GSM SECURITY AND ENCRYPTION

the security mechanisms of GSM are implemented in three different system elements; the Subscriber Identity Module (SIM), the GSM handset or MS, and the GSM network. The SIM contains the IMSI, the individual subscriber authentication key (Ki), the ciphering key generating algorithm (A8), the authentication algorithm (A3), aswell as a Personal Identification Number (PIN). The GSM handset contains the ciphering algorithm (A5). The encryption algorithms (A3, A5, and A8) are present in the GSM network as well. The Authentication Center (AUC), part of the Operation and Maintenance Subsystem (OMS) of the GSM network, consists of a database of identification and authentication information for subscribers. This information consists of the IMSI, the TMSI, the Location Area Identity (LAI), and the individual subscriber authentication key (Ki) for each user. In order for the authentication and security mechanisms to function, all three elements (SIM, handset, and GSM network) are required. This distribution of security credentials and encryption algorithms provides an additional measure of security both in ensuring the privacy of cellular telephone conversations and in the prevention of cellular telephone fraud [4]

Social science textbooks: changing mindsets?

Social science textbooks created within the NCF- 2005 attempt to tackle a whole range of human prejudices, hoping to bring about a sea change in mindsets. They are designed to inculcate respect for diversity, democratic values, critical thinking and questioning. As is widely acknowledged, these textbooks are framed within a progressive educational, social and political understanding (Ritubala and Joshi, 2008-09: 29-42). The matter in these textbooks is engaging, varied and playful: the wall between the world and the school has been broken and the child can bring her rich experience of the world into the classroom (Rai, 2006: 152-57). But we must remember at the same time that the textbook is no magic wand, nor really a vehicle for transformative education. In fact there are many hazards on the textbook’s journey, as it moves through the classroom, and the world of the child

Maintaining regularity and generalization in data using the minimum description length principle and genetic algorithm: Case of grammatical inference

In this paper, a genetic algorithm with minimum description length (GAWMDL) is proposed for grammatical inference. The primary challenge of identifying a language of infinite cardinality from a finite set of examples should know when to generalize and specialize the training data. The minimum description length principle that has been incorporated addresses this issue is discussed in this paper. Previously, the e-GRIDS learning model was proposed, which enjoyed the merits of the minimum description length principle, but it is limited to positive examples only. The proposed GAWMDL, which incorporates a traditional genetic algorithm and has a powerful global exploration capability that can exploit an optimum offspring. This is an effective approach to handle a problem which has a large search space such the grammatical inference problem. The computational capability, the genetic algorithm poses is not questionable, but it still suffers from premature convergence mainly arising due to lack of population diversity. The proposed GAWMDL incorporates a bit mask oriented data structure that performs the reproduction operations, creating the mask, then Boolean based procedure is applied to create an offspring in a generative manner. The Boolean based procedure is capable of introducing diversity into the population, hence alleviating premature convergence. The proposed GAWMDL is applied in the context free as well as regular languages of varying complexities. The computational experiments show that the GAWMDL finds an optimal or close-to-optimal grammar. Two fold performance analysis have been performed. First, the GAWMDL has been evaluated against the elite mating pool genetic algorithm which was proposed to introduce diversity and to address premature convergence. GAWMDL is also tested against the improved tabular representation algorithm. In addition, the authors evaluate the performance of the GAWMDL against a genetic algorithm not using the minimum description length principle. Statistical tests demonstrate the superiority of the proposed algorithm. Overall, the proposed GAWMDL algorithm greatly improves the performance in three main aspects: maintains regularity of the data, alleviates premature convergence and is capable in grammatical inference from both positive and negative corpora

Maintaining regularity and generalizationin data using the minimum description length principle and genetic algorithm: case of grammatical inference

