Biblio

Central to the secure operation of a public key infrastructure (PKI) is the ability to revoke certificates. While much of users' security rests on this process taking place quickly, in practice, revocation typically requires a human to decide to reissue a new certificate and revoke the old one. Thus, having a proper understanding of how often systems administrators reissue and revoke certificates is crucial to understanding the integrity of a PKI. Unfortunately, this is typically difficult to measure: while it is relatively easy to determine when a certificate is revoked, it is difficult to determine whether and when an administrator should have revoked.

In this paper, we use a recent widespread security vulnerability as a natural experiment. Publicly announced in April 2014, the Heartbleed OpenSSL bug, potentially (and undetectably) revealed servers' private keys. Administrators of servers that were susceptible to Heartbleed should have revoked their certificates and reissued new ones, ideally as soon as the vulnerability was publicly announced.

Using a set of all certificates advertised by the Alexa Top 1 Million domains over a period of six months, we explore the patterns of reissuing and revoking certificates in the wake of Heartbleed. We find that over 73% of vulnerable certificates had yet to be reissued and over 87% had yet to be revoked three weeks after Heartbleed was disclosed. Moreover, our results show a drastic decline in revocations on the weekends, even immediately following the Heartbleed announcement. These results are an important step in understanding the manual processes on which users rely for secure, authenticated communication.

In highly configurable systems the configuration space is too big for (re-)certifying every configuration in isolation. In this project, we combine software analysis with network analysis to detect which configuration options interact and which have local effects. Instead of analyzing a system as Linux and SELinux for every combination of configuration settings one by one (>102000 even considering compile-time configurations only), we analyze the effect of each configuration option once for the entire configuration space. The analysis will guide us to designs separating interacting configuration options in a core system and isolating orthogonal and less trusted configuration options from this core.

Information system developers and administrators often overlook critical security requirements and best practices. This may be due to lack of tools and techniques that allow practitioners to tailor security knowledge to their particular context. In order to explore the impact of new security methods, we must improve our ability to study the impact of security tools and methods on software and system development. In this paper, we present early findings of an experiment to assess the extent to which the number and type of examples used in security training stimuli can impact security problem solving. To motivate this research, we formulate hypotheses from analogical transfer theory in psychology. The independent variables include number of problem surfaces and schemas, and the dependent variable is the answer accuracy. Our study results do not show a statistically significant difference in performance when the number and types of examples are varied. We discuss the limitations, threats to validity and opportunities for future studies in this area.

Both SAT and #SAT can represent difficult problems in seemingly dissimilar areas such as planning, verification,  and probabilistic  inference. Here, we examine an expressive new language, #∃SAT, that generalizes both of these languages.   #∃SAT problems require counting the number of satisfiable formulas in a concisely-describable  set of existentially quantified, propositional formulas. We characterize the expressiveness and worst-case difficulty of #∃SAT by proving it is complete for the complexity  class #P NP [1], and re- lating this class to more familiar complexity  classes. We also experiment with three new

general-purpose #∃SAT solvers on a battery  of problem distributions  including  a simple logistics domain. Our experiments show that, despite the formidable worst-case complex-

ity of #P NP [1], many of the instances can be solved efficiently  by noticing and exploiting a particular type of frequent structure.

Online cyber threat descriptions are rich, but little research has attempted to systematically analyze these descriptions. In this paper, we process and analyze two of Symantec’s online threat description corpora. The Anti-Virus (AV) corpus contains descriptions of more than 12,400 threats detected by Symantec’s AV, and the Intrusion Prevention System (IPS) corpus contains descriptions of more than 2,700 attacks detected by Symantec’s IPS. In our analysis, we quantify the over time evolution of threat severity and type in the corpora. We also assess the amount of time Symantec takes to release signatures for newly discovered threats. Our analysis indicates that a very small minority of threats in the AV corpus are high-severity, whereas the majority of attacks in the IPS corpus are high-severity. Moreover, we find that the prevalence of different threat types such as worms and viruses in the corpora varies considerably over time. Finally, we find that Symantec prioritizes releasing signatures for fast propagating threats.

