a framework for automated similarity analysis of malware

Malware, a category of software including viruses, worms, and other malicious programs, is developed by hackers to damage, disrupt, or perform other harmful actions on data, computer systems and networks. Malware analysis, as an indispensable part of the work of IT security specialists, aims to gain an in-depth understanding of malware code. Manual analysis of malware is a very costly and time-consuming process. As more malware variants are evolved by hackers who occasionally use a copy-paste-modify programming style to accelerate the generation of large number of malware, the effort spent in analyzing similar pieces of malicious code has dramatically grown. One approach to remedy this situation is to automatically perform similarity analysis on malware samples and identify the functions they share in order to minimize duplicated effort in analyzing similar codes of malware variants. In this thesis, we present a framework to match cloned functions in a large chunk of malware samples. Firstly, the instructions of the functions to be analyzed are extracted from the disassembled malware binary code and then normalized. We propose a new similarity metric and use it to determine the pair-wise similarity among malware samples based on the calculated similarity of their functions. The developed tool also includes an API class recognizer designed to determine probable malicious operations that can be performed by malware functions. Furthermore, it allows us to visualize the relationship among functions inside malware codes and locate similar functions importing the same API class. We evaluate this framework on three malware datasets including metamorphic viruses created by malware generation tools, real-life malware variants in the wild, and two well-known botnet trojans. The obtained experimental results confirm that the proposed framework is effective in detecting similar malware code

Voriconazole exposure and risk of cutaneous squamous cell carcinoma among lung or hematopoietic cell transplant patients: A systematic review and meta-analysis

Background Current evidence about the association between voriconazole and risk of cutaneous squamous cell carcinoma (SCC) remains inconsistent. Objective To assess the association between voriconazole use and risk of SCC. Methods We systematically searched PubMed and Embase and performed a random effects model meta-analysis to calculate the pooled relative risk (RR) with a 95% confidence interval (CI). Results Of the 8 studies involving a total of 3710 individuals with a lung transplant or hematopoietic cell transplant that were included in the qualitative analysis, 5 were included in the meta-analysis. Use of voriconazole was significantly associated with increased risk of SCC (RR, 1.86; 95% CI, 1.36-2.55). The increased risk did not differ according to type of transplantation or adjustment for sun exposure. Longer duration of voriconazole use was found to be positively associated with risk of SCC (RR, 1.72; 95% CI, 1.09-2.72). Voriconazole use was not associated with increased risk of basal cell carcinoma (RR, 0.84; 95% CI, 0.41-1.71). Limitations There were some heterogeneities in the retrospective observational studies. Conclusions Our findings support an increased risk of SCC associated with voriconazole in individuals with a lung transplant or hematopoietic cell transplant. Routine dermatologic surveillance should be performed, especially among individuals at high risk of developing SCC

SGLT2 inhibitors and risk of cancer in type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials

Aims/hypothesis The association between sodium–glucose cotransporter 2 (SGLT2) inhibitors and the risk of cancer in individuals with type 2 diabetes remains uncertain. This study aimed to evaluate the risk of cancer associated with SGLT2 inhibitor treatment of type 2 diabetes. Methods We systematically searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials and ClinicalTrials.gov from inception to 15 February 2017 to identify eligible randomised controlled trials (RCTs) that report cancer events in individuals with type 2 diabetes treated with SGLT2 inhibitors for at least 24 weeks. We performed pairwise and network meta-analyses as well as a cumulative meta-analysis to calculate ORs and 95% CIs. Results In total, 580 incidences of cancer among 34,569 individuals were identified from 46 independent RCTs with a mean trial duration of 61 weeks. When compared with comparators (placebo or other active glucose-lowering treatments), SGLT2 inhibitors were not significantly associated with an increased risk of overall cancer (OR 1.14 [95% CI 0.96, 1.36]). For pre-specified cancer types, the risk of bladder cancer might be increased with SGLT2 inhibitors (OR 3.87 [95% CI 1.48, 10.08]), especially empagliflozin (OR 4.49 [95% CI 1.21, 16.73]). Interestingly, canagliflozin might be protective against gastrointestinal cancers (OR 0.15 [95% CI 0.04, 0.60]). Conclusions/interpretation Current evidence from short-term RCTs did not indicate a significantly increased risk of overall cancer among individuals with type 2 diabetes using SGLT2 inhibitors. Given the short-term trial durations and uncertainty of evidence, future long-term prospective studies and post-marketing surveillance studies are warranted

