Biblio

The evolution of cloud gaming systems is substantially the security requirements for computer games. Although online game development often utilizes artificial intelligence and human computer interaction, game developers and providers often do not pay much attention to security techniques. In cloud gaming, location-based games are augmented reality games which take the original principals of the game and applies them to the real world. In other terms, it uses the real world to impact the game experience. Because the execution of such games is distributed in cloud computing, users cannot be certain where their input and output data are managed. This introduces the possibility to input incorrect data in the exchange between the gamer's terminal and the gaming platform. In this context, we propose a new gaming concept for augmented reality and location-based games in order to solve the aforementioned cheating scenario problem. The merit of our approach is to establish an accurate and verifiable proof that the gamer reached the goal or found the target. The major novelty in our method is that it allows the gamer to submit an authenticated proof related to the game result without altering the privacy of positioning data.

SQL injection attack (SQLIA) pose a serious security threat to the database driven web applications. This kind of attack gives attackers easily access to the application's underlying database and to the potentially sensitive information these databases contain. A hacker through specifically designed input, can access content of the database that cannot otherwise be able to do so. This is usually done by altering SQL statements that are used within web applications. Due to importance of security of web applications, researchers have studied SQLIA detection and prevention extensively and have developed various methods. In this research, after reviewing the existing research in this field, we present a new hybrid method to reduce the vulnerability of the web applications. Our method is specifically designed to detect and prevent SQLIA. Our proposed method is consists of three phases namely, the database design, implementation, and at the common gateway interface (CGI). Details of our approach along with its pros and cons are discussed in detail.

Hacker forums and other social platforms may contain vital information about cyber security threats. But using manual analysis to extract relevant threat information from these sources is a time consuming and error-prone process that requires a significant allocation of resources. In this paper, we explore the potential of Machine Learning methods to rapidly sift through hacker forums for relevant threat intelligence. Utilizing text data from a real hacker forum, we compared the text classification performance of Convolutional Neural Network methods against more traditional Machine Learning approaches. We found that traditional machine learning methods, such as Support Vector Machines, can yield high levels of performance that are on par with Convolutional Neural Network algorithms.

Deep web, a hidden and encrypted network that crawls beneath the surface web today has become a social hub for various criminals who carry out their crime through the cyber space and all the crime is being conducted and hosted on the Deep Web. This research paper is an effort to bring forth various techniques and ways in which an internet user can be safe online and protect his privacy through anonymity. Understanding how user's data and private information is phished and what are the risks of sharing personal information on social media.

Intrusion Detection Systems (IDS) have been in existence for many years now, but they fall short in efficiently detecting zero-day attacks. This paper presents an organic combination of Semantic Link Networks (SLN) and dynamic semantic graph generation for the on the fly discovery of zero-day attacks using the Spark Streaming platform for parallel detection. In addition, a minimum redundancy maximum relevance (MRMR) feature selection algorithm is deployed to determine the most discriminating features of the dataset. Compared to previous studies on zero-day attack identification, the described method yields better results due to the semantic learning and reasoning on top of the training data and due to the use of collaborative classification methods. We also verified the scalability of our method in a distributed environment.

Internet plays a crucial role in today's life, so the usage of online social network monotonically increasing. People can share multimedia information's fastly and keep in touch or communicate with friend's easily through online social network across the world. Security in authentication is a big challenge in online social network and authentication is a preliminary process for identifying legitimate user. Conventionally, we are using alphanumeric textbased password for authentication approach. But the main flaw points of text based password is highly vulnerable to attacks and difficulty of recalling password during authentication time due to the irregular use of passwords. To overcome the shortcoming of text passwords, we propose a Graphical Password authentication. An approach of Graphical Password is an authentication of amalgam of pictures. It is less vulnerable to attacks and human can easily recall pictures better than text. So the graphical password is a better alternative to text passwords. As the image uploads are increasing by users share through online site, privacy preserving has become a major problem. So we need a Caption Based Metadata Stratification of images for delivers an automatic suggestion of similar category already in database, it works by comparing the caption metadata of album with caption metadata already in database or extract the synonyms of caption metadata of new album for checking the similarity with caption metadata already in database. This stratification offers an enhanced automatic privacy prediction for uploaded images in online social network, privacy is an inevitable factor for uploaded images, and privacy violation is a major concern. So we propose an Automatic Policy Prediction for uploaded images that are classified by caption metadata. An automatic policy prediction is a hassle-free privacy setting proposed to the user.

