Intrusion Detection System Thesis

What is an Intrusion Detection System? Intrusion detection systems (IDS) generally work us both hardware and API to ensure security by monitoring the network traffic. It is majorly launched to detect malicious activities that try to modify or delete the real user data. Also, it identifies the users who violate the security policies of the system. This page is about useful information on current Intrusion Detection System Thesis Research Areas and Ideas!!!

Moreover, the important feature of IDS is alert service. Once the intrusion is subjected to be harmful to the user data, then immediately it warns the corresponding user to take preventive measures. In some cases, the IDS gives alert as well as directly react to malicious actions. For instance: If the particular IP address is detected as suspect, then IDS can block traffic generated from that address. Below, we have mentioned two primary categories of intrusion detection systems which are considered as the subsets,

The IDS subsets include:

  • Anomaly-based Intrusion Detection System (ABIDS)
  • Signature-based Intrusion Detection System (SBIDS)

The working principle of IDS is more or less similar to the Antivirus interface. In SBIDS, it monitors all the flowing transmission packets in the communication channel. Then, it matches them with the already stored anomalies signatures/attributes in the common database. If it is matched, then it is reported as malicious activities.

Similarly, ABIDS also observes the traffic in the network and matches them with some pre-defined measures or metrics. These parameters are used to distinguish the normal and abnormal patterns of the network. And, some of them are protocol, bandwidth, data rate, ports, devices, etc. Compare to SBIDS, ABIDS is much faster to deliver the alert message to the users who are at risk of attack in the network. Now, we can see about the different sources of intrusions in the following,

Top 10 research ideas for intrusion detection system thesis

Intrusion Detection System Data Sources 

  • System Calls
  • Logging System
  • Router Netflow Records
  • Rule Patterns
  • Audits Information
  • Management Information Base (MIB)
  • Network Protocols (UDP / ICMP / TCP)
  • Application Programming Interface (API)
  • Simple Network Management Protocol (SNMP)

In the network, compromising the whole system is a major security threat. Further, it leads to affect the system by following activities,

  • May reveal the user personal data without their knowledge 
  • Deprivation of vital resources and information (For instance: research data)
  • Block the legal user network resources to stop their internal activities 
  • Intended to hurt the reputation of the organization utilizing economic liability 
  • Illegally perform mischievous operations to create harm to another system  

Our research team has long-term experience in developing novel Intrusion Detection System Thesis topics. So, we are familiar with all possible security vulnerabilities with their appropriate solutions. As a result, we support you to choose suitable problem-solving techniques for any kind of IDS research issue. Now, we can see the general use of the IDS and its role in making secure environs.

What are the uses of intrusion detection systems?

  • Highly scalable to adopt new changes in the system 
  • Easy to manage the whole system 
  • Detect the security or privacy risk in high accuracy
  • Simple to sense the forensic information and mitigate the attacks 
  • Support advance intelligent approaches to analyze the threat nature and impact

Now, we can see the reasons that make the intrusion detection system the evergreen thesis topic. Since many problems are not addressed and some are not solved effectively. Most importantly, the current poor security systems make the IDS grow rapidly among other areas. Our research team has developed numerous result-guaranteed solutions for current security attacks and threats. Still, now, we are looking for all possible ways to improve the techniques more efficiently despite challenges. And few of them are given below for your reference:

Research Issues in IDS 

           The NIDS system has the threat of illegal information and IP/TCP attacks. So, it makes the network hosts weak against the protocol attacks.

