Biblio
The data accessibility anytime and anywhere is nowadays the key feature for information technology enabled by the ubiquitous network system for huge applications. However, security and privacy are perceived as primary obstacles to its wide adoption when it is applied to the end user application. When sharing sensitive information, personal s' data protection is the paramount requirement for the security and privacy to ensure the trustworthiness of the service provider. To this end, this paper proposes communication security protocol to achieve data protection when a user is sending his sensitive data to the network through gateway. We design a cipher content and key exchange computation process. Finally, the performance analysis of the proposed scheme ensure the honesty of the gateway service provider, since the user has the ability to control who has access to his data by issuing a cryptographic access credential to data users.
Tactical networks are generally simple ad-hoc networks in design, however, this simple design often gets complicated, when heterogeneous wireless technologies have to work together to enable seamless multi-hop communications across multiple sessions. In recent years, there has been some significant advances in computational, radio, localization, and networking te, and session's rate i.e., aggregate capacity averaged over a 4-time-slot frame)chnologies, which motivate a clean slate design of the control plane for multi-hop tactical wireless networks. In this paper, we develop a global network optimization framework, which characterizes the control plane for multi-hop wireless tactical networks. This framework abstracts the underlying complexity of tactical wireless networks and orchestrates the the control plane functions. Specifically, we develop a cross-layer optimization framework, which characterizes the interaction between the physical, link, and network layers. By applying the framework to a throughput maximization problem, we show how the proposed framework can be utilized to solve a broad range of wireless multi-hop tactical networking problems.
Interchange of information through cell phones, Tabs and PDAs (Personal Digital Assistant) is the new trend in the era of digitization. In day-to-day activities, sensitive information through mobile phones is exchanged among the users. This sensitive information can be in the form of text messages, images, location, etc. The research on Android mobile applications was done at the MIT, and found that applications are leaking enormous amount of information to the third party servers. 73 percent of 55 Android applications were detected to leak personal information of the users [8]. Transmission of files securely on Android is a big issue. Therefore it is important to shield the privacy of user data on Android operating system. The main motive of this paper is to protect the privacy of data on Android Platform by allowing transmission of textual data, location, pictures in encrypted format. By doing so, we achieved intimacy and integrity of data.
Social media plays an integral part in individual's everyday lives as well as for companies. Social media brings numerous benefits in people's lives such as to keep in touch with close ones and specially with relatives who are overseas, to make new friends, buy products, share information and much more. Unfortunately, several threats also accompany the countless advantages of social media. The rapid growth of the online social networking sites provides more scope for criminals and cyber-criminals to carry out their illegal activities. Hackers have found different ways of exploiting these platform for their malicious gains. This research englobes some of the common threats on social media such as spam, malware, Trojan horse, cross-site scripting, industry espionage, cyber-bullying, cyber-stalking, social engineering attacks. The main purpose of the study to elaborates on phishing, malware and click-jacking attacks. The main purpose of the research, there is no particular research available on the forensic investigation for Facebook. There is no particular forensic investigation methodology and forensic tools available which can follow on the Facebook. There are several tools available to extract digital data but it's not properly tested for Facebook. Forensics investigation tool is used to extract evidence to determine what, when, where, who is responsible. This information is required to ensure that the sufficient evidence to take legal action against criminals.
This paper presents a novel sensor parameter fault diagnosis method for generally multiple-input multiple-output (MIMO) affine nonlinear systems based on adaptive observer. Firstly, the affine nonlinear systems are transformed into the particular systems via diffeomorphic transformation using Lie derivative. Then, based on the techniques of high-gain observer and adaptive estimation, an adaptive observer structure is designed with simple method for jointly estimating the states and the unknown parameters in the output equation of the nonlinear systems. And an algorithm of the fault estimation is derived. The global exponential convergence of the proposed observer is proved succinctly. Also the proposed method can be applied to the fault diagnosis of generally affine nonlinear systems directly by the reversibility of aforementioned coordinate transformation. Finally, a numerical example is presented to illustrate the efficiency of the proposed fault diagnosis scheme.
Parameter estimation in wireless sensor networks (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a non-binary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R $ε$ 1, 2, 3,..., encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented.
