New Anomaly Network Intrusion Detection System in Cloud Environment Based on Optimized Back Propagation Neural Network Using Improved Genetic Algorithm

Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over an open environment (Internet), this lead to different matters related to the security and privacy in cloud computing. Thus, defending network accessible Cloud resources and services from various threats and attacks is of great concern. To address this issue, it is essential to create an efficient and effective Network Intrusion System (NIDS) to detect both outsider and insider intruders with high detection precision in the cloud environment. NIDS has become popular as an important component of the network security infrastructure, which detects malicious activities by monitoring network traffic. In this work, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely, Back Propagation Neural Network (BPNN) using an Improved Genetic Algorithm (IGA). Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Since,  Learning rate and Momentum term are among the most relevant parameters that impact the performance of BPNN classifier, we have employed IGA to find the optimal or near-optimal values of these two parameters which ensure high detection rate, high accuracy and low false alarm rate. The CloudSim simulator 4.0 and DARPA’s KDD cup datasets 1999 are used for simulation. From the detailed performance analysis, it is clear that the proposed system called “ANIDS BPNN-IGA” (Anomaly NIDS based on BPNN and IGA) outperforms several state-of-art methods and it is more suitable for network anomaly detection

Web application penetration testing: an analysis of a corporate application according to OWASP guidelines

During the past decade, web applications have become the most prevalent way for service delivery over the Internet. As they get deeply embedded in business activities and required to support sophisticated functionalities, the design and implementation are becoming more and more complicated. The increasing popularity and complexity make web applications a primary target for hackers on the Internet. According to Internet Live Stats up to February 2019, there is an enormous amount of websites being attacked every day, causing both direct and significant impact on huge amount of people. Even with support from security specialist, they continue having troubles due to the complexity of penetration procedures and the vast amount of testing case in both penetration testing and code reviewing. As a result, the number of hacked websites per day is increasing. The goal of this thesis is to summarize the most common and critical vulnerabilities that can be found in a web application, provide a detailed description of them, how they could be exploited and how a cybersecurity tester can find them through the process of penetration testing. To better understand the concepts exposed, there will be also a description of a case of study: a penetration test performed over a company's web application

The InfoSec Handbook

Computer scienc

Design and Analysis of a Dynamically Configured Log-based Distributed Security Event Detection Methodology

Military and defense organizations rely upon the security of data stored in, and communicated through, their cyber infrastructure to fulfill their mission objectives. It is essential to identify threats to the cyber infrastructure in a timely manner, so that mission risks can be recognized and mitigated. Centralized event logging and correlation is a proven method for identifying threats to cyber resources. However, centralized event logging is inflexible and does not scale well, because it consumes excessive network bandwidth and imposes significant storage and processing requirements on the central event log server. In this paper, we present a flexible, distributed event correlation system designed to overcome these limitations by distributing the event correlation workload across the network of event-producing systems. To demonstrate the utility of the methodology, we model and simulate centralized, decentralized, and hybrid log analysis environments over three accountability levels and compare their performance in terms of detection capability, network bandwidth utilization, database query efficiency, and configurability. The results show that when compared to centralized event correlation, dynamically configured distributed event correlation provides increased flexibility, a significant reduction in network traffic in low and medium accountability environments, and a decrease in database query execution time in the high-accountability case

Data Mining Techniques to Understand Textual Data

More than ever, information delivery online and storage heavily rely on text. Billions of texts are produced every day in the form of documents, news, logs, search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Text understanding is a fundamental and essential task involving broad research topics, and contributes to many applications in the areas text summarization, search engine, recommendation systems, online advertising, conversational bot and so on. However, understanding text for computers is never a trivial task, especially for noisy and ambiguous text such as logs, search queries. This dissertation mainly focuses on textual understanding tasks derived from the two domains, i.e., disaster management and IT service management that mainly utilizing textual data as an information carrier. Improving situation awareness in disaster management and alleviating human efforts involved in IT service management dictates more intelligent and efficient solutions to understand the textual data acting as the main information carrier in the two domains. From the perspective of data mining, four directions are identified: (1) Intelligently generate a storyline summarizing the evolution of a hurricane from relevant online corpus; (2) Automatically recommending resolutions according to the textual symptom description in a ticket; (3) Gradually adapting the resolution recommendation system for time correlated features derived from text; (4) Efficiently learning distributed representation for short and lousy ticket symptom descriptions and resolutions. Provided with different types of textual data, data mining techniques proposed in those four research directions successfully address our tasks to understand and extract valuable knowledge from those textual data. My dissertation will address the research topics outlined above. Concretely, I will focus on designing and developing data mining methodologies to better understand textual information, including (1) a storyline generation method for efficient summarization of natural hurricanes based on crawled online corpus; (2) a recommendation framework for automated ticket resolution in IT service management; (3) an adaptive recommendation system on time-varying temporal correlated features derived from text; (4) a deep neural ranking model not only successfully recommending resolutions but also efficiently outputting distributed representation for ticket descriptions and resolutions

Introductory Computer Forensics

INTERPOL (International Police) built cybercrime programs to keep up with emerging cyber threats, and aims to coordinate and assist international operations for ?ghting crimes involving computers. Although signi?cant international efforts are being made in dealing with cybercrime and cyber-terrorism, ?nding effective, cooperative, and collaborative ways to deal with complicated cases that span multiple jurisdictions has proven dif?cult in practic

Gitek Bestill

Gitek Bestill is a system where the merchant will order bread from the bakers, who will then process these orders. The system has a calendar where all the placed orders can be seen, and running campaigns can be displayed. Shrinkage can be registered and a list can be viewed with the shrinkage of the different products. The bakers can add, delete and change products. A search for orders can be done by both merchants and bakers. Gitek Bestill has been developed in HTML, CSS, PHP, JavaScript/jQuery and MySQL.Gitek Bestill er et system for brødbestilling foretatt av kjøpmenn i Coop, og bakere som tar i mot disse bestillingene. Systemet har en kalenderoversikt hvor man ser plasserte ordre, og aktuelle kampanjer. Svinn på brød kan også registreres og man ser liste over brødene med svinn. Bakere har mulighet for å legge til, slette og endre produkter. Søk etter ordre finnes for både kjøpmenn og bakere. Gitek Bestill er utviklet i HTML, CSS, PHP, Javascript/ jQuery og MySQL.Gitek A