Pioglitazone and bladder cancer risk: a systematic review and meta-analysis

Current evidence about the association between pioglitazone and bladder cancer risk remains conflict. We aimed to assess the risk of bladder cancer associated with the use of pioglitazone and identify modifiers that affect the results. We systematically searched PubMed, Embase, and Cochrane Central Register of Controlled Trials from inception to 25 August 2016 for randomized controlled trials (RCTs) and observational studies that evaluated the association between pioglitazone and bladder cancer risk. Conventional and cumulative meta-analyses were used to calculate the odds ratio (OR) with 95% confidence interval (CI). A restricted spline regression analysis was used to examine the dose-response relationship with a generalized least-squares trend test. We included two RCTs involving 9114 patients and 20 observational studies (n = 4,846,088 individuals). An increased risk of bladder cancer in patients treated with pioglitazone versus placebo was noted from RCTs (OR, 1.84; 95%CI, 0.99 to 3.42). In observational studies, the increased risk of bladder cancer was slight but significant among ever-users of pioglitazone versus never-users (OR, 1.13; 95%CI, 1.03 to 1.25), which appeared to be both time- (P = 0.003) and dose-dependent (P = 0.05). In addition, we observed the association differed by region of studies (Europe, United States, or Asia) or source of funding (sponsored by industry or not). Current evidence suggests that pioglitazone may increase the risk of bladder cancer, possibly in a dose- and time-dependent manner. Patients with long-term and high-dose exposure to pioglitazone should be monitored regularly for signs of bladder cancer

Engineering pressure retarded osmosis membrane bioreactor (PRO-MBR) for simultaneous water and energy recovery from municipal wastewater

Osmotic membrane bioreactors (OMBR) have gained increasing interest in wastewater treatment and reclamation due to their high product water quality and fouling resistance. However, high energy consumption (mostly by draw solution recovery) restricted the wider application of OMBR. Herein, we propose a novel pressure retarded osmosis membrane bioreactor (PRO-MBR) for improving the economic feasibility. In comparison with conventional FO-MBR, PRO-MBR exhibited similar excellent contaminants removal performance and comparable water flux. More importantly, a considerable amount of energy can be recovered by PRO-MBR (4.1 kWh/100 m2·d), as a result of which, 10.02% of the specific energy consumption (SEC) for water recovery was reduced as compared with FO-MBR (from 1.42 kWh/m3 to 1.28 kWh/m3). Membrane orientation largely determined the performance of PRO-MBR, higher power density was achieved in AL-DS orientation (peak value of 3.4 W/m2) than that in AL-FS orientation (peak value of 1.4 W/m2). However, PRO-MBR suffered more severe and complex membrane fouling when operated in AL-DS orientation, because the porous support layer was facing sludge mixed liquor. Further investigation revealed fouling was mostly reversible for PRO-MBR, it exhibited similar flux recoverability (92.4%) to that in FO-MBR (95.1%) after osmotic backwash. Nevertheless, flux decline due to membrane fouling is still a restricting factor to power generation of PRO-MBR, its power density was decreased by 38.2% in the first 60 min due to the formation of fouling. Overall, in perspective of technoeconomic feasibility, the PRO-MBR demonstrates better potential than FO-MBR in wastewater treatment and reclamation and deserves more research attention in the future.This work was supported by the National Natural Science Foundation of China [grant number 51978312]; the Six Major Talent Peaks of Jiangsu Province [grant number 2018-JNHB-014]; and the Program to Cultivate Middle-aged and Young Science Leaders of Colleges and Universities of Jiangsu Province

New ionic dinuclear Ir(III) Schiff base complexes with aggregation-induced phosphorescent emission (AIPE)

Two new ionic dinuclear Ir(III) Schiff base complexes which are straightforward to synthesise have luminescence quantum yields as high as 37% in neat films. These are the first examples of dinuclear ionic Ir(III) complexes that display aggregation-induced phosphorescent emission (AIPE)

Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection

Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently

Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection

Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles' future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently