IDS

Diplomsko delo popisuje in razčlenjuje zgodovino študentskih gibanj v Sloveniji. Poleg začetnih poglavij, ki zaobjemajo študentsko gibanje v 20. stoletju, se osrednji del koncentrira na sodobno zgodovino 21. stoletja, v čas oživljanja študentskih bojev. Eden poglavitnih dejavnikov sodobne zgodovine, ki v veliki meri določa zadnje desetletje, je zagotovo ekonomska kriza, na katero so se vladajoče politike odzvale z varčevanjem in rezanjem sredstev za visoko šolstvo. V diplomsko delo sem umestil ključne zgodovinske trenutke in etape študentskega organiziranja, ki zaobjemajo študentsko aktivacijo proti visokošolskemu zakonu v letu 2007, boj proti zakonu o malem delu, ustanovitev Fronte prekercev, zasedbo Filozofske fakultete ter ustanovitev in delovanje organizacije Iskra, ki je nadaljevala boj proti nadaljnjemu varčevanju in organizirala odpor proti ZVIS-u leta 2014.

Mobile Ad-Ho c Network (MANET) is one of the most promising technologi es that have applications in military, environm ental, space exploration and forestry industry areas. This typ e of network has attractive features such as its low transmission p ower to conserve energy, increase throughput, and reduce delay. However, it suffers from many constraints, incl uding limited resources, and the use of insecure wireless communication channels. D ue to the lack of defense, the security of these networks is a worthy concern, particula rly for the applications where confidentiality has prime imp ortance. Thus, any kind of intrusions should b e detected b efore attackers can harm the network in order to op erate MANET in a secure way. In this article, we present a survey of the state-of-theart in Intrusion Detection Systems (IDSs) that are prop osed for MANETs. This is followed by a comparison of each scheme along with t hei r advantages and di sadvantages. This sur vey is concluded by highlighti ng op en research issues in the field

Among various cyber threats, a DDoS attack is one of the major Internet threats that can affect anyone and even cause tremendous financial damage to organization that uses cloud-based services, while the mitigation of this threat can be highly difficult considering the complex infrastructure that it uses to perform its malicious activities. For that purpose it’s important to think proactively rather than reactively when addressing the protection against this type of attacks. The overview of botnets and some of the countermeasures against this threat were discussed in this paper.

"Network intrusion detection system (NIDS)" monitors traffic on a network looking for doubtful activity, which could be an attack or illegal activity. The intrusion detection techniques based upon data mining are generally plummet into one of two categories: misuse detection and anomaly detection. In misuse detection, each instance in a data set is labeled as 'normal' or 'intrusive' and a learning algorithm is trained over the labeled data. In this paper we will discuss about the steps involved in NIDS, further we will compare different techniques of NIDS based on accuracy parameter i.e. precision and recall.

The use of the Internet has increased in all areas in recent years. With the huge growth and use of the internet increasing, there have been an increase in the number of intrusions and hackers. The risk of intrusion in the network environment is serious. The basic concept of the intrusion detection system is highlighted in this report. Most of IDS' work is based on two approaches: the approach to anomaly and the approach to misuse. This paper provides a short assessment of intrusion detection taxonomy and literature. As a starting point for research in the field of intrusion detection, an extensive bibliography is provided. The purpose of this paper is to cover the analysis of various available intrusion detection tools and data mining techniques for detecting intrusions in network. A review on current trends in intrusion detection together with the study on technologies implemented by some researchers in this area is presented

In In spite of the tremendous growth of technologies in computer networking and information technology, still we lack in preventing our resources from theft/attacks. This problem is very big as far as industry / companies are concerned. As maximum of the organizations are facing an increasing number of threats every day in the form of viruses and attack etc. Since many different mechanisms were opted by organizations in the form of intrusion detection and prevention system to protect its organizations for these kinds of attacks, still there are many security breaches in every organization. In order to understand the security risks and IDPS, we will first survey about the common security breaches and then after discuss what are different opportunities and challenges in this particular field. In this paper we made a survey on the overall progress of intrusion detection systems. We survey the existing types, techniques and architectures of Intrusion Detection Systems in the literature....

