Collaborative Intrusion Detection System with Snort Machine Learning Plugin

The Intrusion Detection System (IDS) is a useful infiltration detection tool. With IDS, it may mechanically categorize intrusions, attacks, or breaches of security stratagems across host-level within the network and within the organization. The Krill Herd Optimized Deep Neural Network (KH-DNN) is proposed in this document as a multiclass cyberattack methodology. This model is suggested by uniting the network-based intrusion detection system (NIDS) and the host-based intrusion detection system (HIDS) to advance flexible and actual IDS to categorize and detect unexpected and unpredictable cyber attacks. In this method, two types of IDS are classified, that is, NIDS and HIDS. Classification of the system call is exploited to discriminate behavior into normal and attack categories. In this manuscript accuracy, the parameter of the deep neural network classifier is optimized with the Krill Herd optimization algorithm. Finally, the DNN model is performed in the NIDS dataset and the HIDS dataset. For the HIDS dataset, the KDD Cup 99 dataset was used, which employed two datasets: ADFA-LD and ADFA-WD, and several types of attacks were detected and classified, including Adduser, Hydra-FTP, Java-Meterpreter, Hydra-SSH, and Web-Shell. The suggested IDS-KH-DNN algorithm attains higher accuracy 42.56% and 23.4%, high precision 84.74% and 52.43%, high F-score 53.5% 64.455, high sensitivity 33.4% and 45.23%, and high specificity 31.45% and 44.23% for the NIDS dataset. The proposed system is compared to two current processes: intrusion detection using Gray Wolf optimization-based support vector machine (IDS-GWO-SVM) and intrusion detection using artificial bee colony optimization algorithm based support vector machine (IDS-ABCO-SVM). Finally, the simulation results show that the suggested approach is capable of quickly and accurately locating optimal solutions.

This chapter focuses on the very serious problem of denial of service (DoS) attacks against computer systems and networks. DoS attacks are attacks designed to disrupt, overwhelm, and/or damage computing resources and data. These attacks are aimed not against confidentiality of data or integrity of systems and data, but rather against availability of systems and data. Attackers launch DoS attacks for a wide variety of reasons. The probability of success is high, due in large part to the fact that the Internet itself has not been designed for security. The cost of DoS attacks is often high—several well‐known attacks have reportedly cost organizations billions of dollars. Many types of DoS attacks have surfaced so far, including resource starvation attacks, buffer overflow attacks, and packet fragmentation attacks. Distributed denial of service (DDoS) attacks, attacks in which programs planted in multiple systems cooperate, represent an even higher level of threat. Preventing DoS attacks is difficult in the first place due to a plethora of reasons; limiting the potential for damage and disruption is often the most cost‐effective strategy. Hot sites, cold sites, use of uninterruptible power supplies (UPSs) and Redundant Array of Independent Drives (RAID) solutions, and other measures can limit the damage from DoS attacks. Using properly configured and maintained firewalls is one of the best single prevention measures. Additionally, using packet filters on systems, keeping up with patches, limiting services that run on these systems, and using other measures go a long way in helping prevent susceptibility to DoS attacks.

: Network-based intrusion detection systems (NIDSs) are one component of a comprehensive network security solution. The use of IPsec, which encrypts network traffic, renders network intrusion detection virtually useless unless traffic is decrypted at network gateways. One alternative to NIDSs, host-based intrusion detection systems (HIDSs), provides some of the functionality of NIDSs but with limitations. HIDSs cannot perform a network-wide analysis and can be subverted if a host is compromised. We propose an approach to intrusion detection that combines HIDS, NIDS, and a version of IPsec that encrypts the header and the body of IP packets separately. We refer to the latter generically as TwoKey IPsec. We show that all of the network events currently detectable by the Snort NIDS on un- encrypted network traffic are also detectable on encrypted network traffic using this approach. The NIDS detects network-level events that HIDSs have trouble detecting and HIDSs detect application-level events that can't be detected by the NIDS.

