Biblio

Mobile phones have become nowadays a commodity to the majority of people. Using them, people are able to access the world of Internet and connect with their friends, their colleagues at work or even unknown people with common interests. This proliferation of the mobile devices has also been seen as an opportunity for the cyber criminals to deceive smartphone users and steel their money directly or indirectly, respectively, by accessing their bank accounts through the smartphones or by blackmailing them or selling their private data such as photos, credit card data, etc. to third parties. This is usually achieved by installing malware to smartphones masking their malevolent payload as a legitimate application and advertise it to the users with the hope that mobile users will install it in their devices. Thus, any existing application can easily be modified by integrating a malware and then presented it as a legitimate one. In response to this, scientists have proposed a number of malware detection and classification methods using a variety of techniques. Even though, several of them achieve relatively high precision in malware classification, there is still space for improvement. In this paper, we propose a text mining all repeated pattern detection method which uses the decompiled files of an application in order to classify a suspicious application into one of the known malware families. Based on the experimental results using a real malware dataset, the methodology tries to correctly classify (without any misclassification) all randomly selected malware applications of 3 categories with 3 different families each.

The objective of the work is to provide security to the medical images by embedding medical data (EPR-Electronic Patient Record) along with the image to reduce the bandwidth during communication. Reversible watermarking and Digital Signature itself will provide high security. This application mainly used in tele-surgery (Medical Expert to Medical Expert Communication). Only the authorized medical experts can explore the patients' image because of Kerberos. The proposed work is mainly to restrict the unauthorized access to get the patients'data. So medical image authentication may be achieved without biometric recognition such as finger prints and eye stamps etc. The EPR itself contains the patients' entire history, so after the extraction process Medical expert can able to identify the patient and also the disease information. In future we can embed the EPR inside the medical image after it got encrypted to achieve more security. To increase the authentication, Medical Expert biometric information can be embedded inside the image in the future. Experiments were conducted using more than 500 (512 × 512) image archives in various modalities from the NIH (National Institute of Health) and Aycan sample digital images downloaded from the internet and tests are conducted. Almost in all images with greater than 15000 bits embedding size and got PSNR of 60.4 dB to 78.9 dB with low distortion in received image because of compression, not because of watermarking and average NPCR (Number of Pixels Change Rate) is 98.9 %.

With the continuous development of the Internet of Vehicles, vehicles are no longer isolated nodes, but become a node in the car network. The open Internet will introduce traditional security issues into the Internet of Things. In order to ensure the safety of the networked cars, we hope to set up an intrusion detection system (IDS) on the vehicle terminal to detect and intercept network attacks. In our work, we designed an intrusion detection system for the Internet of Vehicles based on a convolutional neural network, which can run in a low-powered embedded vehicle terminal to monitor the data in the car network in real time. Moreover, for the case of packet encryption in some car networks, we have also designed a separate version for intrusion detection by analyzing the packet header. Experiments have shown that our system can guarantee high accuracy detection at low latency for attack traffic.

Vehicular ad hoc network is one of most recent research areas to deploy intelligent Transport System. Due to their highly dynamic topology, energy constrained and no central point coordination, routing with minimal delay, minimal energy and maximize throughput is a big challenge. Software Defined Networking (SDN) is new paradigm to improve overall network lifetime. It incorporates dynamic changes with minimal end-end delay, and enhances network intelligence. Along with this, intelligence secure routing is also a major constraint. This paper proposes a novel approach to Energy efficient secured routing protocol for Software Defined Internet of vehicles using Restricted Boltzmann Algorithm. This algorithm is to detect hostile routes with minimum delay, minimum energy and maximum throughput compared with traditional routing protocols.

Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture's future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.

The evolution of the Internet of Vehicles (IoV) paradigm has recently attracted a lot of researchers and industries. Vehicular Ad Hoc Networks (VANET) is the networking model that lies at the heart of this technology. It enables the vehicles to exchange relevant information concerning road conditions and safety. However, ensuring communication security has been and still is one of the main challenges to vehicles' interconnection. To secure the interconnected vehicular system, many cryptography techniques, communication protocols, and certification and reputation-based security approaches were proposed. Nonetheless, some limitations are still present, preventing the practical implementation of such approaches. In this paper, we first define a set of locally-perceived behavioral reputation parameters that enable a distributed evaluation of vehicles' reputation. Then, we integrate these parameters into the design of a reputation management system to exclude malicious or faulty vehicles from the IoV network. Our system can help in the prevention of several attacks on the VANET environment such as Sybil and Denial of Service attacks, and can be implemented in a fully decentralized fashion.

