Biblio

Now a day's cloud technology is a new example of computing that pays attention to more computer user, government agencies and business. Cloud technology brought more advantages particularly in every-present services where everyone can have a right to access cloud computing services by internet. With use of cloud computing, there is no requirement for physical servers or hardware that will help the computer system of company, networks and internet services. One of center services offered by cloud technology is storing the data in remote storage space. In the last few years, storage of data has been realized as important problems in information technology. In cloud computing data storage technology, there are some set of significant policy issues that includes privacy issues, anonymity, security, government surveillance, telecommunication capacity, liability, reliability and among others. Although cloud technology provides a lot of benefits, security is the significant issues between customer and cloud. Normally cloud computing technology has more customers like as academia, enterprises, and normal users who have various incentives to go to cloud. If the clients of cloud are academia, security result on computing performance and for this types of clients cloud provider's needs to discover a method to combine performance and security. In this research paper the more significant issue is security but with diverse vision. High performance might be not as dangerous for them as academia. In our paper, we design an efficient secure and verifiable outsourcing protocol for outsourcing data. We develop extended QP problem protocol for storing and outsourcing a data securely. To achieve the data security correctness, we validate the result returned through the cloud by Karush\_Kuhn\_Tucker conditions that are sufficient and necessary for the most favorable solution.

Securing Internet of Things (IoT) systems is a challenge because of its multiple points of vulnerability. A spate of recent hacks and security breaches has unveiled glaring vulnerabilities in the IoT. Due to the computational and memory requirement constraints associated with anomaly detection algorithms in core networks, commercial in-line (part of the direct line of communication) Anomaly Detection Systems (ADSs) rely on sampling-based anomaly detection approaches to achieve line rates and truly-inline anomaly detection accuracy in real-time. However, packet sampling is inherently a lossy process which might provide an incomplete and biased approximation of the underlying traffic patterns. Moreover, commercial routers uses proprietary software making them closed to be manipulated from the outside. As a result, detecting malicious packets on the given network path is one of the most challenging problems in the field of network security. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and mitigate DDoS attacks. Unlike sampling-based approaches for anomaly detection and limitation of proprietary software at routers, we use the SDN infrastructure to relax the sampling-based ADS constraints and collect traffic flow statistics which are maintained at each SDN-enabled switch to achieve high detection accuracy. In order to implement our idea, we discuss how to mitigate DDoS attacks using the features of SDN infrastructure.

Smart spammers and telemarketers circumvent the standalone spam detection systems by making low rate spam-ming activity to a large number of recipients distributed across many telecommunication operators. The collaboration among multiple telecommunication operators (OPs) will allow operators to get rid of unwanted callers at the early stage of their spamming activity. The challenge in the design of collaborative spam detection system is that OPs are not willing to share certain information about behaviour of their users/customers because of privacy concerns. Ideally, operators agree to share certain aggregated statistical information if collaboration process ensures complete privacy protection of users and their network data. To address this challenge and convince OPs for the collaboration, this paper proposes a decentralized reputation aggregation protocol that enables OPs to take part in a collaboration process without use of a trusted third party centralized system and without developing a predefined trust relationship with other OPs. To this extent, the collaboration among operators is achieved through the exchange of cryptographic reputation scores among OPs thus fully protects relationship network and reputation scores of users even in the presence of colluders. We evaluate the performance of proposed protocol over the simulated data consisting of five collaborators. Experimental results revealed that proposed approach outperforms standalone systems in terms of true positive rate and false positive rate.

We present a novel approach to proving the absence of timing channels. The idea is to partition the program’s execution traces in such a way that each partition component is checked for timing attack resilience by a time complexity analysis and that per-component resilience implies the resilience of the whole program. We construct a partition by splitting the program traces at secret-independent branches. This ensures that any pair of traces with the same public input has a component containing both traces. Crucially, the per-component checks can be normal safety properties expressed in terms of a single execution. Our approach is thus in contrast to prior approaches, such as self-composition, that aim to reason about multiple (k≥ 2) executions at once. We formalize the above as an approach called quotient partitioning, generalized to any k-safety property, and prove it to be sound. A key feature of our approach is a demand-driven partitioning strategy that uses a regex-like notion called trails to identify sets of execution traces, particularly those influenced by tainted (or secret) data. We have applied our technique in a prototype implementation tool called Blazer, based on WALA, PPL, and the brics automaton library. We have proved timing-channel freedom of (or synthesized an attack specification for) 24 programs written in Java bytecode, including 6 classic examples from the literature and 6 examples extracted from the DARPA STAC challenge problems.