Most of the social media platforms generate a massive amount of raw data that is slow-paced. On the other hand, Internet Relay Chat (IRC) protocol, which has been extensively used by hacker community to discuss and share their knowledge, facilitates fast-paced and real-time text communications. Previous studies of malicious IRC behavior analysis were mostly either offline or batch processing. This results in a long response time for data collection, pre-processing, and threat detection. However, since the threats can use the latest vulnerabilities to exploit systems (e.g. zero-day attack) and which can spread fast using IRC channels. Current IRC channel monitoring techniques cannot provide the required fast detection and alerting. In this paper, we present an alternative approach to overcome this limitation by providing real-time and autonomic threat detection in IRC channels. We demonstrate the capabilities of our approach using as an example the shadow brokers' leak exploit (the exploit leveraged by WannaCry ransomware attack) that was captured and detected by our framework.

Server honey pots are computer systems that hide in a network capturing attack packets. As the name goes, server honey pots are installed in server machines running a set of services. Enterprises and government organisations deploy these honey pots to know the extent of attacks on their network. Since, most of the recent attacks are advanced persistent attacks there is much research work going on in building better peripheral security measures. In this paper, the authors have deployed several honey pots in a virtualized environment to gather traces of malicious activities. The network infrastructure is resilient and provides much information about hacker's activities. It is cost-effective and can be easily deployed in any organisation without specialized hardware.

This study focuses on the spatial context of hacking to networks of Honey-pots. We investigate the relationship between topological positions and geographic positions of victimized computers and system trespassers. We've deployed research Honeypots on the computer networks of two academic institutions, collected information on successful brute force attacks (BFA) and system trespassing events (sessions), and used Social Network Analysis (SNA) techniques, to depict and understand the correlation between spatial attributes (IP addresses) and hacking networks' topology. We mapped and explored hacking patterns and found that geography might set the behavior of the attackers as well as the topology of hacking networks. The contribution of this study stems from the fact that there are no prior studies of geographical influences on the topology of hacking networks and from the unique usage of SNA to investigate hacking activities. Looking ahead, our study can assist policymakers in forming effective policies in the field of cybercrime.

The energy sector has been actively looking into cyber risk assessment at a global level, as it has a ripple effect; risk taken at one step in supply chain has an impact on all the other nodes. Cyber-attacks not only hinder functional operations in an organization but also waves damaging effects to the reputation and confidence among shareholders resulting in financial losses. Organizations that are open to the idea of protecting their assets and information flow and are equipped; enough to respond quickly to any cyber incident are the ones who prevail longer in global market. As a contribution we put forward a modular plan to mitigate or reduce cyber risks in global supply chain by identifying potential cyber threats at each step and identifying their immediate counterm easures.

Security breaches and attacks are becoming a more critical and, simultaneously, a challenging problems for many firms in networked supply chains. A game theory-based model is developed to investigate how interdependent feature of information security risk influence the optimal strategy of firms to invest in information security. The equilibrium levels of information security investment under non-cooperative game condition are compared with socially optimal solutions. The results show that the infectious risks often induce firms to invest inefficiently whereas trust risks lead to overinvest in information security. We also find that firm's investment may not necessarily monotonous changes with infectious risks and trust risks in a centralized case. Furthermore, relative to the socially efficient level, firms facing infectious risks may invest excessively depending on whether trust risks is large enough.

Cybersecurity is a problem of growing relevance that impacts all facets of society. As a result, many researchers have become interested in studying cybercriminals and online hacker communities in order to develop more effective cyber defenses. In particular, analysis of hacker community contents may reveal existing and emerging threats that pose great risk to individuals, businesses, and government. Thus, we are interested in developing an automated methodology for identifying tangible and verifiable evidence of potential threats within hacker forums, IRC channels, and carding shops. To identify threats, we couple machine learning methodology with information retrieval techniques. Our approach allows us to distill potential threats from the entirety of collected hacker contents. We present several examples of identified threats found through our analysis techniques. Results suggest that hacker communities can be analyzed to aid in cyber threat detection, thus providing promising direction for future work.

Phishing is an online security attack in which the hacker aims in harvesting sensitive information like passwords, credit card information etc. from the users by making them to believe what they see is what it is. This threat has been into existence for a decade and there has been continuous developments in counter attacking this threat. However, statistical study reveals how phishing is still a big threat to today's world as the online era booms. In this paper, we look into the art of phishing and have made a practical analysis on how the state of the art anti-phishing systems fail to prevent Phishing. With the loop-holes identified in the state-of-the-art systems, we move ahead paving the roadmap for the kind of system that will counter attack this online security threat more effectively.