With the proposal of the national industrial 4.0 strategy, the integration of industrial control network and Internet technology is getting higher and higher. At the same time, the closeness of industrial control networks has been broken to a certain extent, making the problem of industrial control network security increasingly serious. S7 protocol is a private protocol of Siemens Company in Germany, which is widely used in the communication process of industrial control network. In this paper, an industrial control intrusion detection model based on S7 protocol is proposed. Traditional protocol parsing technology cannot resolve private industrial control protocols, so, this model uses deep analysis algorithm to realize the analysis of S7 data packets. At the same time, in order to overcome the complexity and portability of static white list configuration, this model dynamically builds a white list through white list self-learning algorithm. Finally, a composite intrusion detection method combining white list detection and abnormal behavior detection is used to detect anomalies. The experiment proves that the method can effectively detect the abnormal S7 protocol packet in the industrial control network.

"Good Governance" - may it be corporate or governmental, is a badly needed focus area in the world today where the companies and governments are struggling to survive the political and economical turmoil around the globe. All governments around the world have a tendency of expanding the size of their government, but eventually they would be forced to think reducing the size by incorporating information technology as a way to provide services to the citizens effectively and efficiently. Hence our attempt is to offer a complete solution from birth of a citizen till death encompassing all the necessary services related to the well being of a person living in a society. Our research and analysis would explore the pros and cons of using IT as a solution to our problems and ways to implement them for a best outcome in e-Governance occasionally comparing with the present scenario when relevant.

In our daily lives, the advances of new technology can be used to sustain the development of people across the globe. Particularly, e-government can be the dynamo of the development for the people. The development of technology and the rapid growth in the use of internet creates a big challenge in the administration in both the public and the private sector. E-government is a vital accomplishment, whereas the security is the main downside which occurs in each e-government process. E-government has to be secure as technology grows and the users have to follow the procedures to make their own transactions safe. This paper tackles the challenges and obstacles to enhance the security of information in e-government. Hence to achieve security data hiding techniques are found to be trustworthy. Reversible data hiding (RDH) is an emerging technique which helps in retaining the quality of the cover image. Hence it is preferred over the traditional data hiding techniques. Modification in the existing algorithm is performed for image encryption scheme and data hiding scheme in order to improve the results. To achieve this secret data is split into 20 parts and data concealing is performed on each part. The data hiding procedure includes embedding of data into least significant nibble of the cover image. The bits are further equally distributed in the cover image to obtain the key security parameters. Hence the obtained results validate that the proposed scheme is better than the existing schemes.

In the context of the rapid technological progress, the cyber-threats become a serious challenge that requires immediate and continuous action. As cybercrime poses a permanent and increasing threat, governments, corporate and individual users of the cyber-space are constantly struggling to ensure an acceptable level of security over their assets. Maliciousness on the cyber-space spans identity theft, fraud, and system intrusions. This is due to the benefits of cyberspace-low entry barriers, user anonymity, and spatial and temporal separation between users, make it a fertile field for deception and fraud. Numerous, supervised and unsupervised, techniques have been proposed and used to identify fraudulent transactions and activities that deviate from regular patterns of behaviour. For instance, neural networks and genetic algorithms were used to detect credit card fraud in a dataset covering 13 months and 50 million credit card transactions. Unsupervised methods, such as clustering analysis, have been used to identify financial fraud or to filter fake online product reviews and ratings on e-commerce websites. Blockchain technology has demonstrated its feasibility and relevance in e-commerce. Its use is now being extended to new areas, related to electronic government. The technology appears to be the most appropriate in areas that require storage and processing of large amounts of protected data. The question is what can blockchain technology do and not do to fight malicious online activity?

The low attention to security and privacy causes some problems on data and information that can lead to a lack of public trust in e-Gov service. Security threats are not only included in technical issues but also non-technical issues and therefore, it needs the implementation of inclusive security. The application of inclusive security to e-Gov needs to develop a model involving security and privacy requirements as a trusted security solution. The method used is the elicitation of security and privacy requirements in a security perspective. Identification is carried out on security and privacy properties, then security and privacy relationships are determined. The next step is developing the design of an inclusive security model on e-Gov. The last step is doing an analysis of e-Gov service activities and the role of inclusive security. The results of this study identified security and privacy requirements for building inclusive security. Identification of security requirements involves properties such as confidentiality (C), integrity (I), availability (A). Meanwhile, privacy requirement involves authentication (Au), authorization (Az), and Non-repudiation (Nr) properties. Furthermore, an inclusive security design model on e-Gov requires trust of internet (ToI) and trust of government (ToG) as an e-Gov service provider. Access control is needed to provide solutions to e-Gov service activities.