  • In general, IDS handles the data based on the network address i.e., it is uniquely identified by the address which has IP packets. But, there is a threat to inject fake IP packets into the network
  • Imperfect attack surface analysis
  • Many of the IDSs are not capable to work with encrypted packets. So, the attacker gets the chance of hacking the network through encrypted packets. So, it is essential to design the IDS that are capable to handle encrypted packets. 
  • Detection of false negative and positive value
  • In the case of signature-based IDS, there is no proper match for the new attacks in the database. So, IDS may not detect the threat due to technical flaws.
  • Interpretation of the huge-scale information 
  • In cloud computing, there are no separate security measures for personal use and business users. All the users are equally treated without any priority to meet the privacy requirements. 
  • The presence of weak authentication may let the attackers hold the system access easily. Once the attackers get into the system, then IDS is not capable to stop the worst impact of data abuse.
  • Operation on newly arrived attack 
  • Occurrence of unexpected noises such as corrupted DNS, poor packets, packets with greater false-alarm. These noises will interrupt the performance of the IDS.  
  • Chance of missing real-world attacks while dealing with several false-alarms
  • Must include the facility to update the software routinely to make the software eligible to manage new attacks. For that, it requires robust strategies and signatures to lessen the threats. 

IDS Methods and Algorithms 

So far, we have debated on the IDS current research challenges. Now, we can see the various IDS techniques and algorithms that are used for accurate detection and prevention of intrusion in the network. Majorly, these techniques fall under any of the following classifications.  

  • Machine learning (ML) based – Works based on the auto-learning mechanisms that are used to detect and classify the various attacks.
  • Statistics-based – Works based on the statistical information of the intrusions. It detects the abnormal patterns from the normal patterns in the network.
  • Rule-based – Works based on the complete context information of the attacks to create the pre-defined rule system for known attacks detection. 

Now, we can see the important security threat, vulnerabilities, and attacks in wireless communication. These are common threats that we have to concentrate on all sorts of web-based applications and services.

Security Attacks and Threats 

  • Password – Attempt to steal or guess the weaker password for illegal data access
    • For instances: Brute force (SSH/FTP-Patator)
  • Denial of Service (DoS) – Block the normal network channel by sending continuous user requests for a specific period
    • For Instance: SN flood, smurf, botnet, slow loris, etc.
  • Injection – Utilizes the scripts to shoot up the commands for achieving unlicensed system access
    • For instance: Cross-site scripts (XSS) and SQL injection
  • Distributed Denial of Service (DDoS) – This does not allow the users to access their requested resource by creating heavy traffic in the network
    • For Instances: RUDY, LAND, teardrop, and more
  • User to Root Attack (U2R) – Pretend to be authenticated user and attempt to grab the root access rights through system weakness
    • For instance: Perl, rootkit, buffer overflow, load, and many more
  • Probe – Hunt for the network gaps to get the system data access through IP address 
    • For instance: Ip/port sweep and satan
  • Remote to Local Attack (R2L) – Similar to U2R, but it tries earning the local access rights through remote nodes in the network and creating harmful things to the system
    • For instance: imap, waremaster, brute force (SSH), spy, multiple-hop, and others

Now, we can see the few significant metrics that are used for evaluating the dataset’s consistency. Since the dataset is one of the important factors to classify the intrusion accurately. Here, our developers have highlighted parameters used in recent IDS research.

How to Select the Dataset for IDS?

  • Network Traffic
  • Varied Attacks 
  • Data/Packet Transmission
  • Digital Anonymity
  • Configuration of Network
  • Standard Protocols
  • Precise Dataset Labeling
  • Standard Features (for selection)
  • Metadata (include whole info on data gathering)
  • Full Packet Capture (include data of payloads)
  • Mixed Networks (include data of host log and traffic)

Here, our native research team has given you the tips to create a flawless intrusion detection system thesis for your PhD study. Further, we are also ready to give you more information on other aspects of the thesis writing. 

How to make Good Thesis writing?