With the increased dependence of organizations on technological solutions, the cyber threats have become some of the major concerns for the very existence of the businesses. Thus, the security measures to be implemented need to go beyond a simple presence of a firewall and anti-malware. In this work, an overview of two Intrusion Detection and Prevention systems (IDPS) was performed. Namely, the architecture of Snort and Suricata IDPS engines was discussed.

व्यक्ति के व्यवहार का निर्धारण स्वयं केवल व्यक्ति के द्वारा नहीं होता है अपितु उसका व्यवहार पर्यावरण, आय-व्यय, राजनीति, शिक्षा, पास-पड़ोस, स्वानुभव, परिवार, समाज, सस्कृति, धर्म, जाति, राष्ट्र एवं तात्कालिक परिस्थितियों आदि से निर्धारित होता है. इसलिए किसी व्यक्ति के बारे किसी अनुशासन विशेष द्वारा किया गया अध्ययन पूर्ण नहीं हो सकता है. इसी सम्पूर्णता के लिए जब विभिन्न ज्ञानानुशासन किसी एक सामान्य मंच पर आकर अपने विषय सामग्री, अपनी अंतर्दृष्टि, अपनी प्रविधि, अवधारणा एवं सिद्धांतों के आदान-प्रदान के माध्यम से किसी समस्या के समाधान का प्रयास करते हैं तो ऐसे अध्ययन को अंतर-अनुशासनिक अध्ययन कहा जाता है. अंतर-अनुशासनिक अध्ययन घटना प्रधान होते हैं.

Onomatopoeia are frequently identified amongst infants’ earliest words (Menn & Vihman, 2011), yet few authors have considered why this might be, and even fewer have explored this phenomenon empirically. Here we analyze mothers’ production of onomatopoeia in infant-directed speech (IDS) to provide an input-based perspective on these forms. Twelve mothers were recorded interacting with their 8-month-olds; onomatopoeic words (e.g. quack) were compared acoustically with their corresponding conventional words (duck). Onomatopoeia were more salient than conventional words across all features measured: mean pitch, pitch range, word duration, repetition, and pause length. Furthermore, a systematic pattern was observed in the production of onomatopoeia, suggesting a conventionalized approach to mothers’ production of these words in IDS.

It is the aim of this study to establish live neonatal nurse and parental infant-directed singing as a potentially efficacious intervention that is beneficial to the health and development of infants in neonatal care and which could conceivably be integrated into nursing practice and the neonatal care environment in a way that is both practical and viable. An appraisal of extant literature highlights the need for further intervention in neonatal care, explains how music meets the necessary criterion as appropriate intervention and discusses its potential, specifically infant-directed singing, to be provided by neonatal nurses and parents. The willingness of neonatal nurses and parents to engage in infant-directed singing in the neonatal care unit environment is assessed through an informal questionnaire survey. From the findings of this and other studies, implications and recommendations for both areas and methods for future research are extrapolated. The intention behind this study is to generate interest and awareness of academics and medical professionals in this potentially cost effective intervention, promoting future research and practice. It is hoped that this will ultimately result in the comfort of song providing profound benefits to the physiological, psychological and emotional development and wellbeing of all infants in neonatal care.

Plant acclimation to stress is associated with profound changes in proteome composition. Since proteins are directly involved in plant stress response, proteomics studies can significantly contribute to unravel the possible relationships between protein abundance and plant stress acclimation. In this review, proteomics studies dealing with plant response to a broad range of abiotic stress factors — cold, heat, drought, waterlogging, salinity, ozone treatment, hypoxia and anoxia, herbicide treatments, inadequate or excessive light conditions, disbalances in mineral nutrition, enhanced concentrations of heavy metals, radioactivity and mechanical wounding are discussed. Most studies have been carried out on model plants Arabidopsis thaliana and rice due to large protein sequence databases available; however, the variety of plant species used for proteomics analyses is rapidly increasing. Protein response pathways shared by different plant species under various stress conditions (glycolytic pathway, enzymes of ascorbate–glutathione cycle, accumulation of LEA proteins) as well as pathways unique to a given stress are discussed. Results from proteomics studies are interpreted with respect to physiological factors determining plant stress response. In conclusion, examples of application of proteomics studies in search for protein markers underlying phenotypic variation in physiological parameters associated with plant stress tolerance are given.