In this study, we conducted a survey of those who have used E-Government Services (civil servants, employees of public institutions, and the public) to empirically identify the factors affecting the continuous use intention E-Government Services, and conducted an empirical analysis using SPSS and Smart PLS with 284 valid samples except for dual, error and poor answers. Based on the success model of the information system (IS access model), we set independent variables which were divided into quality factors (service quality, system quality, information quality) and risk factors (personal information and security), and perceived ease of use and reliability, which are the main variables based on the technology acceptance model (TAM) that best describes the parameter group, were established as useful parameters. In addition, we design the research model by setting user satisfaction and the continuous use intention as dependent variables, conducted the study about how affecting factors influence to the acceptance factors through 14 hypotheses.The study found that 12 from 14 hypotheses were adopted and 2 were rejected. Looking at the results derived, it was analyzed that, firstly, 3 quality factors all affect perceived ease of use in relation to the quality of service, system quality, information quality which are perceived ease of use of E-Government Services. Second, in relation to the quality of service quality, system quality, information quality and perceived usefulness which are the quality factors of E-Government Services, the quality of service and information quality affect perceived usefulness, but system quality does not affect perceived usefulness. Third, it was analyzed that both factors influence reliability in the relationship between Privacy and security and trust which are risk factors. Fourth, the relationship between perceived ease of use and perceived usefulness has shown that perceived ease of use does not affect perceived usefulness. Finally, the relationship between user value factors (perceptual usability, perceived usefulness and trust) and user satisfaction and the continuous use intention was analyzed that user value factors affect user satisfaction while user satisfaction affects the continuous use intention. This study can be meaningful in that it theoretically presented the factors influencing the continued acceptance of e-government services through precedent research, presented the variables and measurement items verified through the empirical analysis process, and verified the causal relationship between the variables. The e-government service can contribute to the implementation of e-government in line with the era of the 4th Industrial Revolution by using it as a reference to the establishment of policies to improve the quality of people's lives and provide convenient services to the people.

This study was conducted in order to assess the E-security behavior of students in a large higher educational institutions in the United Arab Emirates (UAE). Specifically, it sought to determine the current state of students' E-security behavior in the aspects of malware, password usage, data handling, phishing, social engineering, and online scam. An E- Security Behavior Survey Instrument (EBSI) was used to determine the status of security behavior of the participants in doing their computing activities. To complement the survey tool, focus group discussions were conducted to elicit specific experiences and insights of the participants relative to E-security. The results of the study shows that the overall E-security behavior among students in higher education in the United Arab Emirates (UAE) is moderately favorable. Specifically, the investigation reveals that the students favorably behave when it comes to phishing, social engineering, and online scam. However, they uncertainly behave on malware issues, password usage, and data handling.

Over the past few years, botnets have emerged as one of the most serious cybersecurity threats faced by individuals and organizations. After infecting millions of servers and workstations worldwide, botmasters have started to develop botnets for mobile devices. Mobile botnets use different mediums to communicate with their botmasters. Although significant research has been done to detect mobile botnets that use the Internet as their command and control (C&C) channel, little research has investigated SMS botnets per se. In order to fill this gap, in this paper, we first divide SMS botnets based on their characteristics into three families, namely, info stealer, SMS stealer, and SMS spammer. Then, we propose SMSBotHunter, a novel anomaly detection technique that detects SMS botnets using textual and behavioral features and one-class classification. We experimentally evaluate the detection performance of SMSBotHunter by simulating the behavior of human users and SMS botnets. The experimental results demonstrate that most of the SMS messages sent or received by info stealer and SMS spammer botnets can be detected using textual features exclusively. It is also revealed that behavioral features are crucial for the detection of SMS stealer botnets and will improve the overall detection performance.