Much of the data researchers usually collect about users’ privacy and security behavior comes from short-term studies and focuses on specific, narrow activities. We present a design architecture for the Security Behavior Observatory (SBO), a client-server infrastructure designed to collect a wide array of data on user and computer behavior from a panel of hundreds of participants over several years. The SBO infrastructure had to be carefully designed to fulfill several requirements. First, the SBO must scale with the desired length, breadth, and depth of data collection. Second, we must take extraordinary care to ensure the security and privacy of the collected data, which will inevitably include intimate details about our participants' behavior. Third, the SBO must serve our research interests, which will inevitably change over the course of the study, as collected data is analyzed, interpreted, and suggest further lines of inquiry. We describe in detail the SBO infrastructure, its secure data collection methods, the benefits of our design and implementation, as well as the hurdles and tradeoffs to consider when designing such a data collection system. - See more at: https://www.cylab.cmu.edu/research/techreports/2014/tr_cylab14009.html#sthash.vsO39UdR.dpuf

WiFi fingerprint-based localization is regarded as one of the most promising techniques for indoor localization. The location of a to-be-localized client is estimated by mapping the measured fingerprint (WiFi signal strengths) against a database owned by the localization service provider. A common concern of this approach that has never been addressed in literature is that it may leak the client's location information or disclose the service provider's data privacy. In this paper, we first analyze the privacy issues of WiFi fingerprint-based localization and then propose a Privacy-Preserving WiFi Fingerprint Localization scheme (PriWFL) that can protect both the client's location privacy and the service provider's data privacy. To reduce the computational overhead at the client side, we also present a performance enhancement algorithm by exploiting the indoor mobility prediction. Theoretical performance analysis and experimental study are carried out to validate the effectiveness of PriWFL. Our implementation of PriWFL in a typical Android smartphone and experimental results demonstrate the practicality and efficiency of PriWFL in real-world environments.

Practical intrusion detection in Wireless Multihop Networks (WMNs) is a hard challenge. It has been shown that an active-probing-based network intrusion detection system (AP-NIDS) is practical for WMNs. However, understanding its interworking with real networks is still an unexplored challenge. In this paper, we investigate this in practice. We identify the general functional parameters that can be controlled, and by means of extensive experimentation, we tune these parameters and analyze the trade-offs between them, aiming at reducing false positives, overhead, and detection time. The traces we collected help us to understand when and why the active probing fails, and let us present countermeasures to prevent it.

Android "Health-DR." is innovative idea for ambulatory appliances. In rapid developing technology, we are providing "Health-DR." application for the insurance agent, dispensary, patients, physician, annals management (security) for annals. So principally, the ample of record are maintain in to the hospitals. The application just needs to be installed in the customer site with IT environment. Main purpose of our application is to provide the healthy environment to the patient. Our cream focus is on the "Health-DR." application meet to the patient regiment. For the personal use of member, we provide authentication service strategy for "Health-DR." application. Prospective strategy includes: Professional Authentications (User Authentication) by doctor to the patient, actuary and dispensary. Remote access is available to the medical annals, doctor affability and patient affability. "Health-DR." provides expertness anytime and anywhere. The application is middleware to isolate the information from affability management, client discovery and transit of database. Annotations of records are kept in the bibliography. Mainly, this paper focuses on the conversion of E-Health application with flexible surroundings.
 

Mobile ad hoc networks have the features of open medium, dynamic topology, cooperative algorithms, lack of centralized monitoring etc. Due to these, mobile ad hoc networks are much vulnerable to security attacks when compared to wired networks. There are various routing protocols that have been developed to cope up with the limitations imposed by the ad hoc networks. But none of these routing schemes provide complete unlinkability and unobservability. In this paper we have done a survey about anonymous routing and secure communications in mobile ad hoc networks. Different routing protocols are analyzed based on public/private key pairs and cryptosystems, within that USOR can well protect user privacy against both inside and outside attackers. It is a combination of group signature scheme and ID based encryption scheme. These are run during the route discovery process. We implement USOR on ns2, and then its performance is compared with AODV.

Mobile security is as critical as the PIN number on our ATM card or the lock on our front door. More than our phone itself, the information inside needs safeguarding as well. Not necessarily for scams, but just peace of mind. Android seems to have attracted the most attention from malicious code writers due to its popularity. The flexibility to freely download apps and content has fueled the explosive growth of smart phones and mobile applications but it has also introduced a new risk factor. Malware can mimic popular applications and transfer contacts, photos and documents to unknown destination servers. There is no way to disable the application stores on mobile operating systems. Fortunately for end-users, our smart phones are fundamentally open devices however they can quite easily be hacked. Enterprises now provide business applications on these devices. As a result, confidential business information resides on employee-owned device. Once an employee quits, the mobile operating system wipe-out is not an optimal solution as it will delete both business and personal data. Here we propose H-Secure application for mobile security where one can store their confidential data and files in encrypted form. The encrypted file and encryption key are stored on a web server so that unauthorized person cannot access the data. If user loses the mobile then he can login into web and can delete the file and key to stop further decryption process.