Once we had tried to propose an unbreakable CAPTCHA and we reached a result that limitation of time is effect to prevent computers from recognizing characters accurately while computers can finally recognize all text-based CAPTCHA in unlimited time. One of the existing usual ways to prevent computers from recognizing characters is distortion, and adding noise is also effective for the prevention. However, these kinds of prevention also make recognition of characters by human beings difficult. As a solution of the problems, an effective text-based CAPTCHA algorithm with amodal completion was proposed by our team. Our CAPTCHA causes computers a large amount of calculation costs while amodal completion helps human beings to recognize characters momentarily. Our CAPTCHA has evolved with aftereffects and combinations of complementary colors. We evaluated our CAPTCHA with deep learning which is attracting the most attention since deep learning is faster and more accurate than existing methods for recognition with computers. In this paper, we add jagged lines to edges of characters since edges are one of the most important parts for recognition in deep learning. In this paper, we also evaluate that how much the jagged lines decrease recognition of human beings and how much they prevent computers from the recognition. We confirm the effects of our method to deep learning.
Phishing emails have affected users seriously due to the enormous increasing in numbers and exquisite camouflage. Users spend much more effort on distinguishing the email properties, therefore current phishing email detection system demands more creativity and consideration in filtering for users. The proposed research tries to adopt creative computing in detecting phishing emails for users through a combination of computing techniques and social engineering concepts. In order to achieve the proposed target, the fraud type is summarised in social engineering criteria through literature review; a semantic web database is established to extract and store information; a fuzzy logic control algorithm is constructed to allocate email categories. The proposed approach will help users to distinguish the categories of emails, furthermore, to give advice based on different categories allocation. For the purpose of illustrating the approach, a case study will be presented to simulate a phishing email receiving scenario.
This paper proposes a context-aware, graph-based approach for identifying anomalous user activities via user profile analysis, which obtains a group of users maximally similar among themselves as well as to the query during test time. The main challenges for the anomaly detection task are: (1) rare occurrences of anomalies making it difficult for exhaustive identification with reasonable false-alarm rate, and (2) continuously evolving new context-dependent anomaly types making it difficult to synthesize the activities apriori. Our proposed query-adaptive graph-based optimization approach, solvable using maximum flow algorithm, is designed to fully utilize both mutual similarities among the user models and their respective similarities with the query to shortlist the user profiles for a more reliable aggregated detection. Each user activity is represented using inputs from several multi-modal resources, which helps to localize anomalies from time-dependent data efficiently. Experiments on public datasets of insider threats and gesture recognition show impressive results.
In previous work, we proposed a solution to facilitate access to computer science related courses and learning materials using cloud computing and mobile technologies. The solution was positively evaluated by the participants, but most of them indicated that it lacks support for laboratory activities. As it is well known that many of computer science subjects (e.g. Computer Networks, Information Security, Systems Administration, etc.) require a suitable and flexible environment where students can access a set of computers and network devices to successfully complete their hands-on activities. To achieve this criteria, we created a cloud-based virtual laboratory based on OpenStack cloud platform to facilitate access to virtual machine both locally and remotely. Cloud-based virtual labs bring a lot of advantages, such as increased manageability, scalability, high availability and flexibility, to name a few. This arrangement has been tested in a case-study exercise with a group of students as part of Computer Networks and System Administration courses at Kabul Polytechnic University in Afghanistan. To measure success, we introduced a level test to be completed by participants prior and after the experiment. As a result, the learners achieved an average of 17.1 % higher scores in the post level test after completing the practical exercises. Lastly, we distributed a questionnaire after the experiment and students provided positive feedback on the effectiveness and usefulness of the proposed solution.
Large-scale infrastructures are critical to economic and social development, and hence their continued performance and security are of high national importance. Such an infrastructure often is a system of systems, and its functionality critically depends on the inherent robustness of its constituent systems and its defense strategy for countering attacks. Additionally, interdependencies between the systems play another critical role in determining the infrastructure robustness specified by its survival probability. In this paper, we develop game-theoretic models between a defender and an attacker for a generic system of systems using inherent parameters and conditional survival probabilities that characterize the interdependencies. We derive Nash Equilibrium conditions for the cases of interdependent and independent systems of systems under sum-form utility functions. We derive expressions for the infrastructure survival probability that capture its dependence on cost and system parameters, and also on dependencies that are specified by conditional probabilities. We apply the results to cyber-physical systems which show the effects on system survival probability due to defense and attack intensities, inherent robustness, unit cost, target valuation, and interdependencies.