Recommended systems are very popular nowadays. They are used online to help a user get the desired product quickly. Recommended Systems are found on almost every website, especially big companies such as Facebook, eBay, Amazon, NetFlix, and others. In specific cases, these systems help the user find a book, movie, article, product of his or her preference, and are also used on social networks to meet friends who share similar interests in different fields. These companies use referral systems because they bring amazing benefits in a very fast time. To generate more accurate recommendations, recommended systems are based on the user's personal information, eg: different ratings, history observation, personal profiles, etc. Use of these systems is very necessary but the way this information is received, and the privacy of this information is almost constantly ignored. Many users are unaware of how their information is received and how it is used. This paper will discuss how recommended systems work in different online companies and how safe they are to use without compromising their privacy. Given the widespread use of these systems, an important issue has arisen regarding user privacy and security. Collecting personal information from recommended systems increases the risk of unwanted exposure to that information. As a result of this paper, the reader will be aware of the functioning of Recommended systems, the way they receive and use their information, and will also discuss privacy protection techniques against Recommended systems.

This article discusses existing approaches to building regional scale networks. Authors offer a mathematical model of network growth process, on the basis of which simulation is performed. The availability characteristic is used as criterion for measuring optimality. This report describes the mechanism for measuring network availability and contains propositions to make changes to the procedure for designing of regional networks, which can improve its qualitative characteristics. The efficiency of changes is confirmed by simulation.

With rapid growth of network size and complexity, network defenders are facing more challenges in protecting networked computers and other devices from acute attacks. Traffic visualization is an essential element in an anomaly detection system for visual observations and detection of distributed DoS attacks. This paper presents an interactive visualization system called TVis, proposed to detect both low-rate and highrate DDoS attacks using Heron's triangle-area mapping. TVis allows network defenders to identify and investigate anomalies in internal and external network traffic at both online and offline modes. We model the network traffic as an undirected graph and compute triangle-area map based on incidences at each vertex for each 5 seconds time window. The system triggers an alarm iff the system finds an area of the mapped triangle beyond the dynamic threshold. TVis performs well for both low-rate and high-rate DDoS detection in comparison to its competitors.

As a new generation of network architecture with subversive changes to traditional IP networks, Content Centric Networks (CCN) has attracted widespread attention from domestic and foreign scholars for its efficient content distribution, multi-path and secure routing features. The design architecture of CCN network has many advantages. However, it is also easily used illegally, which brings certain security problems. For example, objectified network resources which include requesters, publishers, content and node routes, are faced with many security threats, such as privacy attribute disclosure, privacy detection, content information disclosure, and spoofing and denial of service attacks. A node routing security scheme based on dynamic reputation value is proposed for the security problem of node routing. It is convenient for detecting node routing attacks and defending in time. And it could provide security for the Content Centric Networks node routing without affecting the node routing advantages and normal user requests.

With the popularity of the Internet and mobile intelligent terminals, the number of mobile applications is exploding. Mobile intelligent terminals trend to be the mainstream way of people's work and daily life online in place of PC terminals. Mobile application system brings some security problems inevitably while it provides convenience for people, and becomes a main target of hackers. Therefore, it is imminent to strengthen the security detection of mobile applications. This paper divides mobile application security detection into client security detection and server security detection. We propose a combining static and dynamic security detection method to detect client-side. We provide a method to get network information of server by capturing and analyzing mobile application traffic, and propose a fuzzy testing method based on HTTP protocol to detect server-side security vulnerabilities. Finally, on the basis of this, an automated platform for security detection of mobile application system is developed. Experiments show that the platform can detect the vulnerabilities of mobile application client and server effectively, and realize the automation of mobile application security detection. It can also reduce the cost of mobile security detection and enhance the security of mobile applications.

Cyber-Physical Systems (CPSs) interconnect the physical world with digital computers and networks in order to automate production and distribution processes. Nowadays, most CPSs do not work in isolation, but their digital part is connected to the Internet in order to enable remote monitoring, control and configuration. Such a connection may offer entry-points enabling attackers to gain control silently and exploit access to the physical world at the right time to cause service disruption and possibly damage to the surrounding environment. Prevention and monitoring measures can reduce the risk brought by cyber attacks, but the residual risk can still be unacceptably high in critical infrastructures or services. Resilience - i.e., the ability of a system to withstand adverse events while maintaining an acceptable functionality - is therefore a key property for such systems. In our research, we seek a model-free, quantitative, and general-purpose evaluation methodology to extract resilience indexes from, e.g., system logs and process data. While a number of resilience metrics have already been put forward, little experimental evidence is available when it comes to the cyber security of CPSs. By using the model of a real wastewater treatment plant, and simulating attacks that tamper with a critical feedback control loop, we provide a comparison between four resilience indexes selected through a thorough literature review involving over 40 papers. Our results show that the selected indexes differ in terms of behavior and sensitivity with respect to specific attacks, but they can all summarize and extract meaningful information from bulky system logs. Our evaluation includes an approach for extracting performance indicators from observed variables which does not require knowledge of system dynamics; and a discussion about combining resilience indexes into a single system-wide measure is included. 11The authors wish to thank Leonardo S.p.A. for its financial support. The research herein presented is partially supported by project NEFERIS awarded by the Italian Ministry of Defense to Leonardo S.p.A. in partnership with the University of Genoa. This work received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 830892 for project SPARTA.

