recent years, in addition to the main items sold in the past, including drugs, firearms and child pornography, a growing number of cybercriminals are targeting various types of private information, including different types of account data, identity information and visual data etc. This paper will further discuss the issue of threat detection in the dark web by reviewing the past literature on the subject. An approach is also proposed to identify criminals who commit crimes offline or on the surface network by using private information purchased from the dark web and the original sources of information on the dark web by building a database based on historical victim records for keyword matching and traffic analysis.

In the past few years, the blockchain emerged as peer-to-peer distributed ledger technology for recording transactions, maintained by many peers without any central trusted regulatory authority through distributed public-key cryptography and consensus mechanism. It has not only given the birth of cryptocurrencies, but it also resolved various security, privacy and transparency issues of decentralized systems. This article discussed the blockchain basics overview, architecture, and blockchain security components such as hash function, Merkle tree, digital signature, and Elliptic curve cryptography (ECC). In addition to the core idea of blockchain, we focus on ECC's significance in the blockchain. We also discussed why RSA and other key generation mechanisms are not suitable for blockchain-based IoT applications. We also analyze many possible blockchain-based applications where ECC algorithm is better than other algorithms concerning security and privacy assurance. At the end of the article, we will explain the comparative analysis of ECC and RSA.

Elliptic Curve Cryptography (ECC) is a revolution in asymmetric key cryptography which is based on the hardness of discrete logarithms. ECC offers lightweight encryption as it presents equal security for smaller keys, and reduces processing overhead. But asymmetric schemes are vulnerable to several cryptographic attacks such as plaintext attacks, known cipher text attacks etc. Frequency analysis is a type of cipher text attack which is a passive traffic analysis scenario, where an opponent studies the frequency or occurrence of single letter or groups of letters in a cipher text to predict the plain text part. Block cipher modes are not used in asymmetric key encryption because encrypting many blocks with an asymmetric scheme is literally slow and CBC propagates transmission errors. Therefore, in this research we present a new approach to defence against frequency analysis in ECC using K-Means clustering to defence against Frequency Analysis. In this proposed methodology, security of ECC against frequency analysis is achieved by clustering the points of the curve and selecting different cluster for encoding a text each time it is encrypted. This technique destroys the regularities in the cipher text and thereby guards against cipher text attacks.

Current programming models for developing Internet of Things (IoT) applications are logically centralized and ill-suited for most IoT applications. We contribute Protocols over Things, a decentralized programming model that represents an IoT application via a protocol between the parties involved and provides improved performance over network-level delivery guarantees.

As a current disruptive and transformative technology, artificial intelligence is constantly infiltrating all aspects of production and life. However, with the in-depth development and application of artificial intelligence, the security challenges it faces have become more and more prominent. In the real world, attacks against intelligent systems such as the Internet of Things, smart homes, and driverless cars are constantly appearing, and incidents of artificial intelligence being used in cyber-attacks and cybercrimes frequently occur. This article aims to discuss artificial intelligence security issues and propose some countermeasures.

In recent year, there has been a growing need for intelligent systems that not only are able to provide reliable classifications but can also produce explanations for the decisions they make. The demand for increased explainability has led to the emergence of explainable artificial intelligence (XAI) as a specific research field. In this context, fuzzy logic systems represent a promising tool thanks to their inherently interpretable structure. The use of a rule-base and linguistic terms, in fact, have allowed researchers to create models that are able to produce explanations in natural language for each of the classifications they make. So far, however, designing systems that make use of interval type-2 (IT2) fuzzy logic and also give explanations for their outputs has been very challenging, partially due to the presence of the type-reduction step. In this paper, it will be shown how constrained interval type-2 (CIT2) fuzzy sets represent a valid alternative to conventional interval type-2 sets in order to address this issue. Through the analysis of two case studies from the medical domain, it is shown how explainable CIT2 classifiers are produced. These systems can explain which rules contributed to the creation of each of the endpoints of the output interval centroid, while showing (in these examples) the same level of accuracy as their IT2 counterpart.

