Intrusion Detection by Port Scan Using Snort

Network intrusion detection systems (NIDS) are an important part of any network security architecture. They provide a layer of defense which monitors network traffic for predefined suspicious activity or patterns, and alert system administrators when potential hostile traffic is detected. Network Intrusion Detection Systems (NIDS) perform deep packet inspection on packet payloads to identify, prevent, and inhibit malicious attacks over the Internet[l]. Snort is a lightweight intrusion detection system that can log packets coming across your network. This program can be used on smaller networks but on larger ones, with Gigabit Ethernet, snort can become unreliable. Snort doesn't require that you recompile your kernel or add any software or hardware to your existing distribution but it does require that you have root privileges

IMPLEMENTASI NETWORK INTRUSION DETECTION SYSTEMS (NIDS) SERVER PADA SISTEM SMART IDENTIFICATION

Smart Identification merupakan sistem yang mengintegrasikan server dengan perangkat mobile yang digunakan mahasiswa dengan memanfaatkan teknologi wireless untuk melakukan absensi. Smart identification akan mengidentifikasi Mac address yang terdapat pada perangkat mobile yang digunakan mahasiswa, maka akan lebih efisien untuk sistem absensi yang hanya cukup terhubung dengan access point. Dengan terhubungnya setiap perangkat pada suatu jaringan dibutuhkan suatu keamanan jaringan pada sistem smart identification ini. Masalah yang sering terjadi pada keamanan jaringan dikarenakan sering terjadi Port-Scanning, Malware dan Denial of Services (DoS/DDoS). Untuk mengatasi masalah keamanan jaringan pada suatu jaringan perlu adanya pengawasan dalam jaringan. Network Intrusion Detection System (NIDS) merupakan perangkat lunak yang bekerja secara otomatis untuk memonitor suatu kejadian serta paket data yang masuk pada jaringan. Dengan adanya NIDS server pada sistem smart identification, NIDS ini dapat mendeteksi adanya serangan dan mengirimkan notifikasi berupa sms atau melalui web interface. Kata Kunci: Smart Identification, Mac address, Keamanan Jaringan, Network Intrusion Detection Syste

Short Paper: Strengths And Weaknesses of Deep, Convolutional and Recurrent Neural Networks in Network Intrusion Detection Deployments

The escalating significance of cybersecurity, due to IoT’s growth, demands robust security. As cyberattacks increase, machine learning-based network intrusion detection systems (NIDS) provide an effective countermeasure. This paper conducts experiments to optimize an NIDS pipeline using three artificial neural network (ANN) paradigms, demonstrating the importance of optimization and addressing computational time misconceptions. It assesses realistic datasets and compares performance metrics and execution times. Our main contribution is evaluat- ing data processing pipelines for ANN application in NIDS, and benchmarking processing ap- proaches’ influence on advanced neural-network methods

Detection of nework attacks using graph neural networks

The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS). Recent works proposed the use of Machine Learning (ML) techniques for building such systems (e.g., decision trees, neural networks). However, existing ML-based NIDS do not generalize well to other network scenarios and they are barely robust to common adversarial attacks. This TFM will explore the potential of using graph representations of network flows together with Graph Neural Networks for building more robust NIDS that can better generalize to other networks

Detection of nework attacks using graph neural networks

The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS). Recent works proposed the use of Machine Learning (ML) techniques for building such systems (e.g., decision trees, neural networks). However, existing ML-based NIDS do not generalize well to other network scenarios and they are barely robust to common adversarial attacks. This TFM will explore the potential of using graph representations of network flows together with Graph Neural Networks for building more robust NIDS that can better generalize to other networks

Parallelizing a network intrusion detection system using a GPU.

As network speeds continue to increase and attacks get increasingly more complicated, there is need to improved detection algorithms and improved performance of Network Intrusion Detection Systems (NIDS). Recently, several attempts have been made to use the underutilized parallel processing capabilities of GPUs, to offload the costly NIDS pattern matching algorithms. This thesis presents an interface for NIDS Snort that allows porting of the pattern-matching algorithm to run on a GPU. The analysis show that this system can achieve up to four times speedup over the existing Snort implementation and that GPUs can be effectively utilized to perform intensive computational processes like pattern matching