Intrusion Detection System with Snort in Cloud Computing: Advanced IDS

An intrusion detection system (IDS) is a device or software application that monitors a network for malicious activity or policy violations. It scans a network or a system for a harmful activity or security breaching. IDS protects networks (Network-based intrusion detection system NIDS) or hosts (Host-based intrusion detection system HIDS), and work by either looking for signatures of known attacks or deviations from normal activity. Deep learning algorithms proved their effectiveness in intrusion detection compared to other machine learning methods. In this paper, we implemented deep learning solutions for detecting attacks based on Long Short-Term Memory (LSTM). PCA (principal component analysis) and Mutual information (MI) are used as dimensionality reduction and feature selection techniques. Our approach was tested on a benchmark data set, KDD99, and the experimental outcomes show that models based on PCA achieve the best accuracy for training and testing, in both binary and multiclass classification.

DSS for computer security incident response applying CBR and collaborative response

With the ever increasing use of computers for critical systems, computer security, the protection of data and computer systems from intentional, malicious intervention, is attracting much attention. Among the methods for defence, intrusion detection , i.e. the application of a tool to help the operator identify ongoing or already perpetrated attacks has been the subject of considerable research in the past ten years. A key problem with current intrusion detection systems is the high number of false alarms they produce. This thesis presents research into why false alarms are and will remain a problem and proposes to apply results from the field of information visualisation to the problem of intrusion detection. This was thought to enable the operator to correctly identify false (and true) alarms, and also aid the operator in identifying other operational characteristics of intrusion detection systems. Four different visualisation approaches were tried, mainly on data from web server access logs. Two direct approaches were tried; where the system puts the onus of identifying the malicious access requests on the operator by way of the visualisation. Two indirect approaches were also tried; where the state of two self learning automated intrusion detection systems were visualised to enable the operator to examine their inner workings. This with the hope that in doing so, the operator would gain an understanding of how the intrusion detections systems operated and whether that level of operation, and the quality of the output, was satisfactory. Several experiments were performed and many different attacks in web access data from publicly available web servers were found. The visualisation helped the operator either detect the attacks herself and more importantly the false alarms. It also helped her determine whether other aspects of the operation of the self learning intrusion detection systems were satisfactory.

This paper presents an overview of the technologies and the methodologies used in Network Intrusion Detection and Prevention Systems (NIDPS). Intrusion Detection and Prevention System (IDPS) technologies are differentiated by types of events that IDPSs can recognize, by types of devices that IDPSs monitor and by activity. NIDPSs monitor and analyze the streams of network packets in order to detect security incidents. The main methodology used by NIDPSs is protocol analysis. Protocol analysis requires good knowledge of the theory of the main protocols, their definition, how each protocol works.Keywords: Intrusion Detection and Prevention System, Protocol Analysis, Sensor, Signature, State(ProQuest: ... denotes formulae omitted.)1 IntroductionIncreasing size and complexity of the Internet and Intranet networks have led to increasing number of vulnerabilities that could be exploited. Thus, the internal and external attacks on the information systems are increasing at an alarming rate. Also, these are becoming more severe and sophisticated. The attackers find ingenious ways to bypass the security controls and to compromise the security and the well functioning of the information systems. They are motivated by financial, political, and military objectives. In this context, defending wide area networks from malicious traffic, unauthorized access to systems involves many problems.In security information systems Network Intrusion Detection and Prevention Systems (NIDPS) are important tools to detect possible incidents and also, to attempt to stop them in real time. Due to changing attacks, intrusion detection methodologies and technologies continuously evolve, adding new detection capabilities, to avoid detection. They must adapt to new forms of malware, to the public networks, increased traffic.2 Concepts of Intrusion DetectionAn intrusion is a successful action to gain access to an information system, to compromise it or to make it unavailable. This is possible due to the presence of vulnerability in the target system that can be exploited by a motivated intruder.Intrusion Detection and Prevention is the process of monitoring the information systems by sensors or agents and analyzing the collected information to detect and to attempt to stop the attacks in real time, identifying vulnerabilities, the violation of security policies or standard security practices.An Intrusion Detection and Prevention System (IDPS) is a tool that monitors information systems, collects, analyzes information, and initiates responses when an intrusion is detected.Intrusion Detection Systems (IDSs) mainly work as defensive mechanisms. They only alert the system administrators that an incident has occurred. Intrusion Prevention Systems (IPSs) can take some actions to attempt to stop the attack, such as breaking the connection or modifying the firewall rules to deny access to the intruder. The response of the classic IDS can be slow if the system administrator is busy while the response of the IPS is automatic. An architecture that uses together IPS and IDS technologies is the best solution for defense in depth.Conceptually, a generic IDPS consists of modular components. It mainly has the following components: monitoring system, storage, analyzer, and responder.* Monitoring system - monitors and logs the events in a computer system or network;* Storage - stores information, called audit record, about suspicious activities or intrusions; also, the security policies used in analysis are stored;* Analyzer - uses different analysis methodologies to detect the incidents;* Responder - the response mechanism of incidents.The IDPSs could be classified as:* By detection methodology [12], [18]:- misuse-based detection- anomaly-based detection- stateful protocol analysis* By activity [12]:- network-based- wireless-based- network behavior analysis- host-based* By behavior on detection:- passive- active* By collection and analysis frequency:- continuous- periodicThe detection methodologies describe the characteristics of the analyzer. …

