Network forensics: detection and mitigation of botnet malicious code via darknet

Computer malwares are major threats that always find a way to penetrate the network, posing threats to the confidentiality, integrity and the availability of data. Network-borne malwares penetrate networks by exploiting vulnerabilities in networks and systems. IT administrators in campus wide network continue to look for security control solutions to reduce exposure and magnitude of potential threats. However, with multi-user computers and distributed systems, the campus wide network often becomes a breeding ground for botnets

CRF based feature extraction applied for supervised automatic text summarization

Feature extraction is the promising issue to be addressed in algebraic based Automatic Text Summarization (ATS) methods. The most vital role of any ATS is the identification of most important sentences from the given text. This is possible only when the correct features of the sentences are identified properly. Hence this paper proposes a Conditional Random Field (CRF) based ATS which can identify and extract the correct features which is the main issue that exists with the Non-negative Matrix Factorization (NMF) based ATS. This work proposes a trainable supervised method. Result clearly indicates that the newly proposed approach can identify and segment the sentences based on features more accurately than the existing method addressed

Software defined networking (SDN) and its security issues

The demand of network infrastructure and services is ever increasing. The network architecture and related technology must be flexible enough to accommodate the ever-growing number of users. Software-Defined Networking (SDN) is an approach of networking architecture that improvise conventional network in terms of scalability, security, and availability. At the same time, SDN is vulnerable to security threats as well. This paper studies on SDN architecture, the improvement of SDN from conventional network, the vulnerability and threats in SDN, and possible solutions to some security threats examples. It gives an overview of SDN and security – the architecture advantages that can be leveraged to secure network systems, and the security threats that may occur if improper design and deployment of SDN take place

Multilingual Online Resources for Minority Languages of a Campus Community

AbstractThis paper discusses on an initiative of developing a repository on multilingual language resources for minority languages of a campus community. The choice of language is based on a survey amongst IIUM international students about the status of their mother language's resources and usages in the digital world. As a starting point, multilingual dictionaries of textual and speech for these identified languages are developed. This initiative is an effort to ensure that such minority languages will be protected from being endangered in this era of globalization

Risk identification for an information security management system implementation

ISO/IEC 27001 is an international standard that provides a set of requirements for an Information Security Management System (ISMS) implementation. A risk assessment exercise for an ISMS implementation requires human expertise with comprehensive understanding and considerable knowledge in information security. A common risk assessment exercise is based on three sub-processes, namely, risk identification, risk analysis and risk evaluation. The lack of tools especially in the automation of risk identification emphasized the need of experienced personnel and this becomes a challenge for organizations seeking compliance with the ISMS standard. This paper proposes a relationship concept in asset and threat identification which is part of the risk identification sub-process. The concept provides a foundation to automate the risk assessment process for an identified scope of an ISMS implementation

Study on sparseness effects over NMF applied for automatic text summarization

The significance of Non-negative Matrix Factorization ((NMF) in the field of automatic text summarization is rapidly increasing due to its interpretation and storage capabilities. Interpretation defines the ease at which the structure of high dimensional data can be understood. While, storage capability relates to the extent of data reduction process achieved by NMF. The parametric values that serve as input to the NMF process include initialization method, rank of factorization, sparseness measure and maximum iteration. These inputs are vital to the output produced by NMF. These parameters of NMF were not been considered in the existing literature on Automatic Text Summarization (ATS). This paper sheds light specifically on sparseness of NMF when applied to ATS and the impact it makes to the quality of the summary generated. In this study on NMF algorithm that supports sparseness is treated with various degree of sparsity and its performance impact on text summary generated is compared with other NMF algorithms without sparseness constraints

Behavioral analysis and visualization of Fast-Flux DNS

Today, a growing, sophisticated technique called Fast-Flux Service Networks (FFSN) poses a major problem to Internet security. They are increasingly used in many illegal practices including money mule recruitment sites, distribution of malware downloads, illegal adult content, and other forms of Internet fraud. Essentially, FFSN were first used as a Domain Name Server (DNS) switching mechanism that combine distributed command and control, web-based load balancing, and proxy redirection. However, cyber criminals are applying various techniques to subvert detection, retain uptime of their information infrastructure and maximize their financial gain. Hence, this paper proposed to analyze and visualize the behavior of FFSN in order to facilitate FFSN detection. In this study, we collect, classify and monitor over500 domains and by scrutinizing and visualizing the trained data, we discover the new types of fluxing designated as NSName-Flux(NF). The analysis results of NF exposed that FFSN have become extensively sophisticated and dynamic. This exemplifies that visualization is an alternative and effective data exploration method for understanding the complex behaviors of FFSN

Inculcating secure coding for beginners

This paper describes an implementation of a Secure Coding learning package for Undergraduate students in the Kulliyyah of Information and Communication Technology (KICT) at the International Islamic University Malaysia. The learning package consists of three components which are SCALT, WebGoat and notes on several vulnerabilities in programming languages. This work aims to create awareness among the KICT community on the importance of secure coding in any application development. This package teaches individuals on how hackers take advantage on vulnerabilities that exist on web applications and allows students to experience within the WebGoat environment. Tips on handling vulnerabilities when coding in C, C++ and Java are also provided for students’ reference. This learning package is an effort to inculcate students at an early age of software developers, to produce secure applications

Two suggested probabilistic and kinetic models for astrocytic network in spiking neural networks

Astrocytes, the predominant glial cell type in the brain, were traditionallyconsidered as merely passive supportive cells without any important roles in synaptic information processing. In contrast, the contemporary view was given rise to show that astrocytes play active roles in synaptic neurotransmissionand information processing. Hence, recently two terms have been emerged, tripartite synapse, to describe the communication between an astrocyte and two neurons, and the term astrocytic syncytium or astrocytic network to describe the communication among the astrocytesby gap junction. Therefore, we propose mathematical models for tripartite synapseand astrocytic syncytium basedontwo-state kinetics models, several probabilistic methods and Spiking Neural Network (SNN) to introducenew Artificial Astrocytic Syncytium (AAS) model. The simulation results have shown that proposed model could represent the cellular intrinsic properties of astrocyte based on the spatial and temporal aspects to emulate the astrocytic network functions related to cognitive, learning and memory