Hybrid intrusion detection systems that make use of data mining techniques, in order to improve effectiveness, have been actively pursued in the last decade. However, their complexity to build detection models has become very expensive when confronted with large-scale datasets, making them unviable for real-time retraining. In order to overcome the limitation of the conventional hybrid method, we propose a new lightweight hybrid intrusion detection method that consists of a combination of feature selection, clustering and classification. According to our hypothesis that there are different natures of attack events in each of network protocols, the proposed method examines each of network protocol data separately, but their processes are the same. First, the training dataset is divided into training subsets, depending on their type of network protocol. Next, each training subset is reduced dimensionally by eliminating the irrelevant and redundant features throughout the feature selection process; and then broken down into disjointed regions, depending on their similar feature values, by K-Means clustering. Lastly, the C4.5 decision tree is used to build multiple misuse detection models for suspicious regions, which deviate from the normal and anomaly regions. As a result, each detection model is built from high-quality data, which are less complex and consist of relevant data. For better understanding of the enhanced performance, the proposed method was evaluated through experiments using the NSL-KDD dataset. The experimental results indicate that the proposed method is better in terms of effectiveness (F-value: 0.9957, classification accuracy: 99.52%, false positive rate: 0.26%), and efficiency (the training and testing times of the proposed method are approximately 33% and 25%, respectively, of the time required for its comparison) than the conventional hybrid method using the same algorithm.

In this paper we review the recent advances in honeypot. Some notable proposals and there analysis have been discussed. The aspects of using honeypot in education and in hybrid environment with IDS have been explained. In this paper we also defines the use of signature technique in honeypot for traffic analysis. In the end we summarizes all these aspects.

Data Security has become a very serious part of any organizational information system. Internet threats
have become more intelligent so it can deceive the basic security solutions such as firewalls and antivirus
scanners. To enhance the overall security of the network an additional security layer such as intrusion
detection system (IDS) has to be added. The anomaly detection IDS is a type of IDS that can differentiate between normal and abnormal in the data monitored. This paper proposes two types of IDS, one of them can be used as a network intrusion detection system (NIDS) with overall success (0.9161) and high
detection rate (0.9288) and the other type can also be used as a host intrusion detection system (HIDS) with overall success (0.8493) and very high detection rate (0.9628) using NSL-KDD data set.

Abstract:  In this paper, we discuss the cloud security issues. Cloud computing is a “network of network” over the internet, therefore, chances of intrusion is more with the erudition of intrusion attack. Cloud system security is one of the most important issues that have attracted a lot of research and development effort in past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machine to deploy further large-scale Distributed Denial-Of–Services (DDOS). An intrusion detection system is used to  detect several types of malicious behaviors that can compromise the security and trust of computer system. In a cloud system where the infrastructure is shared by potentially millions of users, abuse and nefarious use of the shared infrastructure benefits attackers to exploit vulnerabilities of the cloud system and use its resource to deploy attacks in more efficient ways. To overcome this problem we are implementing intrusion detection system in which we use genetic algorithm to identify and detect intrusion and also prevent from attack.
Keywords: Cloud computing, network security issue, IDS, Malicious behavior, attack, Packets, analysis.
Title: To Implement Intrusion Detection System for Cloud Computing Using Genetic Algorithm
Author: Ku. Shraddha D. Patil, Ku. Sunita S. Ganveer, Ku. Prachi S. Badge
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Research Publish Journals

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