Cloud computing has gained an extreme importance nowadays. Every organization is getting attracted toward the Cloud computing due to its attractive features like cost saving, adaptability, etc. Although it offers the attractive features but still Cloud threats need great consideration. The insider threat is critically challenging in the Cloud-based environments. In order to mitigate from insider attacks in Clouds, the use of Intrusion detection system (IDS) is quite challenging. Every type of IDS has different methods of attack detection. So, single IDS cannot guarantee the protection from all types of attacks. Thus, in this paper, we have studied the various types of IDS and their features which made them either suitable or unsuitable for cloud computing. Also on the basis of review, required features for the Cloud-based IDS are identified.

Chapter 5 - Blockchain for intrusion detection systems

Cloud computing is a recent innovation in the IT industry that is expanding quickly. Furthermore, this technology is widely used to provide computation, data storage, and other resources remotely through the web on a pay-per-usage basis. It is now the favored option for any IT firm since it increases its capacity to satisfy the computing requirements of its everyday operations through scalability, mobility, and flexibility at a low price. But there are two key problems with cloud computing. The biggest issue is storage-related, and Google has addressed it by adding a new layer to the cloud dubbed “Big data as a service (BDaaS).” The second problem is security and privacy. The Intrusion Detection System (IDS) has become the most widely utilized component of computer systems, security, and compliance processes, safeguarding network-accessible Cloud resources and services from various threats and assaults. This study examines IDS approaches in Cloud Computing and big data sets. To identify anomalous data in BDaaS, we also suggested a sensible intrusion detection system (SIDS) based on the autonomic system. The agent contributes the most to introducing more proprieties, particularly the autonomy element.

Cloud computing relies on sharing computing resources. With high availability and accessibility of resources, cloud computing is under the threat of major cyber-attacks. To detect attacks and preserve security in cloud environment, having an efficient intrusion detection system (IDS) is required. In this article, an effective and efficient IDS is proposed to maintain high level security of data in cloud. The authors have incorporated Reinforcement Learning Automata with their proposed IDS while detecting and classifying attacks. Using learning automata an effective rule set is generated with the proposed algorithm from vast training set to improve the learning process at reduced computation cost and time. After which, the proposed reinforcement learning algorithm helps in classification of attacks accurately using the reinforcement signal. This proposed model was experimented with NSL-KDD as well as KDD 10% dataset and have proved its robustness by detecting attacks more accurately being an IDS.

Intrusion Detection Systems (IDSs) are widely deployed in computer networks to stand against a wide variety of attacks. IDSs deployment raises a serious problem, namely managing of a large number of triggered alerts. This problem becomes worse by the fact that some commercial IDSs may generate thousands of alerts per day. Identifying the real alarms from the huge volume of alarms is a frustrating task for security officers. Thus, reducing false alarms is a critical issue in IDSs efficiency and usability. In this paper, we mine historical alarms to learn how future alarms can be handled more efficiently. First, an approach is proposed for characterizing the “normal” stream of alarms. In addition, an algorithm for detecting anomalies by using continuous and discontinuous sequential patterns is developed, and used in preliminary experiments with real-world data to show that the presented model can handle IDSs alarms efficiently.