The mobile ad-hoc networks (MANET) are an emerging research area with applications. It has features like working as a router, dynamic topology and autonomous. In MANET, nodes have limited resources like battery power, storage space and bandwidth. There is a need to do more research for Denial of Service (DoS) attacks in MANET as compared to wire line DoS attacks. MANET is vulnerable to different types of DoS attack in which packets are drop. Black hole attack, packet drop attack and gray hole attack are an event that eliminates a network's capacity to perform its expected function. These attacks are type of DoS attack which we examine in this paper. This paper shows the effects of black hole attack, packet drop attack and gray hole attack on AODV protocol under different performance metrics throughput, packet drop rate and end-to-end delay. The simulation is done in Network Simulator (NS) 2.

Basic Security Concepts - Why is Computer and Network Security Important. Background and History. The Security Trinity. Information Security. Risk Assessment. Security Models. Basic Terminology. More Basic Terminology. Threats, Vulnerabilities and Attacks - Protocols. The OSI Reference Model. TCP/IP Protocol Suite. Useful Web Sites. Search Engines. Mailing Lists. Encryption, Digital Signatures and Certification - Cryptography. Stream Ciphers. Breaking Ciphers. Block Ciphers. Encryption. Public Key Cryptosystems. Message Integrity. Authentication. Digital Signatures. Competing Standards. Digital Certificates. Limitations of Digital Certificates. Certificate Authorities. Public Key Infrastructure. The Future. The Limitations of Encryption. Kerberos - How Kerberos Works. Kerberos- Limitations. Encryption on the WWW - The World Wide Web. Secure Sockets Layer (HTTPS). Secure HTTP (SHTTP). Microsoft's Internet Explorer. Viewing Digital Certificates with Internet Explorer. Viewing the Encryption Strength of IE5. Viewing Certification Authorities with IE5. Netscape Navigator. Viewing Digital Certificates with Navigator. Authenticode Certificates. E-Mail - E-Mail Issues. E-Mail Issues. Secure E-Mail Protocols. Web-Based E-Mail Services. Security of Stored Messages. Identity: Spoofing and Hiding. E-Mail as a weapon. E-Mail Policies. E-Mail Privacy. Auto-Responses. Operating System Security -- Passwords. Password Attacks. Onetime Passwords. Access Control. Data Redundancy. General Recommendations. Modems. Useful Tools. LAN Security - LAN Guidelines. Controlling End-User Access. Concurrent Logins. Available Disk Space. Restrictions to Location or Workstation. Time/Day Restrictions. Access to Directories and Trustee Rights. File Attributes. Other Privileges. Single Sign-On. Policy-Based Network Management. Honeypot Systems. Network Segmentation. Static IP Addresses vs. DHCP. Media and Protocols - Network Media. Plenum Cabling and Risers. WANs. Redundancy and Alternative Connections. Routers and SNMP - Router Issues. SNMP. Virtual Private Networks - Encryption on the Network. Node-to-Node Encryption. End-to-End Encryption. Where to Encrypt. Virtual Private Networks. PPTP. L2TP. IPSec. SOCKS. Firewalls - Firewalls Pros and Cons. Types of Firewalls. Packet Filters vs. Proxies. Firewall Configurations. Restricting Users' Access to the Internet. Firewall Products. Personal Firewalls. Biometrics - Identification and Authentication. Biometric Identification and Authentication. Biometric Identification Reliability. Backup Authentication. Environmental Conditions. User Acceptance. Security of the Biometric System. Interoperability. Costs vs. Savings. Policies and Procedures - Policies vs. Procedures. Information Security Policy Objectives. Developing Security Policies. Policy and Procedure Manuals. Policy Awareness & Education. Policy Enforcement. Policy Format. Security Policy Suggestions. Information Protection Team. Crisis Management Planning. Sources for Information Policies. Auditing and Intrusion Detection - What is an Audit. Operational Security Audits. System Security Auditing. Activity and Usage Auditing. Audit Mistakes. Deficiencies of Traditional Audit Techniques. Intrusion Detection. Intrusion Detection Systems. Host-Based Intrusion Detection Systems. Network-Based Intrusion Detection Systems. Knowledge-Based Intrusion Detection Systems. Statistical-Based Intrusion Detection Systems. Defense In-Depth Approach . Future Directions. Crisis Management Planning - Crisis Management. Disaster Recovery Planning. Computer Security Incident Response Plan. Browser Security - Cookie Files. Cache Files. Autocomplete.

