Sharif Digital Repository

Sophisticated and targeted malwares, which today are known as Advanced Persistent Threats (APTs), use multi-step, distributed, hybrid and low-level patterns to leak and exfiltrate information, manipulate data, or prevent progression of a program or mission. Since current intrusion detection systems (IDSs) and alert correlation systems do not correlate low-level operating system events with network events and use alert correlation instead of event correlation, the intruders use low and hybrid events in order to distribute the attack vector, hide malwares behaviors, and therefore make detection difficult for such detection systems. In this paper, a new approach for detecting hybrid and... 

Targeted cyber attacks, which today are known as Advanced Persistent Threats (APTs), use low and slow patterns to bypass intrusion detection and alert correlation systems. Since most of the attack detection approaches use a short time-window, the slow APTs abuse this weakness to escape from the detection systems. In these situations, the intruders increase the time of attacks and move as slowly as possible by some tricks such as using sleeper and wake up functions and make detection difficult for such detection systems. In addition, low APTs use trusted subjects or agents to conceal any footprint and abnormalities in the victim system by some tricks such as code injection and stealing... 

Service Identification is one of the most important phases in serviceoriented development methodologies. Although several service identification methods tried to identify services automatically or semi-automatically, various aspects of business domain are not taken into account simultaneously. To overcome this issue, three strategies from three different aspects of business domain are combined for semi-automated identification of services in this article. At first, the tasks interconnections within the business processes are considered. Then, based on the common supporting requirements, another tasks dependency has been determined and finally, regarding the significant impact of data in... 

Static malware detection approaches are time-consuming and cannot deal with code obfuscation techniques. Dynamic malware detection approaches, on the other hand, address these two challenges, however, suffer from behavioral ambiguity, such as the system calls obfuscation. In this paper, we introduce Markhor, a dynamic and behavior-based malware detection approach. Markhor uses system call data dependency and system call control dependency sequences to create a weighted list of malicious patterns. The list is then used to determine the malicious processes. Next, the similarity of a file system call sequences to a malicious pattern is extracted based on a fuzzy algorithm and the file nature is... 

Static malware detection approaches are time-consuming and cannot deal with code obfuscation techniques. Dynamic malware detection approaches, on the other hand, address these two challenges, however, suffer from behavioral ambiguity, such as the system calls obfuscation. In this paper, we introduce Markhor, a dynamic and behavior-based malware detection approach. Markhor uses system call data dependency and system call control dependency sequences to create a weighted list of malicious patterns. The list is then used to determine the malicious processes. Next, the similarity of a file system call sequences to a malicious pattern is extracted based on a fuzzy algorithm and the file nature is... 

Despite the numerous researches in Stress Corrosion Cracking (SCC) risk of austenitic stainless steels in aqueous chloride solution, no formulation or reliable method for prediction of time to failure as a result of SCC has yet been defined. In this paper, the capability of artificial neural network for estimation of the time to failure for SCC of 304 stainless steel in aqueous chloride solution together with sensitivity analysis has been expressed. The output results showed that artificial neural network can predict the time to failure for about 74% of the variance of SCC experimental data. Furthermore, the sensitivity analysis also demonstrated the effects of input parameters (Temperature,... 

Energy use in buildings contributes a large part in global energy demand. To reduce energy use in this group of consumers, specially in cold seasons, an automatic control technique is proposed. In this paper, a model predictive controller (MPC) is employed to minimize the boiler activation time. The method uses the building model and incorporates the weather forecast data to act on the actuator in an optimal fashion while treating the user comfort constraints. This technique, as a part, can be embedded into the building energy management system. The building model parameters are obtained via an online identification process using unscented kalman filter (UKF). This identification is...