Critical Infrastructure represents the basic facilities, services and installations necessary for functioning of a community, such as water, power lines, transportation, or communication systems. Any act or practice that causes a real-time Critical Infrastructure System to impair its normal function and performance will have debilitating impact on security and economy, with direct implication on the society. SCADA (Supervisory Control and Data Acquisition) system is a control system which is widely used in Critical Infrastructure System to monitor and control industrial processes autonomously. As SCADA architecture relies on computers, networks, applications and programmable controllers, it is more vulnerable to security threats/attacks. Traditional SCADA communication protocols such as IEC 60870, DNP3, IEC 61850, or Modbus did not provide any security services. Newer standards such as IEC 62351 and AGA-12 offer security features to handle the attacks on SCADA system. However there are performance issues with the cryptographic solutions of these specifications when applied to SCADA systems. This research is aimed at improving the performance of SCADA security standards by employing NTRU, a faster and light-weight NTRU public key algorithm for providing end-to-end security.

Wireless Sensor Network has a wide range of applications including environmental monitoring and data gathering in hostile environments. This kind of network is easily leaned to different external and internal attacks because of its open nature. Sink node is a receiving and collection point that gathers data from the sensor nodes present in the network. Thus, it forms bridge between sensors and the user. A complete sensor network can be made useless if this sink node is attacked. To ensure continuous usage, it is very important to preserve the location privacy of sink nodes. A very good approach for securing location privacy of sink node is proposed in this paper. The proposed scheme tries to modify the traditional Blast technique by adding shortest path algorithm and an efficient clustering mechanism in the network and tries to minimize the energy consumption and packet delay.

Automated server parameter tuning is crucial to performance and availability of Internet applications hosted in cloud environments. It is challenging due to high dynamics and burstiness of workloads, multi-tier service architecture, and virtualized server infrastructure. In this paper, we investigate automated and agile server parameter tuning for maximizing effective throughput of multi-tier Internet applications. A recent study proposed a reinforcement learning based server parameter tuning approach for minimizing average response time of multi-tier applications. Reinforcement learning is a decision making process determining the parameter tuning direction based on trial-and-error, instead of quantitative values for agile parameter tuning. It relies on a predefined adjustment value for each tuning action. However it is nontrivial or even infeasible to find an optimal value under highly dynamic and bursty workloads. We design a neural fuzzy control based approach that combines the strengths of fast online learning and self-adaptiveness of neural networks and fuzzy control. Due to the model independence, it is robust to highly dynamic and bursty workloads. It is agile in server parameter tuning due to its quantitative control outputs. We implemented the new approach on a testbed of virtualized data center hosting RUBiS and WikiBench benchmark applications. Experimental results demonstrate that the new approach significantly outperforms the reinforcement learning based approach for both improving effective system throughput and minimizing average response time.
 

The significant dependence on cyberspace has indeed brought new risks that often compromise, exploit and damage invaluable data and systems. Thus, the capability to proactively infer malicious activities is of paramount importance. In this context, inferring probing events, which are commonly the first stage of any cyber attack, render a promising tactic to achieve that task. We have been receiving for the past three years 12 GB of daily malicious real darknet data (i.e., Internet traffic destined to half a million routable yet unallocated IP addresses) from more than 12 countries. This paper exploits such data to propose a novel approach that aims at capturing the behavior of the probing sources in an attempt to infer their orchestration (i.e., coordination) pattern. The latter defines a recently discovered characteristic of a new phenomenon of probing events that could be ominously leveraged to cause drastic Internet-wide and enterprise impacts as precursors of various cyber attacks. To accomplish its goals, the proposed approach leverages various signal and statistical techniques, information theoretical metrics, fuzzy approaches with real malware traffic and data mining methods. The approach is validated through one use case that arguably proves that a previously analyzed orchestrated probing event from last year is indeed still active, yet operating in a stealthy, very low rate mode. We envision that the proposed approach that is tailored towards darknet data, which is frequently, abundantly and effectively used to generate cyber threat intelligence, could be used by network security analysts, emergency response teams and/or observers of cyber events to infer large-scale orchestrated probing events for early cyber attack warning and notification.
 

Sources such as speakers and environments from different communication devices produce signal variations that result in interference generated by different communication devices. Despite these convolutions, signal variations produced by different mobile devices leave intrinsic fingerprints on recorded calls, thus allowing the tracking of the models and brands of engaged mobile devices. This study aims to investigate the use of recorded Voice over Internet Protocol calls in the blind identification of source mobile devices. The proposed scheme employs a combination of entropy and mel-frequency cepstrum coefficients to extract the intrinsic features of mobile devices and analyzes these features with a multi-class support vector machine classifier. The experimental results lead to an accurate identification of 10 source mobile devices with an average accuracy of 99.72%.
 

