An Integrated Software Environment For Powertrain Feasibility Assessment Using Optimization And Optimal Control

With the increase in automotive powertrain complexity, an upfront assessment of powertrain capability in meeting its design targets is important early on in the development programs. The optimization of control policy based on powertrain simulation models can facilitate this assessment and establish limits of achievable performance for a given powertrain configuration and parameters. The paper discusses several computational optimization and user interface solutions for deploying a numerical optimal control approach in a user-friendly software environment.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74690/1/j.1934-6093.2006.tb00271.x.pd

Systematic literature review for malware visualization techniques

Analyzing the activities or the behaviors of malicious scripts highly depends on extracted features. It is also significant to know which features are more effective for certain visualization types. Similarly, selecting an appropriate visualization technique plays a key role for analytical descriptive, diagnostic, predictive and prescriptive. Thus, the visualization technique should provide understandable information about the malicious code activities. This paper followed systematic literature review method in order to review the extracted features that are used to identify the malware, different types of visualization techniques and guidelines to select the right visualization techniques. An advanced search has been performed in most relevant digital libraries to obtain potentially relevant articles. The results demonstrate significant resources and types of features that are important to analyze malware activities and common visualization techniques that are currently used and methods to choose the right visualization technique in order to analyze the security events effectively

Analysis of Feature Categories for Malware Visualization

It is important to know which features are more effective for certain visualization types. Furthermore, selecting an appropriate visualization tool plays a key role in descriptive, diagnostic, predictive and prescriptive analytics. Moreover, analyzing the activities of malicious scripts or codes is dependent on the extracted features. In this paper, the authors focused on reviewing and classifying the most common extracted features that have been used for malware visualization based on specified categories. This study examines the features categories and its usefulness for effective malware visualization. Additionally, it focuses on the common extracted features that have been used in the malware visualization domain. Therefore, the conducted literature review finding revealed that the features could be categorized into four main categories, namely, static, dynamic, hybrid, and application metadata. The contribution of this research paper is about feature selection for illustrating which features are effective with which visualization tools for malware visualization

Formulation Of Association Rule Mining (ARM) For An Effective Cyber Attack Attribution In Cyber Threat Intelligence (CTI)

In recent year, an adversary has improved their Tactic, Technique and Procedure (TTPs) in launching cyberattack that make it less predictable, more persistent, resourceful and better funded. So many organisation has opted to use Cyber Threat Intelligence (CTI) in their security posture in attributing cyberattack effectively. However, to fully leverage the massive amount of data in CTI for threat attribution, an organisation needs to spend their focus more on discovering the hidden knowledge behind the voluminous data to produce an effective cyberattack attribution. Hence this paper emphasized on the research of association analysis in CTI process for cyber attack attribution. The aim of this paper is to formulate association ruleset to perform the attribution process in the CTI. The Apriori algorithm is used to formulate association ruleset in association analysis process and is known as the CTI Association Ruleset (CTI-AR). Interestingness measure indicator specially support (s), confidence (c) and lift (l) are used to measure the practicality, validity and filtering the CTI-AR. The results showed that CTI-AR effectively identify the attributes, relationship between attributes and attribution level group of cyberattack in CTI. This research has a high potential of being expanded into cyber threat hunting process in providing a more proactive cybersecurity environment

RENTAKA: A novel machine learning framework for crypto-ransomware pre-encryption detection

Crypto ransomware is malware that locks its victim’s file for ransom using an encryption algorithm. Its popularity has risen at an alarming rate among the cyber community due to several successful worldwide attacks. The encryption employed had caused irreversible damage to the victim’s digital files, even when the victim chose to pay the ransom. As a result, cybercriminals have found ransomware a lucrative and profitable cyber-extortion approach. The increasing computing power, memory, cryptography, and digital currency advancement have caused ransomware attacks. It spreads through phishing emails, encrypting sensitive data, and causing harm to the designated client. Most research in ransomware detection focuses on detecting during the encryption and post-attack phase. However, the damage done by crypto-ransomware is almost impossible to reverse, and there is a need for an early detection mechanism. For early detection of crypto-ransomware, behavior-based detection techniques are the most effective. This work describes RENTAKA, a framework based on machine learning for the early detection of crypto-ransomware.The features extracted are based on the phases of the ransomware lifecycle. This experiment included five widely used machine learning classifiers: Naïve Bayes, kNN, Support Vector Machines, Random Forest, and J48. This study proposed a pre-encryption detection framework for crypto-ransomware using a machine learning approach. Based on our experiments, support vector machines (SVM) performed with the best accuracy and TPR, 97.05% and 0.995, respectively

Automotive Powertrain Control — A Survey

This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72023/1/j.1934-6093.2006.tb00275.x.pd

Digital Forensics Institute in Malaysia: the way forward

Aswami Ariffin, Jill Slay and Husin Jazri set out the digital forensics landscape in Malaysia, analyze the problems encountered, consider its achievements to date, and proposes the formation of a Digital Forensics Institute Index words: digital forensics; digital forensics research; development of digital forensics in Malaysi

Digital forensics in Malaysia

Conference paper on Digital forensics in Malaysia by Aswami Fadillah Mohd Ariffin and Izwan Iskandar Ishak. Aswami Fadillah Mohd Ariffin is the Head of Digital Forensic, CyberSecurity Malaysia and Izwan Iskandar Ishak is a Senior Executive, Strategic Policy & Legal Research of CyberSecurity (Malaysia)

Data Recovery from Proprietary Formatted Cctv Hard Disks

Part 4: FILESYSTEM FORENSICSInternational audienceDigital video recorders (DVRs) for closed-circuit television (CCTV) commonly have an in-built capability to export stored video files to optical storage media. In the event that a DVR is damaged, its contents cannot be easily exported. This renders the forensically-sound recovery of proprietary-formatted video files with their timestamps from a DVR hard disk an expensive and challenging exercise. This paper presents and validates a technique that enables digital forensic practitioners to carve video files with timestamps without referring to the DVR hard disk filesystem

IOS anti-forensics: how can we securely conceal, delete and insert data?

Abstract: With increasing popularity of smart mobile devices such as iOS devices, security and privacy concerns have emerged as a salient area of inquiry. A relatively under-studied area is anti-mobile forensics to prevent or inhibit forensic investigations. In this paper, we propose a "Concealment" technique to enhance the security of non-protected (Class D) data that is at rest on iOS devices, as well as a "Deletion" technique to reinforce data deletion from iOS devices. We also demonstrate how our "Insertion" technique can be used to insert data into iOS devices surreptitiously that would be hard to pick up in a forensic investigation.Authors: Christian D’Orazio, Aswami Ariffin, Kim-Kwang Raymond Choo, University of South AustraliaThis is the authors’’ pre-print version of the paper, and citation should be:•    D’’Orazio C, Ariffin A and Choo K-K R 2014. iOS anti-forensics: How can we securely conceal, delete and insert data?. In 47th Annual Hawaii International Conference on System Sciences (HICSS 2014), 6––9 January 2014, IEEE Computer Society Press [In Press