def __init__(self, density): """Initialize the simulation.""" #Initialize the container container = LJContainer(density) #Equilibriate the system while self.t < EQUILIBRIATION_STEPS: sys.stdout.write("\rEquilibriating the container: {0:3.1f}%".format( self.t*100/EQUILIBRIATION_STEPS)) sys.stdout.flush() #Do one 'tick' consisting of two integrations and a force update container.tick(rescale=True) #Increment time self.t += 1 print("\n") self.t = 0.0 #Start measuring while self.t < MEASUREMENT_STEPS: sys.stdout.write("\rCalculating averages: {0:3.1f}%".format( self.t*100/MEASUREMENT_STEPS)) sys.stdout.flush() #Do one 'tick' consisting of two integrations and a force update container.tick(rescale=True) #Sample averages container.sample(self.t) #Increment time self.t += 1 #Store sampling data util.write_data(container.data, density) #Generate a plot of the measured properties util.generate_report(density) #Print out the average value for the pressure pressure = util.calculate_average(container.data, "P") #Write calculated pressure to disk util.store_pressure(pressure, density) print("\nAverage pressure for density {0}: {1:6.4}".format( density, pressure))
def initial_exploit(self, debug=None): print(f"=== Current state is {self.state} ===") print("Are there untested high-risk issues?") vuldata = None for index, value in enumerate(self.data["issues"]): if value["used"] == 0 and value["severity"] == "high" and ((debug == None) or (value["cwe"] == debug)): vuldata = value vuldata["cookie_string"] = self.data["cookie_string"] self.data["issues"][index]["used"] = 1 j = json.dumps(self.data, indent=4) with open(f'./Data/{self.target}.json', 'w') as w: w.write(j) break if vuldata: report_issue = None self.path_count+=1 print("-> Yes!") print(f"Start to attack {self.target} through issue {vuldata['name']}...") if vuldata["cwe"] == 78: report_issue = Execution.cmd_injection(vuldata) elif vuldata["cwe"] == 79: report_issue = Execution.xss(vuldata) elif vuldata["cwe"] == 89: report_issue = Execution.sql_injection(vuldata) elif vuldata["cwe"] == 22 or vuldata["cwe"] == 98: report_issue = Execution.LFI_Path(vuldata) elif vuldata["cwe"] == 94: report_issue = Execution.RFI(vuldata) elif vuldata["cwe"] == 352: report_issue = Execution.CSRF(vuldata, self.report) if report_issue: if report_issue["next_state"] == "initialization": util.generate_report(self.path_count, self.report, report_issue) self.exploit_back() self.start() elif report_issue["next_state"] == "basic": self.get_shell_success() if report_issue["cwe"] == 78: path = Execution.pe_cmd(report_issue, lhost=self.lhost) elif report_issue["cwe"] == 94: path = Execution.pe_rfi(report_issue, lhost=self.lhost) util.generate_report(self.path_count, self.report, path) if path["Privilege escalation"]["next_state"] == "root": self.pe_success() self.back() elif report_issue["next_state"] == "sql": self.sql_success() util.generate_report(self.path_count, self.report, report_issue) self.back() elif report_issue["next_state"] == "root": self.pe_success() util.generate_report(self.path_count, self.report, report_issue) self.back() else: print("[!] This framework does not include the exploitation of this vulnerability.") self.exploit_back() self.start() else: print(f"-> No!") print(f"[*] ============ Pentest Finished ============") totaltime = int(time.time())-self.start_time print(f"[*] Total time : {time.strftime('%H:%M:%S', time.gmtime(totaltime))}") print(f"[*] Pentest report : ./Report/{self.report}")
] for index, driver in enumerate(drivers): find_task_page(driver, PLATFORM, TASK_NAME, TRAINING_MODE) time.sleep(8) if DATA_SPLIT == 'iid': for i in range(len(DIGIT_CLASS_PATHS)): driver.find_element_by_id('hidden-input_mnist_' + str(i)).send_keys(' \n '.join( digit_files[i])) else: for i in range(len(DIGIT_CLASS_PATHS)): driver.find_element_by_id('hidden-input_mnist_' + str(i)).send_keys(' \n '.join( digit_partitions[i][index])) # Start training on each driver time.sleep(8) start_training(drivers, TRAINING_TYPE, TIME_OFFSETS) time.sleep(5) generate_report('report.txt', \ drivers, \ start_time, \ '//*[@id="app"]/div/div/div/div/div/div/div/main/div/div/div[3]/div/div[1]/div/div[2]/p/span[1]', \ '//*[@id="app"]/div/div/div/div/div/div/div/main/div/div/div[3]/div/div[1]/div/div[1]/p/span[1]', \ 10) for driver in drivers: driver.quit()
history = ae.fit(x=x_train, y=x_train, shuffle=True, epochs=epochs, batch_size=batch_size, validation_data=(x_test, x_test)) end_time = datetime.datetime.today() print(f"Completed {epochs} in {end_time-start_time}.") # generate report here r = { 'start_time': start_time, 'end_time': end_time, 'history': history.history, 'batch_size': batch_size, 'epochs': epochs, 'learning_rate': learning_rate, 'latent_dim': latent_dim, 'n_filters': n_filters, 'input_shape': input_shape, 'rate': rate, 'n_samples': n_samples, 'encoder': e, 'decoder': d, 'autoencoder': ae } util.generate_report(r)
"training history": training_history, "start time": s, "end time": e, "elapsed time": elp, "batch size": batch_size, "epochs": epochs, "input shape": input_shape, "learning rate": lr, "spect type": spect_type, "spect size": spect_size, "standard": standard, "model": saved_model, "train": train, "folds": n_folds } final_loss = generate_report(report_dir, report_data) pickle.dump(training_history, open(os.path.join(report_dir, "training_history.pkl"), "wb"), protocol=2) # email report with open(os.path.join(report_dir, "report_summary.txt"), 'r') as report_fp: report_details = report_fp.read() alert = email_alert() alert.send( subject= "MixCNN Train Cycle {0}-{1:0>2}-{2:0>2} {3:0>2}:{4:0>2} [{5:0.4f}]" .format(s.year, s.month, s.day, s.hour, s.minute, final_loss), message=report_details)