  • Overall design / Research flow verification The proposed system design should meet the requirements of the handpicked Intrusion Detection System Thesis topic. These solutions are needed to valuable to solve the research problem. Also, it must make the readers classify the pre-defined and experimental outcome. 
  • Analysis of Data Sources 
  • In a time of data analysis, proper statistical validation needs to be performed on the research dataset. Also, in the final stage, the comparative study is essential to examine the obtained result with previous works. If any comparisons are applied then appropriately define the data using tables and figures
  • Criteria Measurement
  • Assure that selected criteria will verify and measure the data dependability utilizing both dependent and independent variables 

Further, our research team has given you the happening research areas in the introduction detection system for your awareness. Beyond this, we are also ready to support you in interesting research areas.

Top Research Areas in Intrusion Detection System Thesis

  • Intelligence Forensics on Digital Information  
  • New Developments of Information Security in Cloud Computing
  • Incident Response System in Cyber Intelligence
  • Network Data Security for Next-Generation FireWall (NGFW) 
  • Intrusion Detection System in Wireless Sensor Communication
  • Efficient Data Processing in Heterogeneous Cloud Systems 
  • Dynamic and Secure Cloud Data Distribution 
  • Computing based Fault Tolerance in Embedded Models
  • Intelligent Data Sensing and Aggregation in Security Paradigms
  • Real-time Data Offloading and IDS in Cloud System 
  • Live Multimedia Streaming Methods in Cloud Computing 
  • Assurance of Security in cloud-based Networking Systems
  • Real-time Secure Multimedia Applications in Cloud Computing 
  • Optimized Embedded System in Real-time WSN 
  • Avoidance of Cyber Threat using Intelligent Approaches 
  • Defensive Measures of Intrusion in Mobile Cloud Applications
  • Innovative Security Mechanism for Big Data Database 
  • Optimized Cloud Applications for Intelligent Information Mining 
  • Privacy-Preserving Techniques in Green-Cloud Models and Services
  • Implementation of Network Security Mechanisms in Telemedicine Solutions
  • Enhancement of QoS and QoE in Diverse Application
  • Insider Threat Detection using efficient social engineering techniques 
  • New Technologies and Platforms for Cybersecurity Applications
  • Prevention of Cyber hacking using IDS based Cybersecurity System
  • Optimized MapReduce and Visualization Approaches 

Thesis Ideas in Intrusion Detection System

  • The implication of Active and Passive IDS
  • Emerging Techniques in Signature / Knowledge-based IDS
  • Novel Approaches for Host Intrusion detection systems (HIDS)
  • Improving Security by State-driven Stack Based IDS
  • Traffic Monitoring using Network Intrusion detection systems (NIDS) 
  • User Behavior-based Intrusion Detection and Prevention System

Future Directions of Intrusion Detection System 

  • Proactive Blocking:
    • It is one of the proficient methods to stop hateful traffic before harming the target node. For that, it analyzes the network to detect abnormal patterns by matching them with database signatures. Also, it detects malicious actions on time even the system associated with other external devices.  
  • Smart IDS
    • When we use machine learning algorithms in IDS, it yields the best outcome in handling multi-source data and complex scenarios. Similarly, deep learning algorithms have the special feature of self-learning capability to learn the data dynamically which is used to classify the data with high accuracy. These two mechanisms expected to rule the IDS in the future but now it is still under the research
  • Real Datasets
    • Effective implementation of security mechanisms (like IDS methods) in the real-time dataset. It should support the latest research areas which depend on testbeds, simulated models, and more. 
  • Real-time IDS:
    • In the real world, the smart grid-based IDS are executed on the MOA platform. It is effective to operate streaming information for dynamic detection of abnormal traffic. Further, it supports advanced metering infrastructure (AMI) interaction. Another vial approach with the future scope is IEC–61850 networks. 
  • IDS in SDN
    • This approach is based on both detecting and blocking intrusion. Here, the Software Defined Networking monitors the flow of packets in the network. When the malicious traffic is detected, it forwards the packets to the IDS for an immediate measure of blocking those networks.

Further, if you need more detail regarding our service, make a contact with us for intrusion detection system thesis topics. We are sure to assist you in each step of the study from area identification to thesis submission with expert guidance.

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