The development of future cyber terrorism scenarios is a key component in building a more comprehensive understanding of cyber threats that are likely to emerge in the near-to mid-term future. While developing concepts of likely new, emerging digital technologies is an important part of this process, this article suggests that understanding the psychological and social forces involved in cyber terrorism is also a key component in the analysis and that the synergy of these two dimensions may produce more accurate and detailed future cyber threat scenarios than either analytical element alone.

Most existing approaches focus on examining the values are dangerous for information flow within inter-suspicious modules of cloud applications (apps) in a host by using malware threat analysis, rather than the risk posed by suspicious apps were connected to the cloud computing server. Accordingly, this paper proposes a taint propagation analysis model incorporating a weighted spanning tree analysis scheme to track data with taint marking using several taint checking tools. In the proposed model, Android programs perform dynamic taint propagation to analyse the spread of and risks posed by suspicious apps were connected to the cloud computing server. In determining the risk of taint propagation, risk and defence capability are used for each taint path for assisting a defender in recognising the attack results against network threats caused by malware infection and estimate the losses of associated taint sources. Finally, a case of threat analysis of a typical cyber security attack is presented to demonstrate the proposed approach. Our approach verified the details of an attack sequence for malware infection by incorporating a finite state machine (FSM) to appropriately reflect the real situations at various configuration settings and safeguard deployment. The experimental results proved that the threat analysis model allows a defender to convert the spread of taint propagation to loss and practically estimate the risk of a specific threat by using behavioural analysis with real malware infection.

Information threatening the security of critical infrastructures are exchanged over the Internet through communication platforms, such as online discussion forums. This information can be used by malicious hackers to attack critical computer networks and data systems. Much of the literature on the hacking of critical infrastructure has focused on developing typologies of cyber-attacks, but has not examined the communication activities of the actors involved. To address this gap in the literature, the language of hackers was analyzed to identify potential threats against critical infrastructures using automated analysis tools. First, discussion posts were collected from a selected hacker forum using a customized web-crawler. Posts were analyzed using a parts of speech tagger, which helped determine a list of keywords used to query the data. Next, a sentiment analysis tool scored these keywords, which were then analyzed to determine the effectiveness of this method.

Sony in United States and KHNP in South Korea were hit by a series of cyberattacks late in 2014 that were blamed on North Korea. U.S. president Obama responded strongly and positively as control tower, and led Sony do not surrender to hacker's demand. U.S government demonstrated retaliatory action against North Korea under the proportional principle, blacklisted 3 North Korean entities and 10 officials. That days, there was the outrage of internet of North Korea. In order to enhance the cyber security response capability, U.S created a new office, CTIIC and encouraged the development of ISAOs, and made Sanctions EO, Information Sharing EO etc. KHNP and the Ministry of Industry rectified incidents itself early period when cyber incident arose, and the situation did not recovered as quickly as desired. S. Korea had not retaliation actions, otherwise called for closer global cooperation against cyber-attacks. To enhance national cyber security and resilience, S. Korea government created the new post of presidential secretary for cyber security and draw up `Strengthening National Cyber Security Posture' initiative.

Cloud computing is an emerging paradigm shifting the shape of computing models from being a technology to a utility. However, security, privacy and trust are amongst the issues that can subvert the benefits and hence wide deployment of cloud computing. With the introduction of omnipresent mobile-based clients, the ubiquity of the model increases, suggesting a still higher integration in life. Nonetheless, the security issues rise to a higher degree as well. The constrained input methods for credentials and the vulnerable wireless communication links are among factors giving rise to serious security issues. To strengthen the access control of cloud resources, organizations now commonly acquire Identity Management Systems (IdM). This paper presents that the most popular IdM, namely OAuth, working in scope of Mobile Cloud Computing has many weaknesses in authorization architecture. In particular, authors find two major issues in current IdM. First, if the IdM System is compromised through malicious code, it allows a hacker to get authorization of all the protected resources hosted on a cloud. Second, all the communication links among client, cloud and IdM carries complete authorization token, that can allow hacker, through traffic interception at any communication link, an illegitimate access of protected resources. We also suggest a solution to the reported problems, and justify our arguments with experimentation and mathematical modeling.