Crowd sensing is one of the core features of internet of vehicles, the use of internet of vehicles for crowd sensing is conducive to the rational allocation of sensing tasks. This paper mainly studies the problem of task allocation for crowd sensing in internet of vehicles, proposes a trajectory-based task allocation scheme for crowd sensing in internet of vehicles. With limited budget constraints, participants' trajectory is taken as an indicator of the spatiotemporal availability. Based on the solution idea of the minimal-cover problem, select the minimum number of participating vehicles to achieve the coverage of the target area.

The notion of Cyber Physical Systems is based on using recent computing, communication, and control methods to design and operate intelligent and autonomous systems that can provide using innovative technologies. The existence of several critical applications within the scope of cyber physical systems results in many security and privacy concerns. On the other hand, the distributive nature of these CPS increases security risks. In addition, certain CPS, such as medical ones, generate and process sensitive data regularly, hence, this data must be protected at all levels of generation, processing, and transmission. In this paper, we present a taxonomy based analysis for the state of the art work on security issues in CPS. We identify four types of analysis for security issues in CPS: Modeling, Detection, Prevention, and Response. In addition, we identified six applications of CPS where security is relevant: eHealth and medical, smart grid and power related, vehicular technologies, industrial control and manufacturing, autonomous systems and UAVs, and finally IoT related issues. Then we mapped existing works in the literature into these categories.

In this research paper author surveys the need of data protection from intelligent systems in the private and public sectors. For this, she identifies that the Smart Information Security Intel processes needs to be the suggestive key policy for both sectors of governance either public or private. The information is very sensitive for any organization. When the government offices are concerned, information needs to be abstracted and encapsulated so that there is no information stealing. For this purposes, the art of skill set and new optimized technology needs to be stationed. Author identifies that digital bar-coded air port like security using conveyor belts and digital bar-coded conveyor boxes to scan switched ON articles like internet of things needs to be placed. As otherwise, there can potentially be data, articles or information stealing from the operational sites where access is unauthorized. Such activities shall need to be scrutinized, minutely. The biometric such as fingerprints, iris, voice and face recognition pattern updates in the virtual data tables must be taken to keep data entry-exit log up to-date. The information technicians of the sentinel systems must help catch the anomalies in the professional working time in private and public sectors if there is red flag as indicator. The author in this research paper shall discuss in detail what we shall station, how we shall station and what all measures we might need to undertake to safeguard the stealing of sensitive information from the organizations like administration buildings, government buildings, educational schools, hospitals, courts, private buildings, banks and all other offices nation-wide. The TO-BE new processes shall make the AS-IS office system more information secured, data protected and personnel security stronger.

Emerging intelligent systems have stringent constraints including cost and power consumption. When they are used in critical applications, resiliency becomes another key requirement. Much research into techniques for fault tolerance and dependability has been successfully applied to highly critical systems, such as those used in space, where cost is not an overriding constraint. Further, most resiliency techniques were focused on dealing with failures in the hardware and bugs in the software. The next generation of systems used in critical applications will also have to be tolerant to test escapes after manufacturing, soft errors and transients in the electronics, hardware bugs, hardware and software Trojans and viruses, as well as intrusions and other security attacks during operation. This paper will assess the impact of these threats on the results produced by a critical system, and proposed solutions to each of them. It is argued that run-time checks at the application-level are necessary to deal with errors in the results.

While the general idea of Intelligent Transportation System (ITS) is to employ suitable, sophisticated information and communications technologies, however, such tool also encompass many system complexities. Fittingly, this paper aims to highlight the most contemporary system complications of ITS and in doing so, will also underline the safety, security and risk management concerns. More importantly, effectively treating such issues will ultimately improve the reliability and efficiency of transportation systems. Whereas such issues are among the most significant subjects for any intelligent system, for ITS in particular they the most dominant. For such intelligent systems, the safety, security and risk management issues must not only be decidedly prioritized, but also methodically integrated. As a part of such ITS integration, this paper will delicately examine the Emergency Management System (EMS) development and application. Accurate EMS is not only a mandatory feature of intelligent systems, but it is a fundamental component of ITS which will vigilantly respond to safety, security and risk management apprehensions. To further substantiate such scheme, the Sydney Metro's EMS will be also conferred. It was determined that, the Sydney Metro's EMS although highly advanced, it was also vigilantly aligned with specific designated safety, security and risk management strategies.