Intrusion detection System forms a vital component of internet security. To keep pace with the growing trends, there is a critical need to replace single layer detection technology with multi layer detection. Different types of Denial of Service (DoS) attacks thwart authorized users from gaining access to the networks and we tried to detect as well as alleviate some of those attacks. In this paper, we have proposed a novel cross layer intrusion detection architecture to discover the malicious nodes and different types of DoS attacks by exploiting the information available across different layers of protocol stack in order to improve the accuracy of detection. We have used cooperative anomaly intrusion detection with data mining technique to enhance the proposed architecture. We have implemented fixed width clustering algorithm for efficient detection of the anomalies in the MANET traffic and also generated different types of attacks in the network. The simulation of the proposed architecture is performed in OPNET simulator and we got the result as we expected.

The increasing use of Internet of Things (IoT) devices has led to a rise in Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks on these networks. These attacks can have severe consequences, resulting in the unavailability of critical services and financial losses. In this paper, we propose an Intrusion Detection System (IDS) based on a Conditional Tabular Generative Adversarial Network (CTGAN) for detecting DDoS and DoS attacks on IoT networks. Our CGAN-based IDS utilizes a generator network to produce synthetic traffic that mimics legitimate traffic patterns, while the discriminator network learns to differentiate between legitimate and malicious traffic. The syntactic tabular data generated by CTGAN is employed to train multiple shallow machine-learning and deep-learning classifiers, enhancing their detection model performance. The proposed approach is evaluated using the Bot-IoT dataset, measuring detection accuracy, precision, recall, and F1 measure. Our experimental results demonstrate the accurate detection of DDoS and DoS attacks on IoT networks using the proposed approach. Furthermore, the results highlight the significant contribution of CTGAN in improving the performance of detection models in machine learning and deep learning classifiers.

With the continuous development of computer networks, the security of the network has become increasingly prominent. A major threat to network security is the intrusion of information systems through the network. Intrusion detection of the traditional intrusion detection and alarm technology is not sufficient. Based on neural network technology, this paper studies the intrusion detection and alarm correlation technology. Based on the research on the working principle and workflow of the existing intrusion detection system, a new neural network-based intrusion detection and alarm method is proposed. A neural network-based intrusion detection and alarm system is designed and implemented. Through the experiment of the system prototype, the results show that the intrusion detection and alarm system based on the neural network has a higher detection rate and a lower false alarm rate for intrusion behaviors such as denial of service attack and has higher detection ability for unknown attack behaviors.

Nowadays, Denial of service (DoS) attacks, have become a major security threat to networks and to the Internet, DoS is harmful to networks as it delays legitimate users from accessing the server, In general, some researches were done to detect and prevent DoS from occurring in a wide area network (WAN), but fewer researches were done on Local Area Network (LAN.), yet, detecting and preventing DoS attacks is still a challenging task, especially in LAN. In this paper, we propose an approach merging methods from data mining to detect and prevent DoS attacks, by using multi classification techniques to achieve a sufficient level of accuracy and reduce false alert alarm. And secondly, we will evaluate our approach in comparison with other existing approaches. Our work is based on EGH Dataset to detect DoS attacks, in addition, our approach is implemented using Rapidminer, the experimental results show that the proposed approach is effective in identifying DoS attacks, our designed approach achieves significant results. In the best case, our accuracy is up to 99.96%, we used two component of security; Snort tool and PfSense firewall, and compared our approach with other approaches, and we found that our approach achieves best accuracy results in most cases. Keywords— Data Mining, DoS attacks, intrusion detection, Misuse Detection, Multi Classification.

In ad-hoc and wireless networks, Denial of Service (DoS) attacks has always been problematic during data routing. DoS attacks disrupt the data routing and avert the generated data to reach the destination or the sink nodes. In tunnel attack, which is one of the type of DoS attacks, the intruder attracts all the traffic towards itself and prevent it from forwarding further to the neighboring nodes. Researchers have presented multiple solutions to cater the problem and minimize or avoid the tunnel attacks, but they all have their shortcomings. The main reason behind all the drawbacks is that those solutions have not been confirmed using formal methods. The formal methods can be utilized to authenticate whether or not the solutions are immune to DoS attacks. In our previous researches, it has been revealed that by employing formal methods, our newly developed protocol RAEED can detect and avoid many DoS attacks. In this research paper, it has been proved using model checker that RAEED can efficiently avoid both simple and intelligent tunnel attacks. Moreover, the results of formal methods have been confirmed with the help of computer simulations as well as practical implementation on MicaZ motes. In future we aim to employ formal methods to verify more wireless communication issues in multiple protocols.