In this paper a method of monostatic RCS measuring in real conditions for complex shaped objects is proposed. The basic idea of the method is to provide measuring in near field zone for different parts of the object (fragments) separately. This technique is titled "decomposition method". After such measurements all RCS data are summed and one can obtain the average RCS of investigated object. Such method is much more accessible in comparison with natural measurements in far field zone. In this paper the decomposition method is tested numerically. For this a model of complex shape object (tank T-90) is divided into the fragments for some direction of view. It is shown that the sum of RCS of the fragments is close to the full object RCS for corresponding direction.

Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).

Nowadays, Internet Service Providers (ISPs) have been depending on Deep Packet Inspection (DPI) approaches, which are the most precise techniques for traffic identification and classification. However, constructing high performance DPI approaches imposes a vigilant and an in-depth computing system design because the demands for the memory and processing power. Membership query data structures, specifically Bloom filter (BF), have been employed as a matching check tool in DPI approaches. It has been utilized to store signatures fingerprint in order to examine the presence of these signatures in the incoming network flow. The main issue that arise when employing Bloom filter in DPI approaches is the need to use k hash functions which, in turn, imposes more calculations overhead that degrade the performance. Consequently, in this paper, a new design and implementation for a DPI approach have been proposed. This DPI utilizes a membership query data structure called Cuckoo filter (CF) as a matching check tool. CF has many advantages over BF like: less memory consumption, less false positive rate, higher insert performance, higher lookup throughput, support delete operation. The achieved experiments show that the proposed approach offers better performance results than others that utilize Bloom filter.

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.

The deployment of Software Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies is increasing, with security as a recognized application driving adoption. However, despite the potential with SDN/NFV for automated and adaptive network security services, the controller interaction presents both a performance and scalability challenge, and a threat vector. To overcome the performance issue, stateful data-plane designs have been proposed. However, these solutions do not offer protection from SDN-specific attacks linked to necessary control functions such as link reconfiguration and switch identification. In this work, we leverage the OpenState framework to introduce state-based SDN security protection mechanisms. The extensions required for this design are presented with respect to an SDN configuration-based attack. The demonstration shows the ability of the SDN Configuration (CFG) security protection mechanism to support legitimate relocation requests and to protect against malicious connection attempts.

The paper introduces a smart system developed with sensors that is useful for internal and external security. The system is useful for people living in houses, apartments, high officials, bank, and offices. The system is developed in two phases one for internal security like home another is external security like open areas, streets. The system is consist of a mobile application, capacitive sensing, smart routing these valuable features to ensure safety of life and wealth. This security system is wireless sensor based which is an effective alternative of cctv cameras and other available security systems. Efficiency of this system is developed after going through practical studies and prototyping. The end result explains the feasibility rate, positive impact factor, reliability of the system. More research is possible in future based on this system this research explains that.

Robotic vehicles and especially autonomous robotic vehicles can be attractive targets for attacks that cross the cyber-physical divide, that is cyber attacks or sensory channel attacks affecting the ability to navigate or complete a mission. Detection of such threats is typically limited to knowledge-based and vehicle-specific methods, which are applicable to only specific known attacks, or methods that require computation power that is prohibitive for resource-constrained vehicles. Here, we present a method based on Bayesian Networks that can not only tell whether an autonomous vehicle is under attack, but also whether the attack has originated from the cyber or the physical domain. We demonstrate the feasibility of the approach on an autonomous robotic vehicle built in accordance with the Generic Vehicle Architecture specification and equipped with a variety of popular communication and sensing technologies. The results of experiments involving command injection, rogue node and magnetic interference attacks show that the approach is promising.

Modern botnets can persist in networked systems for extended periods of time by operating in a stealthy manner. Despite the progress made in the area of botnet prevention, detection, and mitigation, stealthy botnets continue to pose a significant risk to enterprises. Furthermore, existing enterprise-scale solutions require significant resources to operate effectively, thus they are not practical. In order to address this important problem in a resource-constrained environment, we propose a reinforcement learning based approach to optimally and dynamically deploy a limited number of defensive mechanisms, namely honeypots and network-based detectors, within the target network. The ultimate goal of the proposed approach is to reduce the lifetime of stealthy botnets by maximizing the number of bots identified and taken down through a sequential decision-making process. We provide a proof-of-concept of the proposed approach, and study its performance in a simulated environment. The results show that the proposed approach is promising in protecting against stealthy botnets.

Due to the transition from analog to digital format, it possible to use IP-protocol for video surveillance systems. In addition, wireless access, color systems with higher resolution, biometrics, intelligent sensors, software for performing video analytics are becoming increasingly widespread. The paper considers only the calculation of the error probability (BER — Bit Error Rate) depending on the realized value of S/N.

