Password Based a Generalize Robust Security System Design Using Neural Network

Among the various means of available resource protection including biometrics, password based system is most simple, user friendly, cost effective and commonly used. But this method having high sensitivity with attacks. Most of the advanced methods for authentication based on password encrypt the contents of password before storing or transmitting in physical domain. But all conventional cryptographic based encryption methods are having its own limitations, generally either in terms of complexity or in terms of efficiency. Multi-application usability of password today forcing users to have a proper memory aids. Which itself degrades the level of security. In this paper a method to exploit the artificial neural network to develop the more secure means of authentication, which is more efficient in providing the authentication, at the same time simple in design, has given. Apart from protection, a step toward perfect security has taken by adding the feature of intruder detection along with the protection system. This is possible by analysis of several logical parameters associated with the user activities. A new method of designing the security system centrally based on neural network with intrusion detection capability to handles the challenges available with present solutions, for any kind of resource has presented

Security Strategies to Prevent Data Breaches in Infrastructure as a Service Cloud Computing

Due to the ever-growing threat of security breaches that information technology (IT) organizations continually face, protecting customer information stored within the cloud is critical to ensuring data integrity. Research shows that new categories of data breaches constantly emerge; thus, security strategies that build trust in consumers and improve system performance are a must. The purpose of this qualitative multiple case study was to explore and analyze the strategies used by database administrators (DBAs) to secure data in a private infrastructure as a service (IaaS) cloud computing. The participants comprised of 6 DBAs from 2 IT companies in Baltimore, Maryland, with experience and knowledge of security strategies to secure data in private IaaS cloud computing. The disruptive innovation theory was the conceptual framework for this study. Data were collected using semistructured interviews and a review of 7 organizational documents. A thematic analysis was used to analyze the data. Four key themes emerged: importance of well-defined security measures in cloud computing, measures to address security controls in cloud computing, limitations of existing security controls in cloud computing, and future and potential security measures solutions in cloud computing. The findings may benefit DBAs and IT organizations by providing strategies to prevent future data breaches. Well-defined security strategies may protect an individual’s data, which in turn may promote individual well-being and build strong communities

Testing Cannot Tell Whether Ballot-Marking Devices Alter Election Outcomes

Like all computerized systems, ballot-marking devices (BMDs) can be hacked, misprogrammed, and misconfigured. Several approaches to testing BMDs have been proposed. In _logic and accuracy_ (_L&A_) tests, trusted agents input known test patterns into the BMD and check whether the printout matches. In _parallel_ or _live_ testing, agents use the BMDs on election day, emulating voters. In _passive_ testing, agents monitor the rate at which voters "spoil" ballots and request another opportunity to mark a ballot: an anomalously high rate might result from BMD malfunctions. In practice, none of these methods can protect against outcome-altering problems. L&A testing is ineffective in part because BMDs "know" the time and date of the test and the election. Neither L&A nor parallel testing can probe even a small fraction of the possible voting transactions that could comprise enough votes to change outcomes. Under mild assumptions, to develop a model of voter interactions with BMDs accurate enough to ensure that parallel tests could reliably detect changes to 5% of the votes (which could change margins by 10% or more) would require monitoring the behavior of more than a million voters in each jurisdiction in minute detail---but the median turnout by jurisdiction in the U.S. is under 3000 voters. Given an accurate model of voter behavior, the number of tests required is still larger than the turnout in a typical U.S. jurisdiction. Under optimistic assumptions, passive testing that has a 99% chance of detecting a 1% change to the margin with a 1% false alarm rate is impossible in jurisdictions with fewer than about 1 million voters, even if the "normal" spoiled ballot rate were known exactly and did not vary from election to election and place to place

SoK: Security Evaluation of SBox-Based Block Ciphers

Cryptanalysis of block ciphers is an active and important research area with an extensive volume of literature. For this work, we focus on SBox-based ciphers, as they are widely used and cover a large class of block ciphers. While there have been prior works that have consolidated attacks on block ciphers, they usually focus on describing and listing the attacks. Moreover, the methods for evaluating a cipher\u27s security are often ad hoc, differing from cipher to cipher, as attacks and evaluation techniques are developed along the way. As such, we aim to organise the attack literature, as well as the work on security evaluation. In this work, we present a systematization of cryptanalysis of SBox-based block ciphers focusing on three main areas: (1) Evaluation of block ciphers against standard cryptanalytic attacks; (2) Organisation and relationships between various attacks; (3) Comparison of the evaluation and attacks on existing ciphers

A reputation framework for behavioural history: developing and sharing reputations from behavioural history of network clients

The open architecture of the Internet has enabled its massive growth and success by facilitating easy connectivity between hosts. At the same time, the Internet has also opened itself up to abuse, e.g. arising out of unsolicited communication, both intentional and unintentional. It remains an open question as to how best servers should protect themselves from malicious clients whilst offering good service to innocent clients. There has been research on behavioural profiling and reputation of clients, mostly at the network level and also for email as an application, to detect malicious clients. However, this area continues to pose open research challenges. This thesis is motivated by the need for a generalised framework capable of aiding efficient detection of malicious clients while being able to reward clients with behaviour profiles conforming to the acceptable use and other relevant policies. The main contribution of this thesis is a novel, generalised, context-aware, policy independent, privacy preserving framework for developing and sharing client reputation based on behavioural history. The framework, augmenting existing protocols, allows fitting in of policies at various stages, thus keeping itself open and flexible to implementation. Locally recorded behavioural history of clients with known identities are translated to client reputations, which are then shared globally. The reputations enable privacy for clients by not exposing the details of their behaviour during interactions with the servers. The local and globally shared reputations facilitate servers in selecting service levels, including restricting access to malicious clients. We present results and analyses of simulations, with synthetic data and some proposed example policies, of client-server interactions and of attacks on our model. Suggestions presented for possible future extensions are drawn from our experiences with simulation

Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack

Today’s popularity of the internet has since proven an effective and efficient means of information sharing. However, this has consequently advanced the proliferation of adversaries who aim at unauthorized access to information being shared over the internet medium. These are achieved via various means one of which is the distributed denial of service attacks-which has become a major threat to the electronic society. These are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ the deep learning approach to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic