Ali Syed

Threat analysis of portable hack tools from USB storage devices and protection solutions

Information security risks associated with Universal Serial Bus (USB) devices have been a serious issue in corporate networks after the wide adoption of USB technologies in the computing industry in 2005. Recently, the U3 USB drives have been of great interest for attackers who want to utilize USB drives as their mobile hack tools. However, beside U3 technology, attackers also have another more flexible alternative, portable application or application virtualization, which allows a wide range of hack tools to be compiled into portable format and run from USB storage devices without requiring any USB specific platform such as U3. In this paper, we provide an investigation into hack tools on U3 platform and USB platform free portable hack tools, their working mechanism, and the compilation techniques. We also provide a general description of most dangerous hack tools with their payloads which can be compiled into portable format. Finally, our proposed solution is aimed at providing the most important and concise solutions for enterprise administrators to secure their systems from portable hack tools.

A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA

In the last five years, crime and accidents rates have increased in many cities of America. The advancement of new technologies can also lead to criminal misuse. In order to reduce incidents, there is a need to understand and examine emerging patterns of criminal activities. This paper analyzed crime and accident datasets from Denver City, USA during 2011 to 2015 consisting of 372,392 instances of crime. The dataset is analyzed by using a number of Classification Algorithms. The aim of this study is to highlight trends of incidents that will in return help security agencies and police department to discover precautionary measures from prediction rates. The classification of algorithms used in this study is to assess trends and patterns that are assessed by BayesNet, NaiveBayes, J48, JRip, OneR and Decision Table. The output that has been used in this study, are correct classification, incorrect classification, True Positive Rate (TP), False Positive Rate (FP), Precision (P), Recall (R) and F-measure (F). These outputs are captured by using two different test methods: k-fold cross-validation and percentage split. Outputs are then compared to understand the classifier performances. Our analysis illustrates that JRip has classified the highest number of correct classifications by 73.71% followed by decision table with 73.66% of correct predictions, whereas OneR produced the least number of correct predictions with 64.95%. NaiveBayes took the least time of 0.57 sec to build the model and perform classification when compared to all the classifiers. The classifier stands out producing better results among all the classification methods. This study would be helpful for security agencies and police department to discover data patterns and analyze trending criminal activity from prediction rates.

Pentaho and Jaspersoft: A Comparative Study of Business Intelligence Open Source Tools Processing Big Data to Evaluate Performances

Regardless of the recent growth in the use of “Big Data” and “Business Intelligence” (BI) tools, little research has been undertaken about the implications involved. Analytical tools affect the development and sustainability of a company, as evaluating clientele needs to advance in the competitive market is critical. With the advancement of the population, processing large amounts of data has become too cumbersome for companies. At some stage in a company’s lifecycle, all companies need to create new and better data processing systems that improve their decision-making processes. Companies use BI Results to collect data that is drawn from interpretations grouped from cues in the data set BI information system that helps organisations with activities that give them the advantage in a competitive market. However, many organizations establish such systems, without conducting a preliminary analysis of the needs and wants of a company, or without determining the benefits and targets that they aim to achieve with the implementation. They rarely measure the large costs associated with the implementation blowout of such applications, which results in these impulsive solutions that are unfinished or too complex and unfeasible, in other words unsustainable even if implemented. BI open source tools are specific tools that solve this issue for organizations in need, with data storage and management. This paper compares two of the best positioned BI open source tools in the market: Pentaho and Jaspersoft, processing big data through six different sized databases, especially focussing on their Extract Transform and Load (ETL) and Reporting processes by measuring their performances using Computer Algebra Systems (CAS). The ETL experimental analysis results clearly show that Jaspersoft BI has an increment of CPU time in the process of data over Pentaho BI, which is represented by an average of 42.28% in performance metrics over the six databases. Meanwhile, Pentaho BI had a marked increment of the CPU time in the process of data over Jaspersoft evidenced by the reporting analysis outcomes with an average of 43.12% over six databases that prove the point of this study. This study is a guiding reference for many researchers and those IT professionals who support the conveniences of Big Data processing, and the implementation of BI open source tool based on their needs.

Review: An evaluation of major threats in cloud computing associated with big data

In todays corporate society, the productivity, stability, and management of an organization relies upon the power of databases. Most organizations outsource their databases in the form of big data and then transfer it into cloud. Although cloud computing technology brings many benefits for an organization, their security risk factor still remains as a big barrier for its wide-spread adoption. Therefore, this problem poses a critical question such as: Is information secure in cloud? Due to this uncertainty, the primary aim of this study is to describe and identify most vulnerable aspects of security threats in cloud environment through content analysis and highlight and evaluate gaps in the literature to draw scholarly attention. This paper analyzed content to source data that helps to identify gaps in the literature. These gaps then have been identified and evaluated to answer questions with possible solutions. This research will help both vendors and users about security issues that have been heightened with recent population advancements and demands that have been pointed out for improvement. This study has reviewed literature in the field over the sp an of six years, and endeavors to seek answers for the question and cast solutions through thorough evaluation and analysis: the security related issues in cloud computing associated with big data must be taking into account by security practitioners when assessing the needs of service providers. This study has found that cloud environment is an innovation, and the blend of parallel computing and cloud computing can offer various advantages. It is ideal for different kinds of applications that can suit different needs if expenses of application modifications consolidate the cost of setup and maintenance of cloud computing. This study has analyzed content and has also found that management solution of only one big secure data after integrating it with cloud needs yet to be designed.

Web Page Classification Using Distributed Learning Automata and Partitioning Graph Algorithm

The characteristic of dynamic websites is that they include hidden contents, and this huge repository is only accessible via the website interfaces. This is a vital capability of all search engines, thus providing the users with links that are more relevant and ranked according to their needs. The drawback of most search engine algorithms is that they rank pages based on hyperlinked relative importance to other pages, rather than user intent and interest. This paper proposes a method based on Learning Automata for the classification of the webpage searches.

Review: Security and Privacy Issues of Fog Computing for the Internet of Things (IoT)

. Internet of Things (IoT), devices, and remote data centers need to connect. The purpose of fog is to reduce the amount of data transported for processing, analysis, and storage, to speed-up the computing processes. The gap between, Fog computing technologies and devices need to narrow down as growth in business today relies on the ability to connect to digital channels for processing large amounts of data. Cloud computing is unfeasible for many internet of things applications, therefore fog computing is often seen as a viable alternative. Fog is suitable for many IoT services as it has enabled an extensive collection of benefits, such as decreased bandwidth, reduced latency, and enhanced security. However, Fog devices that are placed at the edge of the internet have met numerous privacy and security threats. This study aims to examine and highlight the security and privacy issues of fog computing through a comprehensive review of recently published literature of fog computing and suggest solutions for identified problems. Data extracted from 34 peer-reviewed scientific publications (2011–2017) were studied, leading to the identification of 49 different issues that were raised, in relation to fog computing. This study revealed a general agreement among researchers about the novelty of Fog computing, and its early stages of development, and identifies several challenges that need to be met, before its wider application and use reaches its full potential.

Big-data NoSQL databases: A comparison and analysis of “Big-Table”, “DynamoDB”, and “Cassandra”

The growth and enhancement of technology in the corporate society has led to data storage and confidentiality issues. The problem arises from the management of trillions of data, generated every second in corporations, precisely known as “Big Data”. Big Data needs to be stored and managed by larger companies that do not have the right storage systems, as there is not any developed yet. The aim of this paper is to find a solution to this growing problem by analyzing gaps in the literature, and to evaluate possible solutions. This study has analyzed content from top reviewed scientific publications, to gather compared and contrasted data from articles and highlight gaps. The highlighted literature will address this problems, and find solutions by contrasting BigData management approaches of NoSQL databases; BigTable, DynamoDB, and Cassandra. The findings summarized from publications are highlighted and the main features of all three databases and their applications are displayed. The system performances are analyzed based on their consistency, availability and partition intolerance. The study concluded that Googles BigTable and Amazons DynamoDB are also critical and efficient on their own, and also found that the combination of both systems had caused the development of Cassandra. Cassandra is now the primary focus of numerous companies to develop different applications. Furthermore, all three systems are NoSQL storage systems, and BigTable, and based on one master node approach, unlike Dynamo, and Cassandra, it follows a Peer-to-Peer system. BigTable however, with some additional features from DynamoDB has helped the development of Cassandra, which is the basis of various modern applications available both open source and socially.

Computer virus and protection methods using lab analysis

The aim of this paper is to explore the hypothesis of a computer virus threat, and how destructive it can be if executed on a targeted machine. What are the possible counter measures to protect computers from these threats? In this study, we performed an analysis from the data extracted from different test of scenarios and labs conducted in a test environment. Information security risks associated with computer viruses can infect computers and other storage devices by copying themselves into a file and other executable programs. These file get infection and allow attackers to connect to target systems by using backdoors. The results of this study show that, the proper security implementations and the use of up to date operating systems patches and anti-virus programs helps users to prevent the loss of data and any viral attack on the system. Nevertheless, this observation could be used for further research in the network security and related fields; this study will also help computer users to use the possible steps and techniques to protect their systems and information from any possible attacks on their network systems.