Building lightweight security for low-cost pervasive devices is a major challenge considering the design requirements of a small footprint and low power consumption. Physical Unclonable Functions (PUFs) have emerged as a promising technology to provide a low-cost authentication for such devices. By exploiting intrinsic manufacturing process variations, PUFs are able to generate unique and apparently random chip identifiers. Strong-PUFs represent a variant of PUFs that have been suggested for lightweight authentication applications. Unfortunately, many of the Strong-PUFs have been shown to be susceptible to modelling attacks (i.e., using machine learning techniques) in which an adversary has access to challenge and response pairs. In this study, we propose an obfuscation technique during post-processing of Strong-PUF responses to increase the resilience against machine learning attacks. We conduct machine learning experiments using Support Vector Machines and Artificial Neural Networks on two Strong-PUFs: a 32-bit Arbiter-PUF and a 2-XOR 32-bit Arbiter-PUF. The predictability of the 32-bit Arbiter-PUF is reduced to $\approx$ 70% by using an obfuscation technique. Combining the obfuscation technique with 2-XOR 32-bit Arbiter-PUF helps to reduce the predictability to $\approx$ 64%. More reduction in predictability has been observed in an XOR Arbiter-PUF because this PUF architecture has a good uniformity. The area overhead with an obfuscation technique consumes only 788 and 1080 gate equivalents for the 32-bit Arbiter-PUF and 2-XOR 32-bit Arbiter-PUF, respectively.
This paper introduces a multi-factors security key generation mechanism for self-organising Internet of Things (IoT) network and nodes. The mechanism enables users to generate unique set of security keys to enhance IoT security while meeting various business needs. The multi-factor security keys presents an additional security layer to existing security standards and practices currently being adopted by the IoT community. The proposed security key generation mechanism enables user to define and choose any physical and logical parameters he/she prefers, in generating a set of security keys to be encrypted and distributed to registered IoT nodes. IoT applications and services will only be activated after verifying that all security keys are present. Multiple levels of authorisation for different user groups can be easily created through the mix and match of the generated multi-factors security keys. A use case, covering indoor and outdoor field tests was conducted. The results of the tests showed that the mechanism is easily adaptable to meet diverse multivendor IoT devices and is scalable for various applications.
The increasing demand for secure interactions between network domains brings in new challenges to access control technologies. In this paper we design an access control framework which provides a multilevel mapping method between hierarchical access control structures for achieving multilevel security protection in cross-domain networks. Hierarchical access control structures ensure rigorous multilevel security in intra domains. And the mapping method based on subject attributes is proposed to determine the subject's security level in its target domain. Experimental results we obtained from simulations are also reported in this paper to verify the effectiveness of the proposed access control model.
Because of the nature of vehicular communications, security is a crucial aspect, involving the continuous development and analysis of the existing security architectures and punctual theoretical and practical aspects that have been proposed and are in need of continuous updates and integrations with newer technologies. But before an update, a good knowledge of the current aspects is mandatory. Identifying weaknesses and anticipating possible risks of vehicular communication networks through a failure modes and effects analysis (FMEA) represent an important aspect of the security analysis process and a valuable step in finding efficient security solutions for all kind of problems that might occur in these systems.
Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-text oriented topic evolution model sdTEM. We carry out experimental studies on Twitter dataset. The results demonstrate the effectiveness of our method to monitor social media topic evolution compared to the baseline methods.
Many a time's assumptions are key to inventions. One such notion in recent past is about data exchange between two disjoint computer systems. It is always assumed that, if any two computers are separated physically without any inter communication, it is considered to be very secure and will not be compromised, the exchange of data between them would be impossible. But recent growth in the field of computers emphasizes the requirements of security analysis. One such security concern is with the air-gapped systems. This paper deals with the flaws and flow of air-gapped systems.
With Android application packing technology evolving, there are more and more ways to harden APPs. Manually unpacking APPs becomes more difficult as the time needed for analyzing increase exponentially. At the beginning, the packing technology is designed to prevent APPs from being easily decompiled, tampered and re-packed. But unfortunately, many malicious APPs start to use packing service to protect themselves. At present, most of the antivirus software focus on APPs that are unpacked, which means if malicious APPs apply the packing service, they can easily escape from a lot of antivirus software. Therefore, we should not only emphasize the importance of packing, but also concentrate on the unpacking technology. Only by doing this can we protect the normal APPs, and not miss any harmful APPs at the same time. In this paper, we first systematically study a lot of DEX packing and unpacking technologies, then propose and develop a universal unpacking system, named CrackDex, which is capable of extracting the original DEX file from the packed APP. We propose three core technologies: simulation execution, DEX reassembling, and DEX restoration, to get the unpacked DEX file. CrackDex is a part of the Dalvik virtual machine, and it monitors the execution of functions to locate the unpacking point in the portable interpreter, then launches the simulation execution, collects the data of original DEX file through corresponding structure pointer, finally fulfills the unpacking process by reassembling the data collected. The results of our experiments show that CrackDex can be used to effectively unpack APPs that are packed by packing service in a universal approach without any other knowledge of packing service.