Intrusion detection systems (IDS) are systems that are used to monitor networks for malicious events, abnormal activities, and policy violations. They are systems that are capable of detecting and classifying network attacks based on behaviors or signatures of previously known attacks based on markers. However, since network attacks are constantly evolving and it is almost impossible to infuse all possible combinations and signatures of the attacks, the effectiveness of Machine Learning based IDS is often challenged and called into play as a result of novel attacks generated, known as Zero-day attacks. This has facilitated the need to have intelligent-based IDS that could detect anomalies without relying on a detailed signature repository. In this paper, we present a literature-based survey of popular deep learning algorithms and evaluated their capabilities, strengths, limitations, and resource requirements for detecting anomalies and Zero-Day attacks. Based on our evaluation, we propose Long Short-Term (LSTM) networks and Autoencoder networks as the best algorithms for further analysis in intrusion detection.

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

Cloud computing plays an important role in web .The term cloud computing is based on internet computing. Cloud computing is a service to perform tasks through internet. It provides large scale of resource to each user using virtual machine. Cloud computing is easily threatened by the users because all users are not trustworthy. This is the first problem. The second problem in cloud computing is number of users can use the service but it makes the admin to analyze the log. In this paper ,we propose the multilevel intrusion detection system and log audit for detecting the attacks and maintain the security level and effectiveness of the system. The intrusion detection system is based on data mining techniques and anomaly detection method.

SummaryCloud computing is an active environment for accessing shared resources. Security in this environment, is one of the most important issues and concerns. An intrusion detection system is used to provide security and privacy. The intrusion detection system distinguishes between attacks and normal activities and prevents attacks on the cloud environment and notifies managers and cloud providers by creating alerts. Intrusion detection systems are usually implemented with different solutions. In this article, we present an intrusion detection system based on firefly algorithm and random forest algorithm that has the ability to detect intrusion with high accuracy in cloud computing, the results of model testing and evaluation, efficient and appropriate indicates the existence of the proposed method.

Abstract— In the evolving digital landscape, cloud computing has become a cornerstone for scalable and flexible IT infrastructure. However, the rapid adoption of cloud services introduces a wide range of security threats, compliance challenges, and operational risks. This paper, From Threats to Trust: Securing Cloud Infrastructure through Resilience and Compliance presents a comprehensive analysis of cloud security challenges and explores effective mitigation strategies to enhance resilience and regulatory adherence. The study examines critical issues such as data breaches, insider threats, vendor lock-in, insecure interfaces, and distributed denial-of-service (DDoS) attacks within cloud environments. It further highlights the importance of robust security mechanisms, including identity and access management, encryption techniques, continuous monitoring, intrusion detection systems, and secure key management. Emphasis is also placed on compliance with global data protection regulations and the implementation of governance frameworks. By integrating best practices with proactive risk management approaches, this paper provides a structured framework for building secure, resilient, and compliant cloud infrastructures. The findings aim to support organizations in strengthening their security posture, ensuring data integrity and availability, and fostering trust in cloud-based systems. Keywords— Cloud Security, Cloud Computing, Identity and Access Management and Intrusion Detection Systems.

Signature-based intrusion detection systems use a set of attack descriptions to analyze event streams, looking for evidence of malicious behavior. If the signatures are expressed in a well-defined language, it is possible to analyze the attack signatures and automatically generate events or series of events that conform to the attack descriptions. This approach has been used in tools whose goal is to force intrusion detection systems to generate a large number of detection alerts. The resulting "alert storm" is used to desensitize intrusion detection system administrators and hide attacks in the event stream. We apply a similar technique to perform testing of intrusion detection systems. Signatures from one intrusion detection system are used as input to an event stream generator that produces randomized synthetic events that match the input signatures. The resulting event stream is then fed to a number of different intrusion detection systems and the results are analyzed. This paper presents the general testing approach and describes the first prototype of a tool, called Mucus, that automatically generates network traffic using the signatures of the Snort network-based intrusion detection system. The paper describes preliminary cross-testing experiments with both an open-source and a commercial tool and reports the results. An evasion attack that was discovered as a result of analyzing the test results is also presented.