Decoy Routing, the use of routers (rather than end hosts) as proxies, is a new direction in anti-censorship research. Decoy Routers (DRs), placed in Autonomous Systems, proxy traffic from users; so the adversary, e.g. a censorious government, attempts to avoid them. It is quite difficult to place DRs so the adversary cannot route around them – for example, we need the cooperation of 850 ASes to contain China alone [1]. In this paper, we consider a different approach. We begin by noting that DRs need not intercept all the network paths from a country, just those leading to Overt Destinations, i.e. unfiltered websites hosted outside the country (usually popular ones, so that client traffic to the OD does not make the censor suspicious). Our first question is – How many ASes are required for installing DRs to intercept a large fraction of paths from e.g. China to the top-n websites (as per Alexa)? How does this number grow with n ? To our surprise, the same few ($\approx$ 30) ASes intercept over 90% of paths to the top n sites worldwide, for n = 10, 20...200 and also to other destinations. Investigating further, we find that this result fits perfectly with the hierarchical model of the Internet [2]; our first contribution is to demonstrate with real paths that the number of ASes required for a world-wide DR framework is small ($\approx$ 30). Further, censor nations' attempts to filter traffic along the paths transiting these 30 ASes will not only block their own citizens, but others residing in foreign ASes. Our second contribution in this paper is to consider the details of DR placement: not just in which ASes DRs should be placed to intercept traffic, but exactly where in each AS. We find that even with our small number of ASes, we still need a total of about 11, 700 DRs. We conclude that, even though a DR system involves far fewer ASes than previously thought, it is still a major undertaking. For example, the current routers cost over 10.3 billion USD, so if Decoy Routing at line speed requires all-new hardware, the cost alone would make such a project unfeasible for most actors (but not for major nation states).

In machine learning, feature engineering has been a pivotal stage in building a high-quality predictor. Particularly, this work explores the multiple Kernel Discriminant Component Analysis (mKDCA) feature-map and its variants. However, seeking the right subset of kernels for mKDCA feature-map can be challenging. Therefore, we consider the problem of kernel selection, and propose an algorithm based on Differential Mutual Information (DMI) and incremental forward search. DMI serves as an effective metric for selecting kernels, as is theoretically supported by mutual information and Fisher's discriminant analysis. On the other hand, incremental forward search plays a role in removing redundancy among kernels. Finally, we illustrate the potential of the method via an application in privacy-aware classification, and show on three mobile-sensing datasets that selecting an effective set of kernels for mKDCA feature-maps can enhance the utility classification performance, while successfully preserve the data privacy. Specifically, the results show that the proposed DMI forward search method can perform better than the state-of-the-art, and, with much smaller computational cost, can perform as well as the optimal, yet computationally expensive, exhaustive search.

Recently, the increase of interconnectivity has led to a rising amount of IoT enabled devices in botnets. Such botnets are currently used for large scale DDoS attacks. To keep track with these malicious activities, Honeypots have proven to be a vital tool. We developed and set up a distributed and highly-scalable WAN Honeypot with an attached backend infrastructure for sophisticated processing of the gathered data. For the processed data to be understandable we designed a graphical frontend that displays all relevant information that has been obtained from the data. We group attacks originating in a short period of time in one source as sessions. This enriches the data and enables a more in-depth analysis. We produced common statistics like usernames, passwords, username/password combinations, password lengths, originating country and more. From the information gathered, we were able to identify common dictionaries used for brute-force login attacks and other more sophisticated statistics like login attempts per session and attack efficiency.

This paper proposes a new DNA cryptographic technique based on dynamic DNA encoding and asymmetric cryptosystem to increase the level of secrecy of data. The key idea is: to split the plaintext into fixed sized chunks, to encrypt each chunk using asymmetric cryptosystem and finally to merge the ciphertext of each chunk using dynamic DNA encoding. To generate chunks, characters of the plaintext are transformed into their equivalent ASCII values and split it into finite values. Now to encrypt each chunk, asymmetric cryptosystem is applied and the ciphertext is transformed into its equivalent binary value. Then this binary value is converted into DNA bases. Finally to merge each chunk, sufficient random strings are generated. Here to settle the required number of random strings, dynamic DNA encoding is exploited which is generated using Fibonacci series. Thus the use of finite chunks, asymmetric cryptosystem, random strings and dynamic DNA encoding increases the level of security of data. To evaluate the encryption-decryption time requirement, an empirical analysis is performed employing RSA, ElGamal and Paillier cryptosystems. The proposed technique is suitable for any use of cryptography.

