Pengoptimuman algoritma pengesanan batu loncatan dalam sistem pengesanan pencerobohan

Detection of a network intrusion and manual response without any further action does create a problem known as time gap. Time gap is duration between detection and response. Previous researches have used some approaches like intelligent agent and IDS adoption to solve time gap problem. However, they do not consider the aspect of intrusion response mechanism. The purpose of this study is to optimize the stepping stone algorithm, which is part of intrusion response mechanism. In this research, special Top-Down methodology has been used to optimize the stepping stone algorithm. It is achieved by analyzing five stepping stone algorithms, in which each algorithm is divided into three main parts. The parts are packet capture, identification and comparison. Among these algorithms, the best approach which produces minimum processing time from each main part has been deployed and tested as a complete stepping stone algorithm. The results from both the optimized approach and existing algorithm are compared. From this comparison, the optimized algorithm gives the best result. The finding of this research suggests that time gap can be reduced through the optimization of the stepping stone algorithm

Solving time gap problems through the optimization of detecting stepping stone algorithm

This paper describes an analysis of detecting stepping stone algorithm to defeat the time gap problem. It is found that current algorithm of detecting stepping stone is not optimized. Several weaknesses are identified and suggestions are proposed to overcome this problem. The suggestions are applied in the improved algorithm. Since the detecting stepping stone is listed as one of the response technique, it is suggested that the improved algorithm should be used as a remedial to the time gap problem

Intelligent Network-Based Stepping Stone Detection Approach.

This research intends to introduce a new usage of Artificial Intelligent (AI) approaches in Stepping Stone Detection (SSD) fields of research

Approach for solving active perturbation attack problem in stepping stone detection.

Batu loncatan merupakan salah satu daripada teknik menyembunyikan jejak yang digunakan oleh penceroboh untuk menyembunyikan jejaknya. Untuk lebih daripada satu dekad, para penyelidik menumpukan usaha mereka untuk mempertingkatkan pendekatan Pengesanan Batu Loncatan (PBL) untuk mengidentifikasi secara tepatnya hos yang dipergunakan untuk melakukan serangan batu loncatan. Tambahan pula, Serangan Penembusan Aktif (SPA) seperti lengah, jatuhan paket dan chaf mengancam pendekatan PBL. Hari ini, di antara pelbagai jenis SPA, chaf, lengah dan jatuhan paket adalah sangat penting. Stepping stone is one of the hidden tracking techniques used by an intruder to hide its tracks. For more than a decade, researchers have focused themselves in enhancing the Stepping Stone Detection (SSD) approaches in order to identify accurately a compromised host using stepping stones to attack. In addition, Active Perturbation Attacks (APA) such as delays, dropped packets and chaffs threaten the SSD approaches. Today, among the types of APAs, chaffs, delays and packet drops are very significant

Isu dan masalah sink hole terhadap penyelenggaraan jalan raya di Johor Bahru

Penyelenggaraan jalan adalah satu proses kerja-kerja yang terlibat bagi tujuan mengekalkan keadaan jalan seperti keadaan asalnya dari segi ciri-ciri geometri dan juga kekuatan strukurnya (Salleh, 2011). Selain itu, penyelenggaraan jalan merupakan kerja-kerja menjaga dan memperelokkan jalan dan bahagian-bahagian jalan yang telah siap dibina. Penyelenggaraan jalan juga melibatkan lain- lain struktur binaan yang terdapat pada jalan raya dan jalan yang mengalami kadar kerosakan yang bermula setelah siap dibina dan mula digunakan. Kerja-kerja penyelenggaraan dijalankan bagi mengawal kerosakan, menjamin jalan yang dibina boleh mencapai jangka hayat yang lama, menjaga jalan raya untuk kegunaan trafik dan memperbaiki perjalanan sistem trafik (JKR, 2009)

Stepping-stone detection technique for recognizing legitimate and attack connections

A stepping-stone connection has always been assumed as an intrusion since the first research on stepping-stone connections twenty years ago. However, not all stepping-stone connections are malicious.This paper proposes an enhanced stepping-stone detection (SSD) technique which is capable to identify legitimate connections from stepping-stone connections.Stepping-stone connections are identified from raw network traffics using timing-based SSD approach.Then, they go through an anomaly detection technique to differentiate between legitimate and attack connections.This technique has a promising solution to accurately detecting intrusions from stepping-stone connections.It will prevent incorrect responses that punish legitimate users

Enhancing the security of RCIA ultra-lightweight authentication protocol by using Random Number Generator (RNG) technique

This study is an attempt to enhance the security of Robust Confidentiality, Integrity, and Authentication (RCIA) ultra-lightweight authentication protocols.In the RCIA protocol, IDs value is sent between reader and tag as a constant value.This makes RCIA susceptible to traceability attack which lead to the privacy issue. In order to overcome this problem, Random Number Generator (RNG) technique based on Bitwise operations has been used in the tag side.The idea of this technique is to change the IDs of a tag on every query session so that it will not stay as a constant value.The implementation of Enhanced RCIA has been conducted by using a simulation.The simulation provided the ability to show that the operations of RCIA protocol as to compare with the enhanced RCIA.The outcome shows that the enhanced RCIA outperforms existing one in terms of privacy

Detecting backdoor using stepping stone detection approach

Several techniques are used by intruders to hide the track of intrusion in the network.One of these techniques executes by using series of hosts in network (stepping stones chain), which can be detected by using an approach, called Stepping Stone Detection (SSD). However, during all previous years, SSD was only confined to detect this type of intrusion.This paper discusses the using of SSD approach and potential applications in other emerging field by introduce the using of SSD concepts in backdoor attack detection field.This research shows that by using SSD to detect backdoor attack can be gained very low false negative and false positive rates and reduces the scan process time detection

A Survey on Mental Health Detection in Online Social Network

Mental health detection in Online Social Network (OSN) is widely studied in the recent years. OSN has encouraged new ways to communicate and share information, and it is used regularly by millions of people. It generates a mass amount of information that can be utilised to develop mental health detection. The rich content provided by OSN should not be overlooked as it could give more value to the data explored by the researcher. The main purpose of this study is to extract and scrutinise related works from related literature on detection of mental health using OSN. With the focus on the method used, machine learning algorithm, sources of OSN, and types of language used for the mental health detection were chosen for the study. The basic design of this study is in the form of a survey from the literature related to current research in mental health. Major findings revealed that the most frequently used method in mental health detection is machine learning techniques, with Support Vector Machine (SVM) as the most chosen algorithm. Meanwhile, Twitter is the major data source from OSN with English language used for mental health detection. The researcher found a few challenges from the previous studies and analyses, and these include limitations in language barrier, account privacy in OSN, single type of OSN, text analysis, and limited features selection. Based on the limitations, the researcher outlined a future direction of mental health detection using language based on user’s geo-location and mother tongue. The use of pictorial, audio and video formats in OSN could become one of the potential areas to be explored in future research. Extracting data from multiple sources of OSNs with new features selection will probably improve mental health detection in the future. In conclusion, this research has a big potential to be explored further in the future