In this paper, a genetic algorithm with minimum description length (GAWMDL) is proposed for grammatical inference. The primary challenge of identifying a language of infinite cardinality from a finite set of examples should know when to generalize and specialize the training data. The minimum description length principle that has been incorporated addresses this issue is discussed in this paper. Previously, the e-GRIDS learning model was proposed, which enjoyed the merits of the minimum description length principle, but it is limited to positive examples only. The proposed GAWMDL, which incorporates a traditional genetic algorithm and has a powerful global exploration capability that can exploit an optimum offspring. This is an effective approach to handle a problem which has a large search space such the grammatical inference problem. The computational capability, the genetic algorithm poses is not questionable, but it still suffers from premature convergence mainly arising due to lack of population diversity. The proposed GAWMDL incorporates a bit mask oriented data structure that performs the reproduction operations, creating the mask, then Boolean based procedure is applied to create an offspring in a generative manner. The Boolean based procedure is capable of introducing diversity into the population, hence alleviating premature convergence. The proposed GAWMDL is applied in the context free as well as regular languages of varying complexities. The computational experiments show that the GAWMDL finds an optimal or close-to-optimal grammar. Two fold performance analysis have been performed. First, the GAWMDL has been evaluated against the elite mating pool genetic algorithm which was proposed to introduce diversity and to address premature convergence. GAWMDL is also tested against the improved tabular representation algorithm. In addition, the authors evaluate the performance of the GAWMDL against a genetic algorithm not using the minimum description length principle. Statistical tests demonstrate the superiority of the proposed algorithm. Overall, the proposed GAWMDL algorithm greatly improves the performance in three main aspects: maintains regularity of the data, alleviates premature convergence and is capable in grammatical inference from both positive and negative corpora

Proposing hierarchy-similarity based access control framework: A multilevel Electronic Health Record data sharing approach for interoperable environment

Interoperability in healthcare environment deals with sharing of patient’s Electronic Health Records (EHR) with fellow professionals in inter as well as intra departments or organizations. Healthcare environment experiences frequent shifting of doctors, paramedical staff in inter as well as intra departments or hospitals. The system exhibits dynamic attributes of users and resources managed through access control policies defined for that environment. Rules obtained on merging of such policies often generate policy-conflicts thereby resulting in undue data leakages to unintended users. This paper proposes an access control framework that applies a Hierarchy Similarity Analyzer (HSA) on the policies need to be merged. It calculates a Security_Level (SL) and assigns it to the users sharing data. The SL determines the authorized amount of data that can be shared on successful collaboration of two policies. The proposed framework allows integration of independent policies and identifies the possible policy-conflicts arising due to attribute disparities in defined rules. The framework is implemented on XACML policies and compared with other access models designed using centralized and decentralized approaches. Conditional constraints and properties are defined that generate policy-conflicts as prevalent in the policies

Molecular modeling and spectroscopic studies of semustine binding with DNA and its comparison with lomustine-DNA adduct formation

Chloroethyl nitrosoureas constitute an important family of cancer chemotherapeutic agents, used in the treatment of various types of cancer. They exert antitumor activity by inducing DNA interstrand cross-links. Semustine, a chloroethyl nitrosourea, is a 4-methyl derivative of lomustine. There exist some interesting reports dealing with DNA-binding properties of chloroethyl nitrosoureas; however, underlying mechanism of cytotoxicity caused by semustine has not been precisely and completely delineated. The present work focuses on understanding semustine-DNA interaction to comprehend its anti-proliferative action at molecular level using various spectroscopic techniques. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy is used to determine the binding site of semustine on DNA. Conformational transition in DNA after semustine complexation is investigated using circular dichroism (CD) spectroscopy. Stability of semustine-DNA complexes is determined using absorption spectroscopy. Results of the present study demonstrate that semustine performs major-groove-directed DNA alkylation at guanine residues in an incubation-time-drug-concentration-dependent manner. CD spectral outcomes suggest partial transition of DNA from native B-conformation to C-form. Calculated binding constants (K-a) for semustine and lomustine interactions with DNA are 1.53 x 10(3)M(-1) and 8.12 x 10(3)M(-1), respectively. Moreover, molecular modeling simulation is performed to predict preferential binding orientation of semustine with DNA that corroborates well with spectral outcomes. Results based on comparative study of DNA-binding properties of semustine and lomustine, presented here, may establish a correlation between molecular structure and cytotoxicity of chloroethyl nitrosoureas that may be instrumental in the designing and synthesis of new nitrosourea therapeutics possessing better efficacy and fewer side effects