To evaluate the effectiveness of domain highlighting in helping users identify whether Web pages are legitimate or spurious. As a component of the URL, a domain name can be overlooked. Consequently, browsers highlight the domain name to help users identify which Web site they are visiting. Nevertheless, few studies have assessed the effectiveness of domain highlighting, and the only formal study confounded highlighting with instructions to look at the address bar. We conducted two phishing detection experiments. Experiment 1 was run online: Participants judged the legitimacy of Web pages in two phases. In Phase 1, participants were to judge the legitimacy based on any information on the Web page, whereas in Phase 2, they were to focus on the address bar. Whether the domain was highlighted was also varied. Experiment 2 was conducted similarly but with participants in a laboratory setting, which allowed tracking of fixations. Participants differentiated the legitimate and fraudulent Web pages better than chance. There was some benefit of attending to the address bar, but domain highlighting did not provide effective protection against phishing attacks. Analysis of eye-gaze fixation measures was in agreement with the task performance, but heat-map results revealed that participants’ visual attention was attracted by the highlighted domains. Failure to detect many fraudulent Web pages even when the domain was highlighted implies that users lacked knowledge of Web page security cues or how to use those cues. Potential applications include development of phishing prevention training incorporating domain highlighting with other methods to help users identify phishing Web pages.

Objective: To evaluate the effectiveness of domain highlighting in helping users identify whether webpages are legitimate or spurious.

Background: As a component of the URL, a domain name can be overlooked. Consequently, browsers highlight the domain name to help users identify which website they are visiting. Nevertheless, few studies have assessed the effectiveness of domain highlighting, and the only formal study confounded highlighting with instructions to look at the address bar. 

Method: Two phishing detection experiments were conducted. Experiment 1 was run online: Participants judged the legitimacy of webpages in two phases. In phase one, participants were to judge the legitimacy based on any information on the webpage, whereas phase two they were to focus on the address bar. Whether the domain was highlighted was also varied.  Experiment 2 was conducted similarly but with participants in a laboratory setting, which allowed tracking of fixations.

Results: Participants differentiated the legitimate and fraudulent webpages better than chance. There was some benefit of attending to the address bar, but domain highlighting did not provide effective protection against phishing attacks. Analysis of eye-gaze fixation measures was in agreement with the task performance, but heat-map results revealed that participants’ visual attention was attracted by the domain highlighting.

Conclusion: Failure to detect many fraudulent webpages even when the domain was highlighted implies that users lacked knowledge of webpage security cues or how to use those cues.

Application: Potential applications include development of phishing-prevention training incorporating domain highlighting with other methods to help users identify phishing webpages. 

Objective: To evaluate the effectiveness of domain highlighting in helping users identify whether webpages are legitimate or spurious.

Background: As a component of the URL, a domain name can be overlooked. Consequently, browsers highlight the domain name to help users identify which website they are visiting. Nevertheless, few studies have assessed the effectiveness of domain highlighting, and the only formal study confounded highlighting with instructions to look at the address bar. 

Method: We conducted two phishing detection experiments. Experiment 1 was run online: Participants judged the legitimacy of webpages in two phases. In phase one, participants were to judge the legitimacy based on any information on the webpage, whereas phase two they were to focus on the address bar. Whether the domain was highlighted was also varied.  Experiment 2 was conducted similarly but with participants in a laboratory setting, which allowed tracking of fixations.

Results: Participants differentiated the legitimate and fraudulent webpages better than chance. There was some benefit of attending to the address bar, but domain highlighting did not provide effective protection against phishing attacks. Analysis of eye-gaze fixation measures was in agreement with the task performance, but heat-map results revealed that participants’ visual attention was attracted by the highlighted domains.

Conclusion: Failure to detect many fraudulent webpages even when the domain was highlighted implies that users lacked knowledge of webpage security cues or how to use those cues.

Objective: To evaluate the effectiveness of domain highlighting in helping users identify whether webpages are legitimate or spurious.

Background: As a component of the URL, a domain name can be overlooked. Consequently, browsers highlight the domain name to help users identify which website they are visiting. Nevertheless, few studies have assessed the effectiveness of domain highlighting, and the only formal study confounded highlighting with instructions to look at the address bar. 

Method: We conducted two phishing detection experiments. Experiment 1 was run online: Participants judged the legitimacy of webpages in two phases. In phase one, participants were to judge the legitimacy based on any information on the webpage, whereas phase two they were to focus on the address bar. Whether the domain was highlighted was also varied.  Experiment 2 was conducted similarly but with participants in a laboratory setting, which allowed tracking of fixations.

Results: Participants differentiated the legitimate and fraudulent webpages better than chance. There was some benefit of attending to the address bar, but domain highlighting did not provide effective protection against phishing attacks. Analysis of eye-gaze fixation measures was in agreement with the task performance, but heat-map results revealed that participants’ visual attention was attracted by the highlighted domains.

Conclusion: Failure to detect many fraudulent webpages even when the domain was highlighted implies that users lacked knowledge of webpage security cues or how to use those cues.