Vehicular ad hoc networks (VANETs) are intelligent transport subsystems; vehicles can communicate through a wireless medium in this system. There are many applications of VANETs such as traffic safety and preventing the accident of vehicles. Many attacks affect VANETs communication such as denial of service (DoS) and distributed denial of service (DDoS). In the past few years the number of DoS (denial of service) attacks are increasing, so network security and protection of the communication systems are challenging topics; intrusion detection systems need to be improved to identify these attacks effectively and efficiently. Many researchers are currently interested in enhancing the security of VANETs. Based on intrusion detection systems (IDS), machine learning (ML) techniques were employed to develop high-security capabilities. A massive dataset containing application layer network traffic is deployed for this purpose. Interpretability technique Local interpretable model-agnostic explanations (LIME) technique for better interpretation model functionality and accuracy. Experimental results demonstrate that utilizing a random forest (RF) classifier achieves 100% accuracy, demonstrating its capability to identify intrusion-based threats in a VANET setting. In addition, LIME is applied to the RF machine learning model to explain and interpret the classification, and the performance of machine learning models is evaluated in terms of accuracy, recall, and F1 score.

In this work, a Behaviour-based Intrusion Detection Model is suggested. The proposed model can be employed from a single host configuration to a distributed mixture of host-based and network-based Intrusion Detection Systems (IDSs). Unlike most state-of-the-art IDSs that rely on analysing lower-level, raw-data representations, our proposed architecture suggests to use higher-level notions -behaviours- instead; this way, the IDS is able to identify more sophisticated attacks. To assess our premise, a Behaviour-based IDS (BIDS) prototype has been designed and developed that scans file system data to identify attacks. BIDS achieves high detection rates with low corresponding false positive rates, superseding other state-of-the-art file system IDSs.

Chapter 9 - Intrusion Detection Systems: Theory

Denial of Service (DoS) attacks are increasingly dangerous. This DoS attack works by sending data packets continuously so that the target being attacked cannot be operated anymore. DoS attacks attack the most websites, thus making the website inaccessible. An anomaly based intrusion detection system (IDS) is a method used to detect suspicious activity in a system or network on the basis of anomaly pattern arising from such interference. Wireshark is software used to analyze network traffic packets that have various kinds of tools for network professionals.

In WSN, DoS (denial of service) attack makes shortcoming system. The packets travel over and over in the sensor network. By that, all the assets like data transmission, memory, and vitality are squandered by this attack. However, the attacker ought to optimize its attacker plan for request to boost the impact on the system performance because of the deficiency of vitality at the aggressor side. Denial of service (DoS) attack on the Internet has become a squeezing issue. By staying away from these sorts of attacks, network performance can be improved. Therefore, security is a fundamental requirement for these networks. Effective routing is necessary in order to overcome the issued faced by the crosslayer in the DOS attack of the WSN network for the purpose of good transmission. This research work mainly focuses on performance evaluation using optimization methods. To establish the efficient path in the crosslayer against DoS attack, this paper has proposed enhanced lion optimization with an efficient path routing equalization technique (LOEPRE). If any failure node occurs in the network, then the node is recognized and the transfer of the data packet is done to the other node. Retransmission of data causes overload in the network. The proposed model focuses on these issues and overcomes these issues by improving the path efficiently with robust security. It consists of three phases: the initial phase includes the route discovery in the network. In the second phase, the transfer of data is done in the high router path for security purposes. Finally, the efficient path routing equalization technique is used for minimizing the overload in the network; it provides the equalized path length in the network and is highly efficient. Hence, the proposed LOEPRE technique is used to achieve energy efficiency in wireless network for prolonged network lifetime and minimum packet latency and minimize consumption of energy. Moreover, the simulation outcome of the proposed LOEPRE method is highly robust while comparing to the existing methods EFCRS, SSPRA ELOER, EFLOR, and TSTP. It achieves better performance than existing algorithms in comparing metric connectivity ratio, end-to-end delay, overhead, throughput, and packet delivery ratio.