Analysing cyber attack environments yield tremendous insight into adversory behavior, their strategy and capabilities. Designing cyber intensive games that promote offensive and defensive activities to capture or protect assets assist in the understanding of cyber situational awareness. There exists tangible metrics to characterizing games such as CTFs to resolve the intensity and aggression of a cyber attack. This paper synthesizes the characteristics of InCTF (India CTF) and provides an understanding of the types of vulnerabilities that have the potential to cause significant damage by trained hackers. The two metrics i.e. toxicity and effectiveness and its relation to the final performance of each team is detailed in this context.
 

Persisting to ignore the consequences of Cyber Warfare will bring severe concerns to all people. Hackers and governments alike should understand the barriers of which their methods take them. Governments use Cyber Warfare to give them a tactical advantage over other countries, defend themselves from their enemies or to inflict damage upon their adversaries. Hackers use Cyber Warfare to gain personal information, commit crimes, or to reveal sensitive and beneficial intelligence. Although both methods can provide ethical uses, the equivalent can be said at the other end of the spectrum. Knowing and comprehending these devices will not only strengthen the ability to detect these attacks and combat against them but will also provide means to divulge despotic government plans, as the outcome of Cyber Warfare can be worse than the outcome of conventional warfare. The paper discussed the concept of ethics and reasons that led to use information technology in military war, the effects of using cyber war on civilians, the legality of the cyber war and ways of controlling the use of information technology that may be used against civilians. This research uses a survey methodology to overlook the awareness of Arab citizens towards the idea of cyber war, provide findings and evidences of ethics behind the offensive cyber warfare. Detailed strategies and approaches should be developed in this aspect. The author recommended urging the scientific and technological research centers to improve the security and develop defending systems to prevent the use of technology in military war against civilians.
 

In order to strengthen network security and improve the network's active defense intrusion detection capabilities, this paper presented and established one active defense intrusion detection system which based on the mixed interactive honeypot. The system can help to reduce the false information, enhance the stability and security of the network. Testing and simulation experiments show that: the system improved active defense of the network's security, increase the honeypot decoy capability and strengthen the attack predictive ability. So it has better application and promotion value.
 

Cloud Computing means that a relationship of many number of computers through a contact channel like internet. Through cloud computing we send, receive and store data on internet. Cloud Computing gives us an opportunity of parallel computing by using a large number of Virtual Machines. Now a days, Performance, scalability, availability and security may represent the big risks in cloud computing. In this paper we highlights the issues of security, availability and scalability issues and we will also identify that how we make our cloud computing based infrastructure more secure and more available. And we also highlight the elastic behavior of cloud computing. And some of characteristics which involved for gaining the high performance of cloud computing will also be discussed.
 

In bound applications, the locations of events reportable by a device network have to be compelled to stay anonymous. That is, unauthorized observers should be unable to notice the origin of such events by analyzing the network traffic. The authors analyze 2 forms of downsides: Communication overhead and machine load problem. During this paper, the authors give a new framework for modeling, analyzing, and evaluating obscurity in device networks. The novelty of the proposed framework is twofold: initial, it introduces the notion of "interval indistinguishability" and provides a quantitative live to model obscurity in wireless device networks; second, it maps supply obscurity to the applied mathematics downside the authors showed that the present approaches for coming up with statistically anonymous systems introduce correlation in real intervals whereas faux area unit unrelated. The authors show however mapping supply obscurity to consecutive hypothesis testing with nuisance Parameters ends up in changing the matter of exposing non-public supply data into checking out associate degree applicable knowledge transformation that removes or minimize the impact of the nuisance data victimization sturdy cryptography algorithmic rule. By doing therefore, the authors remodeled the matter of analyzing real valued sample points to binary codes, that opens the door for committal to writing theory to be incorporated into the study of anonymous networks. In existing work, unable to notice unauthorized observer in network traffic. However this work in the main supported enhances their supply obscurity against correlation check, the most goal of supply location privacy is to cover the existence of real events.

Black-box web application vulnerability scanners are automated tools that probe web applications for security vulnerabilities. In order to assess the current state of the art, we obtained access to eight leading tools and carried out a study of: (i) the class of vulnerabilities tested by these scanners, (ii) their effectiveness against target vulnerabilities, and (iii) the relevance of the target vulnerabilities to vulnerabilities found in the wild. To conduct our study we used a custom web application vulnerable to known and projected vulnerabilities, and previous versions of widely used web applications containing known vulnerabilities. Our results show the promise and effectiveness of automated tools, as a group, and also some limitations. In particular, "stored" forms of Cross Site Scripting (XSS) and SQL Injection (SQLI) vulnerabilities are not currently found by many tools. Because our goal is to assess the potential of future research, not to evaluate specific vendors, we do not report comparative data or make any recommendations about purchase of specific tools.