This paper explores the process of collective crisis problem-solving in the darkweb. We conducted a preliminary study on one of the Tor-based darkweb forums, during the shutdown of two marketplaces. Content analysis suggests that distrust permeated the forum during the marketplace shutdowns. We analyzed the debates concerned with suspicious claims and conspiracies. The results suggest that a black-market crisis potentially offers an opportunity for cyber-intelligence to disrupt the darkweb by engendering internal conflicts. At the same time, the study also shows that darkweb members were adept at reaching collective solutions by sharing new market information, more secure technologies, and alternative routes for economic activities.
SQL Injection is one of the most critical security vulnerability in web applications. Most web applications use SQL as web applications. SQL injection mainly affects these websites and web applications. An attacker can easily bypass a web applications authentication and authorization and get access to the contents they want by SQL injection. This unauthorised access helps the attacker to retrieve confidential data's, trade secrets and can even delete or modify valuable documents. Even though, to an extend many preventive measures are found, till now there are no complete solution for this problem. Hence, from the surveys and analyses done, an enhanced methodology is proposed against SQL injection disclosure and deterrence by ensuring proper authentication using Heisenberg analysis and password security using Honey pot mechanism.
In this paper, we propose a lightweight multi-receiver encryption scheme for the device to device communications on Internet of Things (IoT) applications. In order for the individual user to control the disclosure range of his/her own data directly and to prevent sensitive personal data disclosure to the trusted third party, the proposed scheme uses device-generated public keys. For mutual authentication, third party generates Schnorr-like lightweight identity-based partial private keys for users. The proposed scheme provides source authentication, message integrity, replay-attack prevention and implicit user authentication. In addition to more security properties, computation expensive pairing operations are eliminated to achieve less time usage for both sender and receiver, which is favourable property for IoT applications. In this paper, we showed a proof of security of our scheme, computational cost comparison and experimental performance evaluations. We implemented our proposed scheme on real embedded Android devices and confirmed that it achieves less time cost for both encryption and decryption comparing with the existing most efficient certificate-based multi-receiver encryption scheme and certificateless multi-receiver encryption scheme.
Named Data Networking (NDN) is one of the future internet architectures, which is a clean-slate approach. NDN provides intelligent data retrieval using the principles of name-based symmetrical forwarding of Interest/Data packets and innetwork caching. The continually increasing demand for rapid dissemination of large-scale scientific data is driving the use of NDN in data-intensive science experiments. In this paper, we establish an intercontinental NDN testbed. In the testbed, an NDN-based application that targets climate science as an example data intensive science application is designed and implemented, which has differentiated features compared to those of previous studies. We verify experimental justification of using NDN for climate science in the intercontinental network, through performance comparisons between classical delivery techniques and NDN-based climate data delivery.
The rapid growth of science and technology in the telecommunications world can come up with new ways for some people bent on abusing for threatening information security as hackers, crackers, carder, phreaker and so on. If the information is on the wrong side will result in losses. Information that must be considered is the security of confidential information. Steganography is a method that can be used to hide a message by using digital media. Digital Steganography using digital media as the container vessel such as images, sounds, text, and video. Hidden secret data can also include images, audio, text, and video. In this final audio steganography implemented. One method that can be used in steganography is the Least Significant Bit (LSB). Steganography implementation will be accompanied by the application of cryptography in the form of encryption and decryption. This method works is messages that have been encrypted beforehand will be hidden evenly on each region in MP3 or WAV already divided, with modify / change the LSB of the media container with the bits of information to be hidden. In making the steganography application, the author uses the Java programming language eclipse, because the program is quite easy and can be run in the Android smartphone operating system.
Organizations are experiencing an ever-growing concern of how to identify and defend against insider threats. Those who have authorized access to sensitive organizational data are placed in a position of power that could well be abused and could cause significant damage to an organization. This could range from financial theft and intellectual property theft to the destruction of property and business reputation. Traditional intrusion detection systems are neither designed nor capable of identifying those who act maliciously within an organization. In this paper, we describe an automated system that is capable of detecting insider threats within an organization. We define a tree-structure profiling approach that incorporates the details of activities conducted by each user and each job role and then use this to obtain a consistent representation of features that provide a rich description of the user's behavior. Deviation can be assessed based on the amount of variance that each user exhibits across multiple attributes, compared against their peers. We have performed experimentation using ten synthetic data-driven scenarios and found that the system can identify anomalous behavior that may be indicative of a potential threat. We also show how our detection system can be combined with visual analytics tools to support further investigation by an analyst.