4 research outputs found
Performance comparison of intrusion detection systems and application of machine learning to Snort system
This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort
A Survey on Biometrics and Cancelable Biometrics Systems
Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results
Visual Clutter Study for Pedestrian Using Large Scale Naturalistic Driving Data
Some of the pedestrian crashes are due to driver’s late or difficult perception of pedestrian’s appearance. Recognition of pedestrians during driving is a complex cognitive activity. Visual clutter analysis can be used to study the factors that affect human visual search efficiency and help design advanced driver assistant system for better decision making and user experience. In this thesis, we propose the pedestrian perception evaluation model which can quantitatively analyze the pedestrian perception difficulty using naturalistic driving data. An efficient detection framework was developed to locate pedestrians within large scale naturalistic driving data. Visual clutter analysis was used to study the factors that may affect the driver’s ability to perceive pedestrian appearance. The candidate factors were explored by the designed exploratory study using naturalistic driving data and a bottom-up image-based pedestrian clutter metric was proposed to quantify the pedestrian perception difficulty in naturalistic driving data. Based on the proposed bottom-up clutter metrics and top-down pedestrian appearance based estimator, a Bayesian probabilistic pedestrian perception evaluation model was further constructed to simulate the pedestrian perception process
Development of secured algorithm to enhance the privacy and security template of biometric technology
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy in Mathematical and Computer Science and Engineering
of the Nelson Mandela African Institution of Science and TechnologyThe security of information and personal privacy are the growing concerns in today’s human
life worldwide. The storage of biometric data in the database has raised the prospect of
compromising the database leading to grave risks and misuse of the person’s privacy such as
growth in terrorism and identity fraud. When a person’s biometric data stored is revealed,
their security and privacy are being compromised. This research described a detailed
evaluation on several outbreaks and threats associated with the biometric technology. It
analyzed the user’s fear and intimidations to the biometric technology alongside the
protection steps for securing the biometric data template in the database. It is known that,
when somebody’s biometric data template is compromised from the database that
consequently might indicate proof of identity robbery of that person. Mixed method to
compute and articulate the results as well as a new tactic of encryption-decryption algorithm
with a design pattern of Model View Template (MVT) are used for securing the biometric
data template in the database. The model managed information logically, the view indicated
the visualization of the data, and the template directed the data migration into pattern object.
Factors influencing fear of biometric technology such as an exposer of personal information,
improper data transfer, and data misuse are found. Strong knowledge of the ideal technology
like the private skills of the biometric technology, data secrecy and perceived helpfulness are
established. The fears and attacks along the technology like a counterfeit of documents and
brute-force attack are known. The designed algorithm based on the cryptographic module of
the Fernet keys instance are utilized. The Fernet keys are combined to generate a multiFernet
key, integrated with biometric data to produce two encrypted files (byte and text file). These
files are incorporated with Twilio message and firmly stored in the database. The storage
database has security measures that guard against an impostor’s attack. The database system
can block the attacker from unauthorized access. Thus, significantly increased individual data
privacy and integrity