The innovations in communication and computing technologies are changing the way we carry-out the tasks in our daily lives. These revolutionary and disrupting technologies are available to the users in various hardware form-factors like Smart Phones, Embedded Appliances, Configurable or Customizable add-on devices, etc. One such technology is Bluetooth [1], which enables the users to communicate and exchange various kinds of information like messages, audio, streaming music and file transfer in a Personal Area Network (PAN). Though it enables the user to carry-out these kinds of tasks without much effort and infrastructure requirements, they inherently bring with them the security and privacy concerns, which need to be addressed at different levels. In this paper, we present an application-layer framework, which provides strong mutual authentication of applications, data confidentiality and data integrity independent of underlying operating system. It can make use of the services of different Cryptographic Service Providers (CSP) on different operating systems and in different programming languages. This framework has been successfully implemented and tested on Android Operating System on one end (using Java language) and MS-Windows 7 Operating System on the other end (using ANSI C language), to prove the framework's reliability/compatibility across OS, Programming Language and CSP. This framework also satisfies the three essential requirements of Security, i.e. Confidentiality, Integrity and Availability, as per the NIST Guide to Bluetooth Security specification and enables the developers to suitably adapt it for different kinds of applications based on Bluetooth Technology.

State-level intrusion in the cyberspace of another country seriously threatens a state's peace and security. Consequently many types of cyberspace intrusion are being referred to as cyber war with scant regard to the legal position under international law. This is but one of the challenges facing state-level cyber intrusion. The current rules of international law prohibit certain types of intrusion. However, international law does not define which intrusion fall within the prohibited category of intrusion nor when the threshold of intrusion is surpassed. International lawyers have to determine the type of intrusion and threshold on a case-by-case basis. The Tallinn Manual may serve as guideline in this assessment, but determination of the type of intrusion and attribution to a specific state is not easily established. The current rules of international law do not prohibit all intrusion which on statelevel may be highly invasive and destructive. Unrestrained cyber intrusion may result in cyberspace becoming a battle space in which state(s) with strong cyber abilities dominate cyberspace resulting in resentment and fear among other states. The latter may be prevented on an international level by involving all states on an equal and transparent manner in cyberspace governance.
 

Educational software systems have an increasingly significant presence in engineering sciences. They aim to improve students' attitudes and knowledge acquisition typically through visual representation and simulation of complex algorithms and mechanisms or hardware systems that are often not available to the educational institutions. This paper presents a novel software system for CryptOgraphic ALgorithm visuAl representation (COALA), which was developed to support a Data Security course at the School of Electrical Engineering, University of Belgrade. The system allows users to follow the execution of several complex algorithms (DES, AES, RSA, and Diffie-Hellman) on real world examples in a step by step detailed view with the possibility of forward and backward navigation. Benefits of the COALA system for students are observed through the increase of the percentage of students who passed the exam and the average grade on the exams during one school year.
 

Remote user authentication using smart cards is a method of verifying the legitimacy of remote users accessing the server through insecure channel, by using smart cards to increase the efficiency of the system. During last couple of years many protocols to authenticate remote users using smart cards have been proposed. But unfortunately, most of them are proved to be unsecure against various attacks. Recently this year, Yung-Cheng Lee improved Shin et al.'s protocol and claimed that their protocol is more secure. In this article, we have shown that Yung-Cheng-Lee's protocol too has defects. It does not provide user anonymity; it is vulnerable to Denial-of-Service attack, Session key reveal, user impersonation attack, Server impersonation attack and insider attacks. Further it is not efficient in password change phase since it requires communication with server and uses verification table.
 

In the cyber crime huge log data, transactional data occurs which tends to plenty of data for storage and analyze them. It is difficult for forensic investigators to play plenty of time to find out clue and analyze those data. In network forensic analysis involves network traces and detection of attacks. The trace involves an Intrusion Detection System and firewall logs, logs generated by network services and applications, packet captures by sniffers. In network lots of data is generated in every event of action, so it is difficult for forensic investigators to find out clue and analyzing those data. In network forensics is deals with analysis, monitoring, capturing, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper focuses on data collection from the cyber system and web browser. The FTK 4.0 is discussing for memory forensic analysis and remote system forensic which is to be used as evidence for aiding investigation.