According to the new Tor network (6.0.5 version) can help the domestic users easily realize "over the wall", and of course criminals may use it to visit deep and dark website also. The paper analyzes the core technology of the new Tor network: the new flow obfuscation technology based on meek plug-in and real instance is used to verify the new Tor network's fast connectivity. On the basis of analyzing the traffic confusion mechanism and the network crime based on Tor, it puts forward some measures to prevent the using of Tor network to implement network crime.

Deep Neural Network (DNN) has recently become the “de facto” technique to drive the artificial intelligence (AI) industry. However, there also emerges many security issues as the DNN based intelligent systems are being increasingly prevalent. Existing DNN security studies, such as adversarial attacks and poisoning attacks, are usually narrowly conducted at the software algorithm level, with the misclassification as their primary goal. The more realistic system-level attacks introduced by the emerging intelligent service supply chain, e.g. the third-party cloud based machine learning as a service (MLaaS) along with the portable DNN computing engine, have never been discussed. In this work, we propose a low-cost modular methodology-Stealth Infection on Neural Network, namely “SIN2”, to demonstrate the novel and practical intelligent supply chain triggered neural Trojan attacks. Our “SIN2” well leverages the attacking opportunities built upon the static neural network model and the underlying dynamic runtime system of neural computing framework through a bunch of neural Trojaning techniques. We implement a variety of neural Trojan attacks in Linux sandbox by following proposed “SIN2”. Experimental results show that our modular design can rapidly produce and trigger various Trojan attacks that can easily evade the existing defenses.

Embry-Riddle Aeronautical University (ERAU) is working with the Air Force Research Lab (AFRL) to develop a distributed multi-layer autonomous UAS planning and control technology for gathering intelligence in Anti-Access Area Denial (A2/AD) environments populated by intelligent adaptive adversaries. These resilient autonomous systems are able to navigate through hostile environments while performing Intelligence, Surveillance, and Reconnaissance (ISR) tasks, and minimizing the loss of assets. Our approach incorporates artificial life concepts, with a high-level architecture divided into three biologically inspired layers: cyber-physical, reactive, and deliberative. Each layer has a dynamic level of influence over the behavior of the agent. Algorithms within the layers act on a filtered view of reality, abstracted in the layer immediately below. Each layer takes input from the layer below, provides output to the layer above, and provides direction to the layer below. Fast-reactive control systems in lower layers ensure a stable environment supporting cognitive function on higher layers. The cyber-physical layer represents the central nervous system of the individual, consisting of elements of the vehicle that cannot be changed such as sensors, power plant, and physical configuration. On the reactive layer, the system uses an artificial life paradigm, where each agent interacts with the environment using a set of simple rules regarding wants and needs. Information is communicated explicitly via message passing and implicitly via observation and recognition of behavior. In the deliberative layer, individual agents look outward to the group, deliberating on efficient resource management and cooperation with other agents. Strategies at all layers are developed using machine learning techniques such as Genetic Algorithm (GA) or NN applied to system training that takes place prior to the mission.

By using generalized regression neural network clustering analysis, effective clustering of five kinds of network intrusion behavior modes is carried out. First of all, intrusion data is divided into five categories by making use of fuzzy C means clustering algorithm. Then, the samples that are closet to the center of each class in the clustering results are taken as the clustering training samples of generalized neural network for the data training, and the results output by the training are the individual owned invasion category. The experimental results showed that the new algorithm has higher classification accuracy of network intrusion ways, which can provide more reliable data support for the prevention of the network intrusion.