In the Web 2.0 era, user interaction makes Web application more diverse, but brings threats, among which XSS vulnerability is the common and pernicious one. In order to promote the efficiency of XSS detection, this paper investigates the parameter characteristics of malicious XSS attacks. We identify whether a parameter is malicious or not through detecting user input parameters with SVM algorithm. The original malicious XSS parameters are deformed by DQN algorithm for reinforcement learning for rule-based WAF to be anti-anti-virus. Based on this method, we can identify whether a specific WAF is secure. The above model creates a more efficient automatic XSS detection tool and a more targeted automatic anti-anti-virus payload generation tool. This paper also explores the automatic generation of XSS attack codes with RNN LSTM algorithm.

Cross site scripting (XSS) attack is one of the attacks on the web. It brings session hijack with HTTP cookies, information collection with fake HTML input form and phishing with dummy sites. As a countermeasure of XSS attack, machine learning has attracted a lot of attention. There are existing researches in which SVM, Random Forest and SCW are used for the detection of the attack. However, in the researches, there are problems that the size of data set is too small or unbalanced, and that preprocessing method for vectorization of strings causes misclassification. The highest accuracy of the classification was 98% in existing researches. Therefore, in this paper, we improved the preprocessing method for vectorization by using word2vec to find the frequency of appearance and co-occurrence of the words in XSS attack scripts. Moreover, we also used a large data set to decrease the deviation of the data. Furthermore, we evaluated the classification results with two procedures. One is an inappropriate procedure which some researchers tend to select by mistake. The other is an appropriate procedure which can be applied to an attack detection filter in the real environment.

XSS is one of the common vulnerabilities in web applications. Many black-box testing tools may collect a large number of payloads and traverse them to find a payload that can be successfully injected, but they are not very efficient. And previous research has paid less attention to how to improve the efficiency of black-box testing to detect XSS vulnerability. To improve the efficiency of testing, we develop an XSS testing tool. It collects 6128 payloads and uses a headless browser to detect XSS vulnerability. The tool can discover XSS vulnerability quickly with the ART(Adaptive Random Testing) method. We conduct an experiment using 3 extensively adopted open source vulnerable benchmarks and 2 actual websites to evaluate the ART method. The experimental results indicate that the ART method can effectively improve the fuzzing method by more than 27.1% in reducing the number of attempts before accomplishing a successful injection.

In this paper, we present a combinatorial testing methodology for automated black-box security testing of complex web applications. The focus of our work is the identification of Cross-site Scripting (XSS) vulnerabilities. We introduce a new modelling scheme for test case generation of XSS attack vectors consisting of locally optimized attack models. The modelling approach takes into account the response and behavior of the web application and is particularly efficient when used in conjunction with combinatorial testing. In addition to the modelling scheme, we present a research prototype of a security testing tool called XSSInjector, which executes attack vectors generated from our methodology against web applications. The tool also employs a newly developed test oracle for detecting XSS which allow us to precisely identify whether injected JavaScript is actually executed and thus eliminate false positives. Our testing methodology is sufficiently generic to be applied to any web application that returns HTML code. We describe the foundations of our approach and validate it via an extensive case study using a verification framework and real world web applications. In particular, we have found several new critical vulnerabilities in popular forum software, library management systems and gallery packages.

QR codes are the means to offer more direct and instant access to information. However, QR codes have shown their deficiency, being a very powerful attack vector, for example, to execute phishing attacks. In this study, we have proposed a solution that allows controlling access to the information offered by QR codes. Through a scanner designed in APP Inventor which has been called XSStudent, a system has been built that analyzes the URLs obtained and compares them with a previously trained system. This study was executed by means of a controlled attack to the users of the university who through a flyer with a QR code and a fictional link accessed an infected page with JavaScript code that allowed a successful cross-site scripting attack. The results indicate that 100% of the users are vulnerable to this type of attacks, so also, with our proposal, an attack executed in the universities using the Beef software would be totally blocked.